# HG changeset patch # User ecology # Date 1727442064 0 # Node ID ad96b20423cf02cfe4f706ad08a2f01ae61605f0 planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/EMLassemblyline commit 4b040fe7867d965fb88ce70cc08081367b62b063 diff -r 000000000000 -r ad96b20423cf annotations_template.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/annotations_template.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,81 @@ +#17/07/2024 +#Seguineau Pauline + +#Make annotation template +#Load packages + +library(EMLassemblyline) + +#Load arguments + +args = commandArgs(trailingOnly=TRUE) + +if(length(args)>0){ + prod <- args[1] + if (prod == "fr_scratch"){ + select <- args[2] + data_table <- args[3] + if(select == "data_table"){ + data_table <- args[3] + }else if(select == "other_entities"){ + other_entities <- args[3] + }else if(select == "both"){ + data_table <- args[3] + other_entities <- args[4]} + }else{ + eml <- args[2] + } +} + +#Transform arguments + +if(prod =="fr_scratch" && select == "both"){ + table = strsplit(data_table," ") + other = strsplit(other_entities," ") + +#Make templates to add annotations from scratch + + template_annotations( + path ="data_files/", + data.table = table[[1]], + other.entity = other[[1]]) + +}else if (prod == "fr_scratch" && select == "data_table"){ + + table = strsplit(data_table," ") + template_annotations( + path ="data_files/", + data.table = table[[1]]) + +}else if (prod == "fr_scratch" && select == "other_entities"){ + + other = strsplit(other_entities," ") + template_annotations( + path ="data_files/", + other.entity = other[[1]]) + +#Make templates to add annotations from EML file + +}else{ + template_annotations( + path= "data_files/", + eml = eml)} + + + + + + + + + + + + + + + + + + + diff -r 000000000000 -r ad96b20423cf eal_table_templates.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/eal_table_templates.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,21 @@ +#28/11/2023 #SEGUINEAU Pauline + +#Load packages + +library(EMLassemblyline) + +#Load arguments + +if (length(commandArgs(trailingOnly = TRUE)) > 0) { + data_table <- commandArgs(trailingOnly = TRUE)[1] +} + +#Transform arguments + +table = strsplit(data_table," ") + +#Make templates to describe data tables : Describes columns of a data table (classes, units, datetime formats, missing value codes) + catégorical variables. + +template_table_attributes(path = ".",data.path= "data_files", data.table = table[[1]]) +template_categorical_variables(path = ".", data.path = "data_files") + diff -r 000000000000 -r ad96b20423cf eal_templates.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/eal_templates.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,23 @@ +#22/11/2023 #SEGUINEAU Pauline + +###First tool of EML Workflow + +#Load packages + +library(EMLassemblyline) + +#Load arguments + +args = commandArgs(trailingOnly=TRUE) +if(length(args)>0){ + + license <- args[1] + file_type <- args[2] +} + +#Make templates to describe core features of a data package (abstract, methods, keywords, personnel, license). + +template_core_metadata(path=".",license = license, file.type = file_type) + + + diff -r 000000000000 -r ad96b20423cf eml2eal.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/eml2eal.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,15 @@ +##07/06/2023 +##Genthon Tanguy +###eml2eal + +#load arguments +args = commandArgs(trailingOnly=TRUE) +if (length(args)==0) +{ + stop("This tool needs at least one argument") +}else{ + data <- args[1] +} + + +EMLassemblyline::eml2eal(data,path=".") diff -r 000000000000 -r ad96b20423cf eml_validate.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/eml_validate.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,35 @@ +#17/09/2024 +#Seguineau Pauline + +#EML validate + +#Load packages + +library(emld) +library(EML) + +#Load arguments + +args = commandArgs(trailingOnly=TRUE) + +if(length(args)>0){ + eml_file <- args[1] +}else{ + +stop("This tool needs at least one argument.")} + +#run eml_validate + +eml = read_eml(eml_file) + +eml_valid = eml_validate(eml) + +if(eml_valid==T){ + cat("Your EML is valid.") +}else if(eml_valid==F && length(attr(eml_valid,""))>1){ + cat("Your EML is not valid. You can improve it by correcting these errors:","\n") + attr(eml_valid,"") +}else{ + cat("Your EML is not valid. You can improve it by correcting this error:","\n") + attr(eml_valid,"") +} diff -r 000000000000 -r ad96b20423cf eml_validate.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/eml_validate.xml Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,46 @@ + + check if your EML file is valid + + r-base + r-eml + r-emld + + report.txt + '$inputmetadatafile' + '$output' + ]]> + + + + + + + + + + + + + + + 10.32614/CRAN.package.emld + 10.32614/CRAN.package.EML + + diff -r 000000000000 -r ad96b20423cf entities_templates.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/entities_templates.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,17 @@ +#19/12/2023 #SEGUINEAU Pauline + +#Load arguments + +if (length(commandArgs(trailingOnly = TRUE)) > 0) { + data_objects <- commandArgs(trailingOnly = TRUE)[1] +} + +#Transform arguments + +data = strsplit(data_objects," ")[[1]] + +#Make entities templates for raster and/or vector and/or other entities data + +EMLassemblyline::template_entities(path="output_template",data.path="data_files",data.objects=data) + + diff -r 000000000000 -r ad96b20423cf geo_cov_temp.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/geo_cov_temp.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,38 @@ +#27/11/2023 +#Seguineau Pauline +#Make geographic coverage template + +#Load packages + +library(EMLassemblyline) + +#Load arguments + +args = commandArgs(trailingOnly=TRUE) +if(length(args)>0){ + + data_table <- args[1] + tablename <- args[2] + lat_col <- as.numeric(args[3]) + long_col <- as.numeric(args[4]) + site_col <- as.numeric(args[5]) + empty <- args[6] +} + +datatable = read.table(data_table,sep="\t",header=T) + +latcol = names(datatable[lat_col]) +longcol = names(datatable[long_col]) +sitecol = names(datatable[site_col]) + +if (empty == "false"){ + empty = FALSE +}else if (empty=="true"){ + empty=TRUE} + + +#Make template + +template_geographic_coverage(path =".", data.path = "data_files", data.table = tablename, lat.col = latcol, lon.col = longcol, site.col = sitecol, empty = empty) + + diff -r 000000000000 -r ad96b20423cf make_eml.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/make_eml.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,442 @@ +##07/06/2023 ##Genthon Tanguy +##update 15/11/2023 ## Seguineau Pauline + +###make_eml + +args = commandArgs(trailingOnly=TRUE) +if(length(args)>0){ + title <- args[1] + start <- args[2] + end <-args[3] + data_table <- args[4] + data_raster <- args[5] + data_vector <- args[6] + data_other <- args[7] + destable <- args[8] + desraster<- args[9] + desvector<- args[10] + desother <- args[11] + quote <- args[12] + table_url <- args[13] + other_url <- args[14] + raster_url <- args[15] + vector_url <- args[16] +} + +#Load package + +library(EMLassemblyline) +sessionInfo() + + +###Format data### +if (data_table == ""){ + table=NULL +}else{ + table = strsplit(data_table," ") + for (file in table){ + name_table = gsub("\\.[a-zA-Z]*", "", file)} + } + + +if (quote != ""){ + quote = strsplit(quote,",") + if (length(quote[[1]]) != length(table[[1]])){ + stop("Your number of quote(s) isn't equal to your number of data table file(s). Please enter the quote parameter as many time as the number of data tables you've input")} +} + + +tablequote=NULL +for (quote_table in quote[[1]]){ + if (quote_table=="quote"){ + quote_table = sub("quote",'"', quote_table)} + else if (quote_table=="apostrophe"){ + quote_table = gsub("apostrophe","'",quote_table)} + else if (quote_table=="none"){ + quote_table = gsub("none","",quote_table)} + tablequote = c(tablequote, quote_table) +} + + +if (data_raster == ""){ + raster=NULL +}else{ + raster = strsplit(data_raster," ") + for (file in raster){ + name_raster = gsub("\\.[a-zA-Z]*", "", file)} + } + +if (data_vector == ""){ + vector=NULL +}else{ + vector = strsplit(data_vector," ") + for (file in vector){ + name_vector = gsub("\\.[a-zA-Z]*", "", file)} + } + +if (data_other == ""){ + other=NULL +}else{ + other = strsplit(data_other," ") + for (file in other){ + name_other = gsub("\\.[a-zA-Z]*", "", file)} + } + +if (data_table !=""){ + if (destable == ""){ + des_table = name_table + }else{ + des_table = strsplit(destable,",")} +} + +if (data_raster !=""){ + if (desraster == ""){ + des_raster = name_raster + }else{ + des_raster = strsplit(desraster,",")} +} + +if (data_vector !=""){ + if (desvector == ""){ + des_vector = name_vector + }else{ + des_vector = strsplit(desvector,",")} +} + +if (data_other !=""){ + if (desother == ""){ + des_other = name_other + }else{ + des_other = strsplit(desother,",")} +} + +if (data_table !=""){ + if (table_url == ""){ + urltable = "" + }else{ + table_url = gsub("\\-" ,"", table_url) + urltable = strsplit(table_url,",") + } +} + +if (data_other !=""){ + if (other_url == ""){ + urlother = "" + }else{ + other_url = gsub("\\-" ,"", other_url) + urlother = strsplit(other_url,",") + } +} + +if (data_raster !=""){ + if (raster_url == ""){ + urlraster = "" + }else{ + raster_url = gsub("\\-" ,"", raster_url) + urlraster = strsplit(raster_url,",") + } +} + +if (data_vector !=""){ + if (vector_url == ""){ + urlvector = "" + }else{ + vector_url = gsub("\\-" ,"", vector_url) + urlvector = strsplit(vector_url,",") + } +} + + + +###Make EML### + +if (!is.null(table) && !is.null(raster) && !is.null(vector) && !is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + data.table=table[[1]], + data.table.name = name_table, + data.table.description = des_table[[1]], + data.table.quote.character = tablequote, + data.table.url = urltable[[1]], + spatial.raster=raster[[1]], + spatial.raster.name = name_raster, + spatial.raster.description = des_raster[[1]], + spatial.raster.url = urlraster[[1]], + spatial.vector=vector[[1]], + spatial.vector.name = name_vector, + spatial.vector.description = des_vector[[1]], + spatial.vector.url = urlvector[[1]], + other.entity=other[[1]], + other.entity.name = name_other, + other.entity.description = des_other[[1]], + other.entity.url= urlother[[1]]) + +}else if (is.null(table) && is.null(raster) && is.null(vector) && is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end)) + +}else if (!is.null(table) && is.null(raster) && is.null(vector) && is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + data.table=table[[1]], + data.table.name = name_table, + data.table.description = des_table[[1]], + data.table.quote.character = tablequote, + data.table.url = urltable[[1]] + ) + +}else if (!is.null(table) && !is.null(raster) && is.null(vector) && is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + data.table=table[[1]], + data.table.name = name_table, + data.table.description = des_table[[1]], + data.table.quote.character = tablequote, + data.table.url = urltable[[1]], + spatial.raster=raster[[1]], + spatial.raster.name = name_raster, + spatial.raster.description = des_raster[[1]], + spatial.raster.url = urlraster[[1]]) + +}else if (!is.null(table) && !is.null(raster) && !is.null(vector) && is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + data.table=table[[1]], + data.table.name = name_table, + data.table.description = des_table[[1]], + data.table.quote.character = tablequote, + data.table.url = urltable[[1]], + spatial.raster=raster[[1]], + spatial.raster.name = name_raster, + spatial.raster.description = des_raster[[1]], + spatial.raster.url = urlraster[[1]], + spatial.vector=vector[[1]], + spatial.vector.name = name_vector, + spatial.vector.description = des_vector[[1]], + spatial.vector.url = urlvector[[1]]) + +}else if (is.null(table) && !is.null(raster) && is.null(vector) && is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + spatial.raster=raster[[1]], + spatial.raster.name = name_raster, + spatial.raster.description = des_raster[[1]], + spatial.raster.url = urlraster[[1]]) + +}else if (is.null(table) && !is.null(raster) && !is.null(vector) && is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + spatial.raster=raster[[1]], + spatial.raster.name = name_raster, + spatial.raster.description = des_raster[[1]], + spatial.raster.url = urlraster[[1]], + spatial.vector=vector[[1]], + spatial.vector.name = name_vector, + spatial.vector.description = des_vector[[1]], + spatial.vector.url = urlvector[[1]]) + +}else if (is.null(table) && !is.null(raster) && !is.null(vector) && !is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + spatial.raster=raster[[1]], + spatial.raster.name = name_raster, + spatial.raster.description = des_raster[[1]], + spatial.raster.url = urlraster[[1]], + spatial.vector=vector[[1]], + spatial.vector.name = name_vector, + spatial.vector.description = des_vector[[1]], + spatial.vector.url = urlvector[[1]], + other.entity=other[[1]], + other.entity.name = name_other, + other.entity.description = des_other[[1]], + other.entity.url= urlother[[1]]) + +}else if (is.null(table) && is.null(raster) && !is.null(vector) && is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + spatial.vector=vector[[1]], + spatial.vector.name = name_vector, + spatial.vector.description = des_vector[[1]], + spatial.vector.url = urlvector[[1]]) + +}else if (is.null(table) && is.null(raster) && !is.null(vector) && !is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + spatial.vector=vector[[1]], + spatial.vector.name = name_vector, + spatial.vector.description = des_vector[[1]], + spatial.vector.url = urlvector[[1]], + other.entity=other[[1]], + other.entity.name = name_other, + other.entity.description = des_other[[1]], + other.entity.url= urlother[[1]]) + +}else if (is.null(table) && is.null(raster) && is.null(vector) && !is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + other.entity=other[[1]], + other.entity.name = name_other, + other.entity.description = des_other[[1]], + other.entity.url= urlother[[1]]) + +}else if (!is.null(table) && is.null(raster) && !is.null(vector) && is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + data.table=table[[1]], + data.table.name = name_table, + data.table.description = des_table[[1]], + data.table.quote.character = tablequote, + data.table.url = urltable[[1]], + spatial.vector=vector[[1]], + spatial.vector.name = name_vector, + spatial.vector.description = des_vector[[1]], + spatial.vector.url = urlvector[[1]]) + +}else if (!is.null(table) && is.null(raster) && is.null(vector) && !is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + data.table=table[[1]], + data.table.name = name_table, + data.table.description = des_table[[1]], + data.table.quote.character = tablequote, + data.table.url = urltable[[1]], + other.entity=other[[1]], + other.entity.name = name_other, + other.entity.description = des_other[[1]], + other.entity.url= urlother[[1]]) + +}else if (is.null(table) && !is.null(raster) && is.null(vector) && !is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + spatial.raster=raster[[1]], + spatial.raster.name = name_raster, + spatial.raster.description = des_raster[[1]], + spatial.raster.url = urlraster[[1]], + other.entity=other[[1]], + other.entity.name = name_other, + other.entity.description = des_other[[1]], + other.entity.url= urlother[[1]]) + +}else if (!is.null(table) && is.null(raster) && !is.null(vector) && !is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + data.table=table[[1]], + data.table.name = name_table, + data.table.description = des_table[[1]], + data.table.quote.character = tablequote, + data.table.url = urltable[[1]], + spatial.vector=vector[[1]], + spatial.vector.name = name_vector, + spatial.vector.description = des_vector[[1]], + spatial.vector.url = urlvector[[1]], + other.entity=other[[1]], + other.entity.name = name_other, + other.entity.description = des_other[[1]], + other.entity.url= urlother[[1]]) + +}else if (!is.null(table) && !is.null(raster) && is.null(vector) && !is.null(other)){ + + EMLassemblyline::make_eml( + path="output_template", + data.path="data_files", + eml.path=".", + dataset.title = title, + temporal.coverage = c(start,end), + data.table=table[[1]], + data.table.name = name_table, + data.table.description = des_table[[1]], + data.table.quote.character = tablequote, + data.table.url = urltable[[1]], + spatial.raster=raster[[1]], + spatial.raster.name = name_raster, + spatial.raster.description = des_raster[[1]], + spatial.raster.url = urlraster[[1]], + other.entity=other[[1]], + other.entity.name = name_other, + other.entity.description = des_other[[1]], + other.entity.url= urlother[[1]])} + +issues() +old.names <- list.files(path=".", pattern=".xml") +file.rename(from=old.names, to="eml.xml") + + + + + + diff -r 000000000000 -r ad96b20423cf raster_templates.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/raster_templates.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,22 @@ +#15/12/2023 #SEGUINEAU Pauline + +#Load packages + +library(EMLassemblyline) + +#Load arguments + +if (length(commandArgs(trailingOnly = TRUE)) > 0) { + data_raster <- commandArgs(trailingOnly = TRUE)[1] +} + +#Transform arguments + +raster = strsplit(data_raster," ") + +#Make templates to describe data raster + +template_raster_attributes(path = ".",data.path= "data_files", raster.file = raster[[1]]) +template_categorical_variables(path = ".", data.path = "data_files") + + diff -r 000000000000 -r ad96b20423cf taxo_cov_template.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/taxo_cov_template.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,47 @@ +#28/11/2023 +#Seguineau Pauline +#Make taxonomic coverage template + +#Load packages + +library(EMLassemblyline) + +#Load arguments + +args = commandArgs(trailingOnly=TRUE) +if(length(args)>0){ + data_taxa <- args[1] + taxa_table <- args[2] + taxa_col <- as.numeric(args[3]) + taxa_name_type <- args[4] + authority <- as.numeric(args[5]) + authority2 <- as.numeric(args[6]) + authority3 <- as.numeric(args[7]) + empty <- args[8] +} + +#transfom arguments +taxatable = read.table(data_taxa,header=T,sep="\t") +taxacol = names(taxatable[taxa_col]) + +if (authority2 == 0 && authority3==0){ + authority_f = authority} + +if(authority2 == 0 && authority3 != 0){ + authority_f = c(authority,authority3)} + +if (authority2 !=0 && authority3==0){ + authority_f = c(authority,authority2)} + +if (authority3 !=0 && authority2 !=0){ + authority_f = c(authority,authority2,authority3)} + +if (empty == "false"){ + empty = FALSE +}else if (empty=="true"){ + empty=TRUE} + + +#Make template + +template_taxonomic_coverage(path =".", data.path = "data_files", taxa.table = taxa_table, taxa.col = taxacol, taxa.name.type = taxa_name_type , taxa.authority = authority_f, empty = empty) diff -r 000000000000 -r ad96b20423cf test-data/Assessing_the_importance_of_field_margins_for_bat_species.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Assessing_the_importance_of_field_margins_for_bat_species.xml Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,2074 @@ + + + Assessing the importance of field margins for bat species and communities in intensive agricultural landscapes - Data + c + + Constance + + Blary + + CEFE + constance.blary@cefe.cnrs.fr + https://orcid.org/0000-0001-6204-9983 + + + + Kévin + + Barré + + CESCO MNHN + kevin.barre@mnhn.fr + https://orcid.org/0000-0001-5368-4053 + + + + Christian + + Kerbiriou + + CESCO + christian.kerbiriou@mnhn.fr + https://orcid.org/0000-0001-6080-4762 + + + + Isabelle + + Le Viol + + CESCO + isabelle.le-viol@mnhn.fr + + 2021-05-25 + + Landscape simplification and degradation through agricultural intensification is widely recognized as a main driver of biodiversity loss. In intensively used agricultural landscapes, patches of semi-natural habitats and particularly connections between them are of high importance for many taxa. Vegetated connections like hedgerows are especially important for foraging and commuting of mobile taxa such as bats. However, the interest of another treeless linear habitat – herbaceous field margins – remains unstudied for insectivorous bats. Field margins are nevertheless known as an important habitat for other taxa, including bat prey. Here we assessed the importance of field margins for bats compared to other landscape variables. We measured bat activity based on a repeated passive acoustic monitoring during 17 complete nights in summer on 112 study sites in an intensively used agricultural landscape. Each night, we sampled bat species activity and community metrics (i.e. species richness and community habitat specialization index) at different distances to field margins, and along a gradient of relative density of field margins. To compare field margin effects with other landscape variables, the sampled sites were selected by keeping a large variability in these other variables (land-cover Shannon diversity index, forests, hedgerows, water bodies, main roads, urban areas, grasslands, number of crops and rapeseed percentage). Only Myotis sp. were affected by herbaceous field margins. Specifically, the Myotis group activity decreased with the distance to herbaceous field margins (i.e. towards field crop cores), and positively correlated with relative density of herbaceous field margins, for which the effect size was comparable to other landscape variables. However, other landscape variables such as the proportion of and the distance to forests, the relative density of and the distance to hedgerows or land-cover Shannon diversity index, affected species richness, community specialization index, and bat activity of species from open, edge and narrow-space foragers, including the Myotis group as well. Our results highlight that herbaceous field margins have a positive effect on the activity of narrow-space bat foragers as Myotis species, but do not replace other landscape variables that drive the activity of the whole community. + + + + Acoustic monitoring + Bat community + Farmland biodiversity + Field borders + Habitat specialisation + Landscape composition + Keywords + + + This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data ("Data User" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is." The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you. + + + + + Yvelines - Essonne - Seine et Marne + + 1.60296 + 3.56409 + 49.08428 + 48.12266 + + + + + + 2015-07-08 + + + 2015-08-02 + + + + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_33949 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_4560 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_1560 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_1560 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://www.eionet.europa.eu/gemet/concept/4648 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://www.eionet.europa.eu/gemet/concept/2519 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://www.eionet.europa.eu/gemet/concept/420 + + + ongoing + + + + Constance + "" + Blary + + CEFE + constance.blary@cefe.cnrs.fr + https://orcid.org/0000-0001-6204-9983 + + + + Kévin + "" + Barré + + CESCO MNHN + kevin.barre@mnhn.fr + https://orcid.org/0000-0001-5368-4053 + + + + The aim of the study was to investigate the potential of field margins to support bat activity compared to other landscape variables in agricultural landscapes. As a consequence, we chose the location of sampling sites in a way that limited correlations between field margins and other landscape variables to avoid statistical issues (see Statistical analysis section), and to maximise the gradient of each. The weather was highly stable and favourable to bats throughout the sampling period. We recorded bat activity on 112 sampling sites along a gradient of distances from 0 to 447 m from field margins (median: 60 m, median absolute deviation: 57 m) and along a gradient of relative density of field margins ranging from 0 to 64 m/ha (computed in different sizes of buffers around sampling sites, from 250 m to 4,000 m, see Landscape variable and Statistical analysis sections for more details about buffer sizes). Each night, we sampled simultaneously three to 11 sites at different densities of and distances from field margins, in order to limit potential correlations with temporal variables (i.e. weather, moon). Each site was sampled one time and echolocation calls were recorded on sites separated by at least 230 m from each other to avoid simultaneous recordings (see acoustic sampling section). Recorders were associated with a single microphone throughout the study and were homogeneously distributed between distances to the field margin and relative densities of field margins, in order to limit potential correlations between recorders and field margins variables. + Sampling was carried out over 17 nights from the 8th of July to the 2nd of August 2015, during the seasonal peak in bat activity. Recordings were performed during the entire night, from 30 min before sunset to 30 min after sunrise. Standardized acoustic bat survey was carried out using Song Meter SM2Bat+ and omnidirectional microphones SMX-US (Wildlife Acoustics Inc., Concord, MA, USA) put horizontally at one meter above the ground on a pole and pointed perpendicularly to the field margin. Recording was triggered automatically by ultrasounds between eight and 192 kHz, using a trigger level set to 6 dB Signal Noise Ratio and set to continue recording until two seconds after last trigger event, and a 384 kHz sampling rate, as widely used in many previous studies (e.g. Azam et al., 2016; Millon et al., 2017; Barré et al., 2017, 2019). As it is currently impossible to distinguish individual bats from their echolocation calls, it is not possible to count bat abundance. Instead, we used the number of bat passes recorded during a night as a measure of bat activity (however this metric is correlated to the abundance, see Mimet et al., 2020). We defined a bat pass as one or more echolocation call within a 5-second interval, which is a commonly accepted standard in Europe (Stahlschmidt and Brühl; 2012, Millon et al.; 2015, Kerbiriou et al. 2018; 2019; Put et al., 2019). We used the software TADARIDA (Bas et al., 2017) to automatically detect sound events and assign bat passes (11,324 bat passes for a total of ~450,000 calls) to the most accurate taxonomic level associated to a confidence score calculated by the software (i.e. the probability that a bat pass has been assigned to the right species). Two species groups were firstly constructed in order to limit identification errors: (i) a Myotis group (including Myotis nattereri, Myotis myotis, Myotis mystacinus and Myotis daubentonii) and (ii) a Plecotus group (including Plecotus austriacus and Plecotus auritus). In addition to these species-groups, we considered four species: (i) P. pipistrellus that are edge space foragers, (ii) Nyctalus leisleri and (iii) Nyctalus noctula that are open space foragers, and (iv) Eptesicus serotinus, that are edge space foragers (Robinson and Stebbincs, 1996). Given that automated identification can generate high error rates at species level, we followed the Barré et al. (2019) approach proposing a cautious method to ensure results robustness against automated identification errors. The method allows to model the error rate for each species or group, according to confidence scores of automated identifications provided by the software TADARIDA (Bas et al., 2017). This method allows to perform statistical analyses based on two levels of maximum error rate tolerance: first on a 0.5 maximum error rate tolerance and then by confirming results on a safer threshold of 0.1 maximum error rate tolerance. Indeed, each threshold of error rate tolerated in data involve different caveats which potentially induce biases in acoustic data (i.e. false positives and false negatives). The 0.5 maximum error rate tolerance keeps high number of bat passes for analysis including false positives, while a more restrictive 0.1 threshold limits false positives, but at the cost of discarding more true positives, which thus induce a loss of statistical power. As a consequence, checking the consistence in results whatever the threshold considered ensures robust conclusions (see Barré et al. 2019 for more details). + + + + The study was conducted in France, in the Île-de-France region (Yvelines, Essonne and Seine-et-Marne departments) in an intensively used agricultural landscape. This region is covered by 59% agricultural areas, 22% forests and semi natural areas, 18% artificial surfaces and 1% water bodies (Corine Land Cover data from 2016; https://www.data.gouv.fr/en/datasets/corine-land-cover-occupation-des-sols-en-france). The agricultural areas were dominated by arable land (90%) for intensive cropping (73 % of rapeseed, 7% of wheat, 3% of barley, based on manual mapping during the experiment in a 100 m radius around sampling sites). Crops were harvested before the study between the end of June and the beginning of July. +We defined field margins as an herbaceous linear structure with only spontaneous vegetation, without fertilizers or pesticides immediately applied, without trees or bushes, 2 ± 1 m wide, and that were surrounding fields. + + Sampling was carried out over 17 nights from the 8th of July to the 2nd of August 2015, during the seasonal peak in bat activity. Recordings were performed during the entire night, from 30 min before sunset to 30 min after sunrise. Standardized acoustic bat survey was carried out using Song Meter SM2Bat+ and omnidirectional microphones SMX-US (Wildlife Acoustics Inc., Concord, MA, USA) put horizontally at one meter above the ground on a pole and pointed perpendicularly to the field margin. Recording was triggered automatically by ultrasounds between eight and 192 kHz, using a trigger level set to 6 dB Signal Noise Ratio and set to continue recording until two seconds after last trigger event, and a 384 kHz sampling rate, as widely used in many previous studies (e.g. Azam et al., 2016; Millon et al., 2017; Barré et al., 2017, 2019). +As it is currently impossible to distinguish individual bats from their echolocation calls, it is not possible to count bat abundance. Instead, we used the number of bat passes recorded during a night as a measure of bat activity (however this metric is correlated to the abundance, see Mimet et al., 2020). We defined a bat pass as one or more echolocation call within a 5-second interval, which is a commonly accepted standard in Europe (Stahlschmidt and Brühl; 2012, Millon et al.; 2015, Kerbiriou et al. 2018; 2019; Put et al., 2019). + + + + Effect of agricultural landscape features on bats + + + Kévin + "" + Barré + + CESCO MNHN + kevin.barre@mnhn.fr + https://orcid.org/0000-0001-5368-4053 + Principal Investigator + + DIM ASTREA Région Ile-de-France, FRB + + + data_blary_al.tsv + Content of data_blary_al.txt + + data_blary_al.txt + 79724 + 189e312d6c6635a7ca21aa1b6c15bda2 + + + 1 + \r\n + column + + + + + + + + + Id + Sampling site ID + string + + + + + sampling site ID + + + + + + "NA" + "No information" + + + + dist_field_margin + Distance to the nearest field margin element (in meters) + float + + + + meter + + + real + + 0.197097079 + 446.7105128 + + + + + + NA + No information + + + + dist_hedgerow + Distance to the nearest hedgerow element (in meters) + float + + + + meter + + + real + + 26.18609735 + 993.9548575 + + + + + + "NA" + "No information" + + + + dist_forest + Distance to the nearest forest element (in meters) + float + + + + meter + + + real + + 35.65219329 + 2117.555351 + + + + + + "NA" + "No information" + + + + dist_road + Distance to the nearest road element (in meters) + float + + + + meter + + + real + + 26.78687042 + 1906.832346 + + + + + + "NA" + "No information" + + + + dist_water + Distance to the nearest water body element (in meters) + float + + + + meter + + + real + + 293.5477229 + 7059.607301 + + + + + + "NA" + "No information" + + + + dist_urban + Distance to the nearest urban element (in meters) + float + + + + meter + + + real + + 117.752441 + 1660.510336 + + + + + + "NA" + "No information" + + + + density_field_margins_250m + Relative density of field margins in a 250 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0 + 63.98307757 + + + + + + "NA" + "No information" + + + + density_field_margins_500m + Relative density of filed margins in a 500 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 1.30602297 + 56.85356039 + + + + + + "NA" + "No information" + + + + density_field_margins_750m + Relative density of filed margins in a 750 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 3.76147295 + 51.98107287 + + + + + + "NA" + "No information" + + + + density_field_margins_1000m + Relative density of filed margins in a 1000 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 7.016125291 + 49.65501534 + + + + + + "NA" + "No information" + + + + density_field_margins_1500m + Relative density of filed margins in a 1500 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 13.19438883 + 46.14576092 + + + + + + "NA" + "No information" + + + + density_field_margins_2000m + Relative density of filed margins in a 2000 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 14.31645702 + 49.15383992 + + + + + + "NA" + "No information" + + + + density_field_margins_4000m + Relative density of filed margins in a 4000 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 12.0861939 + 49.04803019 + + + + + + "NA" + "No information" + + + + density_roads_250m + Relative density of roads in a 250 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0 + 41.24173899 + + + + + + "NA" + "No information" + + + + density_roads_500m + Relative density of roads in a 500 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0 + 28.67314656 + + + + + + "NA" + "No information" + + + + density_roads_750m + Relative density of roads in a 750 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0 + 21.52403654 + + + + + + "NA" + "No information" + + + + density_roads_1000m + Description for density_roads_1000m + float + + + + dimensionless + + + real + + 0 + 16.31772774 + + + + + + "NA" + "No information" + + + + density_roads_1500m + Relative density of roads in a 1500 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0 + 14.66196384 + + + + + + "NA" + "No information" + + + + density_roads_2000m + Relative density of roads in a 2000 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0.691124942 + 15.21731043 + + + + + + "NA" + "No information" + + + + density_roads_4000m + Relative density of roads in a 4000 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 4.415217013 + 12.96935098 + + + + + + "NA" + "No information" + + + + density_hedgerows_250m + Relative density of hedgerows in a 250 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0 + 29.04454202 + + + + + + "NA" + "No information" + + + + density_hedgerows_500m + Relative density of hedgerows in a 500 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0 + 30.69898208 + + + + + + "NA" + "No information" + + + + density_hedgerows_750m + Relative density of hedgerows in a 750 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0 + 27.45535012 + + + + + + "NA" + "No information" + + + + density_hedgerows_1000m + Relative density of hedgerows in a 1000 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0.018214632 + 20.45344062 + + + + + + "NA" + "No information" + + + + density_hedgerows_1500m + Relative density of hedgerows in a 1500 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0.28226013 + 15.24459232 + + + + + + "NA" + "No information" + + + + density_hedgerows_2000m + Relative density of hedgerows in a 2000 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 0.793527384 + 16.1536311 + + + + + + "NA" + "No information" + + + + density_hedgerows_4000m + Relative density of hedgerows in a 4000 meters buffer around the sampling site (in meters per hectare) + float + + + + dimensionless + + + real + + 1.067627189 + 12.42166846 + + + + + + "NA" + "No information" + + + + shannon_250m + Land-cover Shannon diversity index in 250 meters buffer around the sampling site (no unit of measurement) + float + + + + dimensionless + + + real + + 0 + 0.855530354 + + + + + + "NA" + "No information" + + + + shannon_500m + Land-cover Shannon diversity index in 500 meters buffer around the sampling site (no unit of measurement) + float + + + + dimensionless + + + real + + 0 + 0.977506236 + + + + + + "NA" + "No information" + + + + shannon_750m + Land-cover Shannon diversity index in 750 meters buffer around the sampling site (no unit of measurement) + float + + + + dimensionless + + + real + + 0.009166349 + 1.072124368 + + + + + + "NA" + "No information" + + + + shannon_1000m + Land-cover Shannon diversity index in 1000 meters buffer around the sampling site (no unit of measurement) + float + + + + dimensionless + + + real + + 0.025382592 + 1.123819367 + + + + + + "NA" + "No information" + + + + shannon_1500m + Land-cover Shannon diversity index in 1500 meters buffer around the sampling site (no unit of measurement) + float + + + + dimensionless + + + real + + 0.050243126 + 1.129956244 + + + + + + "NA" + "No information" + + + + shannon_2000m + Land-cover Shannon diversity index in 2000 meters buffer around the sampling site (no unit of measurement) + float + + + + dimensionless + + + real + + 0.066009112 + 1.190823677 + + + + + + "NA" + "No information" + + + + shannon_4000m + Land-cover Shannon diversity index in 4000 meters buffer around the sampling site (no unit of measurement) + float + + + + dimensionless + + + real + + 0.188127771 + 1.328685868 + + + + + + "NA" + "No information" + + + + grassland_250 + Percentage of land covered by grassland in different 250 m buffer sizes + float + + + + percent + + + real + + 0 + 46.5648855 + + + + + + "NA" + "No information" + + + + forest_250m + Percentage of land covered by forest in different 250 m buffer sizes + float + + + + percent + + + real + + 0 + 30.25980642 + + + + + + "NA" + "No information" + + + + urban_250m + Percentage of land covered by urban area in different 250 m buffer sizes + float + + + + percent + + + real + + 0 + 7.997962303 + + + + + + "NA" + "No information" + + + + grassland_500 + Percentage of land covered by grassland in different 500 m buffer sizes + float + + + + percent + + + real + + 0 + 35.31134598 + + + + + + "NA" + "No information" + + + + forest_500m + Percentage of land covered by forest in different 500 m buffer sizes + float + + + + percent + + + real + + 0 + 29.51362363 + + + + + + "NA" + "No information" + + + + urban_500m + Percentage of land covered by urban area in different 500 m buffer sizes + float + + + + percent + + + real + + 0 + 8.348180199 + + + + + + "NA" + "No information" + + + + grassland_750 + Percentage of land covered by grassland in different 750 m buffer sizes + float + + + + percent + + + real + + 0 + 23.93162393 + + + + + + "NA" + "No information" + + + + forest_750m + Percentage of land covered by forest in different 750 m buffer sizes + float + + + + percent + + + real + + 0 + 37.17890687 + + + + + + "NA" + "No information" + + + + urban_750m + Percentage of land covered by urban area in different 750 m buffer sizes + float + + + + percent + + + real + + 0 + 14.74017887 + + + + + + "NA" + "No information" + + + + grassland_1000 + Percentage of land covered by grassland in different 1000 m buffer sizes + float + + + + percent + + + real + + 0 + 20.70600968 + + + + + + "NA" + "No information" + + + + forest_1000m + Percentage of land covered by forest in different 1000 m buffer sizes + float + + + + percent + + + real + + 0 + 39.53725216 + + + + + + "NA" + "No information" + + + + urban_1000m + Percentage of land covered by urban area in different 1000 m buffer sizes + float + + + + percent + + + real + + 0 + 19.74593269 + + + + + + "NA" + "No information" + + + + grassland_1500 + Percentage of land covered by grassland in different 1500 m buffer sizes + float + + + + percent + + + real + + 0.026879439 + 13.52712356 + + + + + + "NA" + "No information" + + + + forest_1500m + Percentage of land covered by forest in different 1500 m buffer sizes + float + + + + percent + + + real + + 0.014150076 + 46.80246128 + + + + + + "NA" + "No information" + + + + urban_1500m + Percentage of land covered by urban area in different 1500 m buffer sizes + float + + + + percent + + + real + + 0.063679846 + 28.33451252 + + + + + + "NA" + "No information" + + + + grassland_2000 + Percentage of land covered by grassland in different 2000 m buffer sizes + float + + + + percent + + + real + + 0.082766305 + 9.971589326 + + + + + + "NA" + "No information" + + + + forest_2000m + Description for forest_2000m + float + + + + percent + + + real + + 0.008754128 + 49.82452231 + + + + + + "NA" + "No information" + + + + urban_2000m + Percentage of land covered by urban area in different 2000 m buffer sizes + float + + + + percent + + + real + + 0.421796535 + 25.34798768 + + + + + + "NA" + "No information" + + + + grassland_4000 + Percentage of land covered by grassland in different 4000 m buffer sizes + float + + + + percent + + + real + + 0.174480141 + 8.655696087 + + + + + + "NA" + "No information" + + + + forest_4000m + Percentage of land covered by forest in different 4000 m buffer sizes + float + + + + percent + + + real + + 0.37902 + 56.74757214 + + + + + + "NA" + "No information" + + + + urban_4000m + Percentage of land covered by urban area in different 4000 m buffer sizes + float + + + + percent + + + real + + 1.227729864 + 18.25776748 + + + + + + "NA" + "No information" + + + + perc_rapeseed + Percentage of rapeseed crops in a 100 m radius around each site at the sampling date + float + + + + percent + + + real + + 0 + 1 + + + + + + "NA" + "No information" + + + + nb_crops + Number of different crops in a 100 m radius around each site at the sampling date + float + + + + number + + + natural + + 1 + 4 + + + + + + "NA" + "No information" + + + + date + Description for date + date + + + YYYY-MM-DD + + + + + "NA" + "No information" + + + + X + Sampling site longitude + float + + + + dimensionless + + + natural + + 616338 + 742845 + + + + + + "NA" + "No information" + + + http://www.w3.org/1999/02/22-rdf-syntax-ns#type + http://rs.tdwg.org/dwc/terms/decimalLongitude + + + + Y + Sampling site latitude + float + + + + dimensionless + + + natural + + 6784925 + 6847092 + + + + + + "NA" + "No information" + + + http://www.w3.org/1999/02/22-rdf-syntax-ns#type + http://rs.tdwg.org/dwc/terms/decimalLatitude + + + + recorder + Recorder ID used for data collection on the sampling site + string + + + + + + 10250 + Value: 10250 for attribute: recorder + + + 11693 + Value: 11693 for attribute: recorder + + + 13289 + Value: 13289 for attribute: recorder + + + 13676 + Value: 13676 for attribute: recorder + + + 13733 + Value: 13733 for attribute: recorder + + + 13740 + Value: 13740 for attribute: recorder + + + 17232 + Value: 17232 for attribute: recorder + + + 17329 + Value: 17329 for attribute: recorder + + + 17573 + Value: 17573 for attribute: recorder + + + 17578 + Value: 17578 for attribute: recorder + + + 17598 + Value: 17598 for attribute: recorder + + + 17601 + Value: 17601 for attribute: recorder + + + 17607 + Value: 17607 for attribute: recorder + + + 17608 + Value: 17608 for attribute: recorder + + + 3760 + Value: 3760 for attribute: recorder + + + + + + + "NA" + "No information" + + + + min_t + Minimum temperature recorded on the sampling area during the sampling night (in celsius) + float + + + + celsius + + + real + + 8.1 + 20 + + + + + + "NA" + "No information" + + + + max_wind + Maximum wind speed recorded on the sampling area during the sampling night (in km/h) + float + + + + kilometerPerHour + + + whole + + 0 + 30 + + + + + + "NA" + "No information" + + + + CSI_ER05 + Community specialization index calculated from species level activity on the 0.5 maximum error rate tolerance threshold + float + + + + dimensionless + + + real + + 0.004968013 + 0.241732748 + + + + + + "NA" + "No information" + + + + CSI_ER01 + Community specialization index calculated from species level activity on the 0.1 maximum error rate tolerance threshold + float + + + + dimensionless + + + real + + 0.00199574 + 0.238529275 + + + + + + "NA" + "No information" + + + + richness_ER05 + Species richness calculated from species level activity on the 0.5 maximum error rate tolerance threshold + float + + + + dimensionless + + + whole + + 0 + 11 + + + + + + "NA" + "No information" + + + + richness_ER01 + Species richness calculated from species level activity on the 0.1 maximum error rate tolerance threshold + float + + + + dimensionless + + + whole + + 0 + 9 + + + + + + "NA" + "No information" + + + + Nyclei_ER05 + Number of bat passes of Nyclei specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 51 + + + + + + "NA" + "No information" + + + + Nyclei_ER01 + Number of bat passes of Nyclei specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 49 + + + + + + "NA" + "No information" + + + + Pippip_ER05 + Number of bat passes of Pippip specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 863 + + + + + + "NA" + "No information" + + + + Pippip_ER01 + Number of bat passes of Pippip specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 842 + + + + + + "NA" + "No information" + + + + Eptser_ER05 + Number of bat passes of Epster specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 65 + + + + + + "NA" + "No information" + + + + Eptser_ER01 + Number of bat passes of Epster specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 40 + + + + + + "NA" + "No information" + + + + Nycnoc_ER05 + Number of bat passes of Nycnoc specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 17 + + + + + + "NA" + "No information" + + + + Nycnoc_ER01 + Number of bat passes of Nycnoc specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 13 + + + + + + "NA" + "No information" + + + + Myo_ER05 + Number of bat passes of Myo specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 42 + + + + + + "NA" + "No information" + + + + Myo_ER01 + Number of bat passes of Myo specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 33 + + + + + + "NA" + "No information" + + + + Ple_ER05 + Number of bat passes of Ple specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 8 + + + + + + "NA" + "No information" + + + + Ple_ER01 + Number of bat passes of Ple specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold + float + + + + number + + + whole + + 0 + 5 + + + + + + "NA" + "No information" + + + + 112 + + + diff -r 000000000000 -r ad96b20423cf test-data/Present_Surface_pH.tif Binary file test-data/Present_Surface_pH.tif has changed diff -r 000000000000 -r ad96b20423cf test-data/annotations.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotations.tabular Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,10 @@ +id element context subject predicate_label predicate_uri object_label object_uri +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 biodiversity http://aims.fao.org/aos/agrovoc/c_33949 +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 mammals 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http://www.w3.org/1999/02/22-rdf-syntax-ns#type decimalLatitude http://rs.tdwg.org/dwc/terms/decimalLatitude diff -r 000000000000 -r ad96b20423cf test-data/annotations.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotations.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,10 @@ +id element context subject predicate_label predicate_uri object_label object_uri +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 biodiversity http://aims.fao.org/aos/agrovoc/c_33949 +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 mammals http://aims.fao.org/aos/agrovoc/c_4560 +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 chiroptera http://aims.fao.org/aos/agrovoc/c_1560 +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 bats http://aims.fao.org/aos/agrovoc/c_1560 +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 Landscape 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areas displaying a diversity of abiotic and physical parameters that have been shown to drive the distribution of marine organisms. The dataset presented here contributes to our understanding of these interactions, providing essential information for ongoing research and conservation efforts in this Antarctic ecosystem. This dataset presents a table containing abiotic and physical parameters specific to the Dumont D’Urville Sea and a Jupyter notebook to execute a R script in the ecoregionalization Galaxy workflow. The abiotic and physical parameters were first presented in the research article 'Predicting habitat preferences for Anthometrina adriani (Echinodermata) on the East Antarctic continental shelf' by Hemery et al. (2011). The jupyter notebook is dedicated to the pivot_wider function from the tidyr R package. 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http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Blary Constance "" /ResponsibleParty dataset Blary Constance "" member of https://schema.org/memberOf +/Barré Kévin "" /ResponsibleParty dataset Barré Kévin "" is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Barré Kévin "" /ResponsibleParty dataset Barré Kévin "" member of https://schema.org/memberOf diff -r 000000000000 -r ad96b20423cf test-data/annotemp/annotations_template_2.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/annotations_template_2.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,33 @@ +id element context subject predicate_label predicate_uri object_label object_uri +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 +/ceamarc_env.tsv /dataTable dataset ceamarc_env.tsv is about http://purl.obolibrary.org/obo/IAO_0000136 +/ceamarc_env.tsv/long /dataTable/attribute ceamarc_env.tsv long contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/lat /dataTable/attribute ceamarc_env.tsv lat contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Carbo /dataTable/attribute ceamarc_env.tsv Carbo contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Grav /dataTable/attribute ceamarc_env.tsv Grav contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Maxbearing /dataTable/attribute ceamarc_env.tsv Maxbearing contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Maxmagnit /dataTable/attribute ceamarc_env.tsv Maxmagnit contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Meancurmag /dataTable/attribute ceamarc_env.tsv Meancurmag contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Meansal /dataTable/attribute ceamarc_env.tsv Meansal contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Meantheta /dataTable/attribute ceamarc_env.tsv Meantheta contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Mud /dataTable/attribute ceamarc_env.tsv Mud contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Prof /dataTable/attribute ceamarc_env.tsv Prof contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Rugosity /dataTable/attribute ceamarc_env.tsv Rugosity contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Sand /dataTable/attribute ceamarc_env.tsv Sand contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Seaice_prod /dataTable/attribute ceamarc_env.tsv Seaice_prod contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Sili /dataTable/attribute ceamarc_env.tsv Sili contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Slope /dataTable/attribute ceamarc_env.tsv Slope contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Standcurmag /dataTable/attribute ceamarc_env.tsv Standcurmag contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Standsal /dataTable/attribute ceamarc_env.tsv Standsal contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Standtheta /dataTable/attribute ceamarc_env.tsv Standtheta contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/pivot_wider_jupytool_notebook.ipynb /otherEntity dataset pivot_wider_jupytool_notebook.ipynb is about http://purl.obolibrary.org/obo/IAO_0000136 +/Marc Eléaume /ResponsibleParty dataset Marc Eléaume is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Marc Eléaume /ResponsibleParty dataset Marc Eléaume member of https://schema.org/memberOf +/Lenaïg G. Hemery /ResponsibleParty dataset Lenaïg G. Hemery is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Lenaïg G. Hemery /ResponsibleParty dataset Lenaïg G. Hemery member of https://schema.org/memberOf +/Ben Galton-Fenzi /ResponsibleParty dataset Ben Galton-Fenzi is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Ben Galton-Fenzi /ResponsibleParty dataset Ben Galton-Fenzi member of https://schema.org/memberOf +/Pauline Seguineau /ResponsibleParty dataset Pauline Seguineau is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Pauline Seguineau /ResponsibleParty dataset Pauline Seguineau member of https://schema.org/memberOf +/Yvan Le Bras /ResponsibleParty dataset Yvan Le Bras is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Yvan Le Bras /ResponsibleParty dataset Yvan Le Bras member of https://schema.org/memberOf diff -r 000000000000 -r ad96b20423cf test-data/annotemp/annotations_template_3.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/annotations_template_3.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,32 @@ +id element context subject predicate_label predicate_uri object_label object_uri +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 +/ceamarc_env.tsv /dataTable dataset ceamarc_env.tsv is about http://purl.obolibrary.org/obo/IAO_0000136 +/ceamarc_env.tsv/long /dataTable/attribute ceamarc_env.tsv long contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/lat /dataTable/attribute ceamarc_env.tsv lat contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Carbo /dataTable/attribute ceamarc_env.tsv Carbo contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Grav /dataTable/attribute ceamarc_env.tsv Grav contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Maxbearing /dataTable/attribute ceamarc_env.tsv Maxbearing contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Maxmagnit /dataTable/attribute ceamarc_env.tsv Maxmagnit contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Meancurmag /dataTable/attribute ceamarc_env.tsv Meancurmag contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Meansal /dataTable/attribute ceamarc_env.tsv Meansal contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Meantheta /dataTable/attribute ceamarc_env.tsv Meantheta contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Mud /dataTable/attribute ceamarc_env.tsv Mud contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Prof /dataTable/attribute ceamarc_env.tsv Prof contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Rugosity /dataTable/attribute ceamarc_env.tsv Rugosity contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Sand /dataTable/attribute ceamarc_env.tsv Sand contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Seaice_prod /dataTable/attribute ceamarc_env.tsv Seaice_prod contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Sili /dataTable/attribute ceamarc_env.tsv Sili contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Slope /dataTable/attribute ceamarc_env.tsv Slope contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Standcurmag /dataTable/attribute ceamarc_env.tsv Standcurmag contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Standsal /dataTable/attribute ceamarc_env.tsv Standsal contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/ceamarc_env.tsv/Standtheta /dataTable/attribute ceamarc_env.tsv Standtheta contains measurements of type http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType +/Marc Eléaume /ResponsibleParty dataset Marc Eléaume is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Marc Eléaume /ResponsibleParty dataset Marc Eléaume member of https://schema.org/memberOf +/Lenaïg G. Hemery /ResponsibleParty dataset Lenaïg G. Hemery is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Lenaïg G. Hemery /ResponsibleParty dataset Lenaïg G. Hemery member of https://schema.org/memberOf +/Ben Galton-Fenzi /ResponsibleParty dataset Ben Galton-Fenzi is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Ben Galton-Fenzi /ResponsibleParty dataset Ben Galton-Fenzi member of https://schema.org/memberOf +/Pauline Seguineau /ResponsibleParty dataset Pauline Seguineau is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Pauline Seguineau /ResponsibleParty dataset Pauline Seguineau member of https://schema.org/memberOf +/Yvan Le Bras /ResponsibleParty dataset Yvan Le Bras is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Yvan Le Bras /ResponsibleParty dataset Yvan Le Bras member of https://schema.org/memberOf diff -r 000000000000 -r ad96b20423cf test-data/annotemp/annotations_template_4.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/annotations_template_4.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,13 @@ +id element context subject predicate_label predicate_uri object_label object_uri +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 +/pivot_wider_jupytool_notebook.ipynb /otherEntity dataset pivot_wider_jupytool_notebook.ipynb is about http://purl.obolibrary.org/obo/IAO_0000136 +/Marc Eléaume /ResponsibleParty dataset Marc Eléaume is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Marc Eléaume /ResponsibleParty dataset Marc Eléaume member of https://schema.org/memberOf +/Lenaïg G. Hemery /ResponsibleParty dataset Lenaïg G. Hemery is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Lenaïg G. Hemery /ResponsibleParty dataset Lenaïg G. Hemery member of https://schema.org/memberOf +/Ben Galton-Fenzi /ResponsibleParty dataset Ben Galton-Fenzi is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Ben Galton-Fenzi /ResponsibleParty dataset Ben Galton-Fenzi member of https://schema.org/memberOf +/Pauline Seguineau /ResponsibleParty dataset Pauline Seguineau is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Pauline Seguineau /ResponsibleParty dataset Pauline Seguineau member of https://schema.org/memberOf +/Yvan Le Bras /ResponsibleParty dataset Yvan Le Bras is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type Person https://schema.org/Person +/Yvan Le Bras /ResponsibleParty dataset Yvan Le Bras member of https://schema.org/memberOf diff -r 000000000000 -r ad96b20423cf test-data/annotemp/attributes_ceamarc_env.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/attributes_ceamarc_env.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,20 @@ +attributeName attributeDefinition class unit dateTimeFormatString missingValueCode missingValueCodeExplanation +long Longitude is a geographic coordinate that specifies the east–west position of a point numeric degree "" -9999 no data available +lat Latitude is a geographic coordinate that specifies the north–south position of a point. numeric degree "" -9999 no data available +Carbo Percentage of biogenic carbonate numeric percent "" -9999 no data available +Grav Percentage of gravel in the seabed numeric percent "" -9999 no data available +Maxbearing Maximum current bearing numeric radian "" -9999 no data available +Maxmagnit Maximum current magnitude numeric meterPerSecond "" -9999 no data available +Meancurmag Mean current magnitude numeric meterPerSecond "" -9999 no data available +Meansal Mean salinity of the sea numeric PSU "" -9999 no data available +Meantheta Mean temperature of the sea numeric degreeCelsius "" -9999 no data available +Mud Percentage of mud in the seabed numeric percent "" -9999 no data available +Prof Depth of the sea numeric meter "" -9999 no data available +Rugosity Rugosity of the seabed numeric dimensionless "" -9999 no data available +Sand Percentage of sand in the seabed numeric percent "" -9999 no data available +Seaice_prod Seaice production in meter per year numeric meterPerYear "" -9999 no data available +Sili Percentage of biogenic silica in the seabed numeric percent "" -9999 no data available +Slope Slope of the seabed numeric degree "" -9999 no data available +Standcurmag Current magnitude standard deviation numeric meterPerSecond "" -9999 no data available +Standsal Salinity standard deviation numeric PSU "" -9999 no data available +Standtheta Temperature standard deviation numeric degreeCelsius "" -9999 no data available diff -r 000000000000 -r ad96b20423cf test-data/annotemp/ceamarc_env.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/ceamarc_env.tsv Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,10 @@ +long lat Carbo Grav Maxbearing Maxmagnit Meancurmag Meansal Meantheta Mud Prof Rugosity Sand Seaice_prod Sili Slope Standcurmag Standsal Standtheta +139.22 -65.57 0.88 28.59 3.67 0.03 0.03 34.62 -0.13 22.72 -441 -9999 55.76 0.24 3.27 0.28 0.01 0.01 0.18 +139.22 -65.57 0.88 28.61 3.64 0.02 0.03 34.62 -0.13 22.48 -439 -9999 55.74 0.24 3.29 0.27 0.01 0.01 0.18 +139.23 -65.57 0.92 28.62 3.59 0.02 0.03 34.62 -0.14 22.25 -438 -9999 56.28 0.25 3.32 0.22 0.01 0.01 0.19 +139.24 -65.57 0.92 28.63 3.51 0.01 0.03 34.62 -0.14 21.95 -436 -9999 56.57 0.26 3.3 0.08 0.01 0.01 0.19 +139.24 -65.57 0.92 28.64 3.35 0.01 0.03 34.62 -0.14 21.7 -437 -9999 56.58 0.26 3.28 0.05 0.01 0.01 0.19 +139.25 -65.57 0.93 28.65 3 0.0096293305978179 0.03 34.62 -0.15 21.44 -436 -9999 56.63 0.26 3.26 0.29 0.01 0.01 0.19 +139.26 -65.57 0.93 28.63 2.49 0.00871255807578564 0.03 34.62 -0.15 21.11 -432 -9999 56.67 0.26 3.23 0.43 0.01 0.01 0.19 +139.26 -65.57 0.93 28.64 2.01 0.01 0.03 34.62 -0.16 20.83 -429 -9999 56.71 0.26 3.21 0.37 0.01 0.01 0.19 +139.27 -65.57 0.94 28.65 1.71 0.01 0.03 34.62 -0.16 20.55 -427 -9999 56.75 0.26 3.19 0.32 0.01 0.01 0.2 diff -r 000000000000 -r ad96b20423cf test-data/annotemp/custom_units.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/custom_units.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,4 @@ +id unitType parentSI multiplierToSI description +PSU density gramPerGram 0.001 "1 PSU = 1g/kg" +degreeCelsius temperature dimensionless 1 "0 °C + 273,15 = 273,15 Kelvin" +meterPerYear speed centimeterPerYear 0.01 "meter per year" diff -r 000000000000 -r ad96b20423cf test-data/annotemp/entities.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/entities.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,2 @@ +objectName variable value +pivot_wider_jupytool_notebook.ipynb entityType executable diff -r 000000000000 -r ad96b20423cf test-data/annotemp/geographic_coverage.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/geographic_coverage.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,2 @@ +geographicDescription northBoundingCoordinate southBoundingCoordinate eastBoundingCoordinate westBoundingCoordinate +Dumont D'Urville sea sampling border -65.57 -67.01 145.53 139.22 diff -r 000000000000 -r ad96b20423cf test-data/annotemp/intellectual_rights.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/intellectual_rights.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,1 @@ +This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data ("Data User" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is." The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you. diff -r 000000000000 -r ad96b20423cf test-data/annotemp/keywords.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/keywords.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,9 @@ +keyword keywordThesaurus +Antarctica "" +Southern Ocean "" +Dumont D'Urville sea "" +CEAMARC "" +Abiotic and physical parameters "" +Ecoregionalization "" +Jupyter notebook "" +Pivot wider function "" diff -r 000000000000 -r ad96b20423cf test-data/annotemp/methods.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/methods.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,1 @@ +Abiotic and physical parameters in this dataset were taken from oceanographic model and in situ measurements (Hemery et al., 2011). Here we quote the relevant section of the “Materials and methods” part from the study: “Abiotic or physical parameters. Two different categories of abiotic parameters were used (Fig. 2): parameters likely to be subject to daily or seasonal variability (temperature, salinity, current magnitude, general current direction and sea-ice concentration) and parameters with long-term stability (bathymetry, slope, rugosity and seabed sediments). Parameters with short-term variability are taken from an oceanographic model, whereas parameters with long-term stability are derived from in situ measurements. Physical oceanography. Physical oceanographic parameters near the seabed (temperature, salinity, mean current magnitude, standard deviations of these 3 parameters, maximum current magnitude and general current direction) are taken from a climatologically forced ocean circulation model. The model was run for a total of 23 yr. A spinup phase of 20 yr was required for the model to reach quasi-equilibrium. The mean and SD were calculated by using 6-hourly model data for the last 3 yr. Sea ice growth rate was calculated directly from special sensor microwave imager (SSM/I) observations (Tamura et al. 2008). The model is based on the Rutgers/University of California Los Angeles Regional Ocean Model System (ROMS; Shchepetkin & McWilliams 2005) and is identical to the one described by B. K. Galton-Fenzi et al. (unpubl.). The model used here, initially developed for regional modelling studies of the Amery Ice Shelf/Ocean system and has been used in circum-Antarctic modelling studies, is able to simulate ice/ocean interactions at a high level of realism (Galton-Fenzi 2009, 2010). For the region of this study, the model is able to reproduce the circulation patterns and water mass properties when compared with oceanographic measurements taken in the vicinity of the George V and Adélie basins (A. Meijers pers. comm.). The model domain extends from 135.77° E to 158.08° E and from 69.417° S to 62.724° S. The southern boundary of the model is a closed, solid, free slip wall and the eastern and western boundaries are partially open. The horizontal grid resolution is between 2.16 km near the southern boundary to 2.88 km near the northern boundary. There are 31 vertical levels that are concentrated towards the top and bottom of the model domain. The parameters used here were taken from the lowest vertical level in the model that lies immediately adjacent to the seabed. The choice of mixing and advection schemes follows the choices that were successfully used by Dinniman et al. (2003, 2007) for studies of the shelf seas near the Ross Ice Shelf. The melting and freezing formulation uses the full 3-equation formulation and dynamics frazil ice model used in the studies of Galton-Fenzi (2009, 2010). Lateral boundary fields (potential temperature, salinity and currents) on the open boundaries are relaxed to monthly climatologies from ECCO2 (Menemenlis et al. 2008, Wunsch et al. 2009). Ten primary tidal constituents were added as a free-surface forcing. The tidal amplitudes and phase are calculated with a nonstandard ROMS subroutine, based on tidal information from the Proudman Oceanographic Laboratory (Murray 1964) and modified to be included within ROMS, yielding a standard tidal prediction. The bathymetry and the ice-draft information come from a version of R-TOPO (Timmermann et al. 2010) that was modified to include the high-resolution bathymetric data (outlined below, Beaman et al. 2011) and knowledge of the glacial ice drafts in the region (B. Legresy pers.comm.). The open ocean surface fluxes are modified by an imposed climatological sea-ice cover that includes polynyas, derived from SSM/I observations (Tamura et al. 2008). During summer, the Tamura et al. (2008) data are supplemented with open-water heat and salt fluxes by using the monthly climatologies from the NCEP-2 (Kanamitsu et al. 2002). Bathymetry and seabed sediments. A bathymetry model based on multibeam swath sonar and single beam bathymetry data was produced at about 250 m resolution for the study area (Beaman et al. 2011). A raster map of the slope gradient was generated from this bathymetry model by using ESRI ArcGIS 9.2 Spatial Analysis Tools and a raster map of the rugosity was generated by using Benthic Terrain Modeler tools. Sediment grabs provided substratum composition data, such as the percentage of gravel, mud, sand, biogenic carbonate and biogenic silica at sample sites (Beaman & O’Brien 2009). These data are used as a broad-scale representation of the seafloor substratum.” (Hemery et al., 2011). The Jupyter notebook file in this dataset was created with the Interactive JupyTool and the notebook Galaxy tool (https://ecology.usegalaxy.eu/root?tool_id=interactive_tool_jupyter_notebook). This Jupyter notebook script uses the pivot_wider function from the tidyr library to prepare data for the Ecoregionalization workflow. diff -r 000000000000 -r ad96b20423cf test-data/annotemp/personnel.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/personnel.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,7 @@ +givenName middleInitial surName organizationName electronicMailAddress userId role projectTitle fundingAgency fundingNumber +Marc "" Eléaume Muséum National d'Histoire Naturelle marc.eleaume@mnhn.fr 0000-0003-1999-1177 creator "" "" "" +Marc "" Eléaume Muséum National d'Histoire Naturelle marc.eleaume@mnhn.fr 0000-0003-1999-1177 contact "" "" "" +Lenaïg G. Hemery Pacific Northwest National Laboratory Marine Sciences Laboratory lenaighemery@gmail.com,lenaig.hemery@pnnl.gov 0000-0001-5337-4514 creator "" "" "" +Ben "" Galton-Fenzi Australian Antarctic Division:Kingston, Tasmania, AU "" 0000-0003-1404-4103 creator "" "" "" +Pauline "" Seguineau Muséum National d'Histoire Naturelle pauline.seguineau@mnhn.fr 0009-0009-5969-8478 creator "" "" "" +Yvan "" Le Bras Muséum National d'Histoire Naturelle yvan.le-bras@mnhn.fr 0000-0002-8504-068X creator "" "" "" diff -r 000000000000 -r ad96b20423cf test-data/annotemp/pivot_wider_jupytool_notebook.ipynb --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotemp/pivot_wider_jupytool_notebook.ipynb Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,85 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pivot wider Jupytool " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This Jupyter notebook is dedicated to the pivot_wider function from the tidyr R package. \n", + "This script is the final part of the data preparation for the ecoregionalization Galaxy workflow. " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#Date : 22/05/2024\n", + "#Author : Seguineau Pauline & Yvan Le Bras \n", + "\n", + "#Load libraries\n", + "library(tidyr)\n", + "\n", + "#load file \n", + "\n", + "input_path = \"galaxy_inputs\"\n", + "\n", + "for (dir in list.dirs(input_path)){\n", + " for (file in list.files(dir)) {\n", + " file_path = file.path(dir, file)}\n", + "}\n", + "\n", + "file = read.table(file_path,header=T, sep = \"\\t\")\n", + "\n", + "#Run pivot_wider function\n", + "pivot_file = pivot_wider(data = file,\n", + " names_from = phylum_class_order_family_genus_specificEpithet,\n", + " values_from = individualCount,\n", + " values_fill = 0,\n", + " values_fn = sum)\n", + "\n", + "#Replace all occurences >= 1 by 1 to have only presence (1) or absence (0) data\n", + "for(c in 3:length(pivot_file)){\n", + " pivot_file[c][pivot_file[c]>=1] <- 1}\n", + "\n", + "\n", + "write.table(pivot_file, \"outputs/pivot_file.tabular\", sep = \"\\t\", quote = F, row.names = F)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this Jupyter notebook, we used the pivot_wider function of the tidyr package to transform our data into a wider format and adapted to subsequent analyses as part of the Galaxy workflow for ecoregionalization. This transformation allowed us to convert our data to a format where each taxon becomes a separate column. We also took care to fill in the missing values with zeros and to sum the individual counts in case of duplications. Then all data >= 1 are replace by 1 to have only presence (1) or abscence (0) data.\n", + "\n", + "Thus, this notebook is an essential building block of our analysis pipeline, ensuring that the data is properly formatted and ready to be explored and interpreted for ecoregionalization studies." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.0.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff -r 000000000000 -r ad96b20423cf test-data/attributes_data_7.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/attributes_data_7.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,7 @@ +attributeName attributeDefinition class unit dateTimeFormatString missingValueCode missingValueCodeExplanation +LIB_SITE Description for: LIB_SITE character "" "" "" "" +X Description for: X numeric dimensionless "" "" "" +Y Description for: Y numeric dimensionless "" "" "" +PROGRAMME Description for: PROGRAMME categorical "" "" "" "" +OPERATEUR_ Description for: OPERATEUR_ categorical "" "" "" "" +STATUT_DU_ Description for: STATUT_DU_ categorical "" "" "" "" diff -r 000000000000 -r ad96b20423cf test-data/attributes_data_blary_al.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/attributes_data_blary_al.tabular Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,81 @@ +attributeName attributeDefinition class unit dateTimeFormatString missingValueCode missingValueCodeExplanation +Id Sampling site ID character "NA" "No information" +dist_field_margin Distance to the nearest field margin element (in meters) numeric meter NA No information +dist_hedgerow Distance to the nearest hedgerow element (in meters) numeric meter "NA" "No information" +dist_forest Distance to the nearest forest element (in meters) numeric meter "NA" "No information" +dist_road Distance to the nearest road element (in meters) numeric meter "NA" "No information" +dist_water Distance to the nearest water body element (in meters) numeric meter "NA" "No information" +dist_urban Distance to the nearest urban element (in meters) numeric meter "NA" "No information" +density_field_margins_250m Relative density of field margins in a 250 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_500m Relative density of filed margins in a 500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_750m Relative density of filed margins in a 750 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_1000m Relative density of filed margins in a 1000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_1500m Relative density of filed margins in a 1500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_2000m Relative density of filed margins in a 2000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_4000m Relative density of filed margins in a 4000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_250m Relative density of roads in a 250 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_500m Relative density of roads in a 500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_750m Relative density of roads in a 750 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_1000m Description for density_roads_1000m numeric dimensionless "NA" "No information" +density_roads_1500m Relative density of roads in a 1500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_2000m Relative density of roads in a 2000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_4000m Relative density of roads in a 4000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_250m Relative density of hedgerows in a 250 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_500m Relative density of hedgerows in a 500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_750m Relative density of hedgerows in a 750 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_1000m Relative density of hedgerows in a 1000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_1500m Relative density of hedgerows in a 1500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_2000m Relative density of hedgerows in a 2000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_4000m Relative density of hedgerows in a 4000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +shannon_250m Land-cover Shannon diversity index in 250 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_500m Land-cover Shannon diversity index in 500 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_750m Land-cover Shannon diversity index in 750 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_1000m Land-cover Shannon diversity index in 1000 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_1500m Land-cover Shannon diversity index in 1500 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_2000m Land-cover Shannon diversity index in 2000 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_4000m Land-cover Shannon diversity index in 4000 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +grassland_250 Percentage of land covered by grassland in different 250 m buffer sizes numeric percent "NA" "No information" +forest_250m Percentage of land covered by forest in different 250 m buffer sizes numeric percent "NA" "No information" +urban_250m Percentage of land covered by urban area in different 250 m buffer sizes numeric percent "NA" "No information" +grassland_500 Percentage of land covered by grassland in different 500 m buffer sizes numeric percent "NA" "No information" +forest_500m Percentage of land covered by forest in different 500 m buffer sizes numeric percent "NA" "No information" +urban_500m Percentage of land covered by urban area in different 500 m buffer sizes numeric percent "NA" "No information" +grassland_750 Percentage of land covered by grassland in different 750 m buffer sizes numeric percent "NA" "No information" +forest_750m Percentage of land covered by forest in different 750 m buffer sizes numeric percent "NA" "No information" +urban_750m Percentage of land covered by urban area in different 750 m buffer sizes numeric percent "NA" "No information" +grassland_1000 Percentage of land covered by grassland in different 1000 m buffer sizes numeric percent "NA" "No information" +forest_1000m Percentage of land covered by forest in different 1000 m buffer sizes numeric percent "NA" "No information" +urban_1000m Percentage of land covered by urban area in different 1000 m buffer sizes numeric percent "NA" "No information" +grassland_1500 Percentage of land covered by grassland in different 1500 m buffer sizes numeric percent "NA" "No information" +forest_1500m Percentage of land covered by forest in different 1500 m buffer sizes numeric percent "NA" "No information" +urban_1500m Percentage of land covered by urban area in different 1500 m buffer sizes numeric percent "NA" "No information" +grassland_2000 Percentage of land covered by grassland in different 2000 m buffer sizes numeric percent "NA" "No information" +forest_2000m Description for forest_2000m numeric percent "NA" "No information" +urban_2000m Percentage of land covered by urban area in different 2000 m buffer sizes numeric percent "NA" "No information" +grassland_4000 Percentage of land covered by grassland in different 4000 m buffer sizes numeric percent "NA" "No information" +forest_4000m Percentage of land covered by forest in different 4000 m buffer sizes numeric percent "NA" "No information" +urban_4000m Percentage of land covered by urban area in different 4000 m buffer sizes numeric percent "NA" "No information" +perc_rapeseed Percentage of rapeseed crops in a 100 m radius around each site at the sampling date numeric percent "NA" "No information" +nb_crops Number of different crops in a 100 m radius around each site at the sampling date numeric number "NA" "No information" +date Description for date Date YYYY-MM-DD "NA" "No information" +X Sampling site longitude numeric dimensionless "NA" "No information" +Y Sampling site latitude numeric dimensionless "NA" "No information" +recorder Recorder ID used for data collection on the sampling site categorical "NA" "No information" +min_t Minimum temperature recorded on the sampling area during the sampling night (in celsius) numeric celsius "NA" "No information" +max_wind Maximum wind speed recorded on the sampling area during the sampling night (in km/h) numeric kilometerPerHour "NA" "No information" +CSI_ER05 Community specialization index calculated from species level activity on the 0.5 maximum error rate tolerance threshold numeric dimensionless "NA" "No information" +CSI_ER01 Community specialization index calculated from species level activity on the 0.1 maximum error rate tolerance threshold numeric dimensionless "NA" "No information" +richness_ER05 Species richness calculated from species level activity on the 0.5 maximum error rate tolerance threshold numeric dimensionless "NA" "No information" +richness_ER01 Species richness calculated from species level activity on the 0.1 maximum error rate tolerance threshold numeric dimensionless "NA" "No information" +Nyclei_ER05 Number of bat passes of Nyclei specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Nyclei_ER01 Number of bat passes of Nyclei specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Pippip_ER05 Number of bat passes of Pippip specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Pippip_ER01 Number of bat passes of Pippip specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Eptser_ER05 Number of bat passes of Epster specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Eptser_ER01 Number of bat passes of Epster specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Nycnoc_ER05 Number of bat passes of Nycnoc specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Nycnoc_ER01 Number of bat passes of Nycnoc specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Myo_ER05 Number of bat passes of Myo specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Myo_ER01 Number of bat passes of Myo specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Ple_ER05 Number of bat passes of Ple specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Ple_ER01 Number of bat passes of Ple specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" diff -r 000000000000 -r ad96b20423cf test-data/attributes_data_blary_al.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/attributes_data_blary_al.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,81 @@ +attributeName attributeDefinition class unit dateTimeFormatString missingValueCode missingValueCodeExplanation +Id Sampling site ID character "NA" "No information" +dist_field_margin Distance to the nearest field margin element (in meters) numeric meter NA No information +dist_hedgerow Distance to the nearest hedgerow element (in meters) numeric meter "NA" "No information" +dist_forest Distance to the nearest forest element (in meters) numeric meter "NA" "No information" +dist_road Distance to the nearest road element (in meters) numeric meter "NA" "No information" +dist_water Distance to the nearest water body element (in meters) numeric meter "NA" "No information" +dist_urban Distance to the nearest urban element (in meters) numeric meter "NA" "No information" +density_field_margins_250m Relative density of field margins in a 250 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_500m Relative density of filed margins in a 500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_750m Relative density of filed margins in a 750 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_1000m Relative density of filed margins in a 1000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_1500m Relative density of filed margins in a 1500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_2000m Relative density of filed margins in a 2000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_field_margins_4000m Relative density of filed margins in a 4000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_250m Relative density of roads in a 250 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_500m Relative density of roads in a 500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_750m Relative density of roads in a 750 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_1000m Description for density_roads_1000m numeric dimensionless "NA" "No information" +density_roads_1500m Relative density of roads in a 1500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_2000m Relative density of roads in a 2000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_roads_4000m Relative density of roads in a 4000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_250m Relative density of hedgerows in a 250 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_500m Relative density of hedgerows in a 500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_750m Relative density of hedgerows in a 750 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_1000m Relative density of hedgerows in a 1000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_1500m Relative density of hedgerows in a 1500 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_2000m Relative density of hedgerows in a 2000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +density_hedgerows_4000m Relative density of hedgerows in a 4000 meters buffer around the sampling site (in meters per hectare) numeric dimensionless "NA" "No information" +shannon_250m Land-cover Shannon diversity index in 250 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_500m Land-cover Shannon diversity index in 500 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_750m Land-cover Shannon diversity index in 750 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_1000m Land-cover Shannon diversity index in 1000 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_1500m Land-cover Shannon diversity index in 1500 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_2000m Land-cover Shannon diversity index in 2000 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +shannon_4000m Land-cover Shannon diversity index in 4000 meters buffer around the sampling site (no unit of measurement) numeric dimensionless "NA" "No information" +grassland_250 Percentage of land covered by grassland in different 250 m buffer sizes numeric percent "NA" "No information" +forest_250m Percentage of land covered by forest in different 250 m buffer sizes numeric percent "NA" "No information" +urban_250m Percentage of land covered by urban area in different 250 m buffer sizes numeric percent "NA" "No information" +grassland_500 Percentage of land covered by grassland in different 500 m buffer sizes numeric percent "NA" "No information" +forest_500m Percentage of land covered by forest in different 500 m buffer sizes numeric percent "NA" "No information" +urban_500m Percentage of land covered by urban area in different 500 m buffer sizes numeric percent "NA" "No information" +grassland_750 Percentage of land covered by grassland in different 750 m buffer sizes numeric percent "NA" "No information" +forest_750m Percentage of land covered by forest in different 750 m buffer sizes numeric percent "NA" "No information" +urban_750m Percentage of land covered by urban area in different 750 m buffer sizes numeric percent "NA" "No information" +grassland_1000 Percentage of land covered by grassland in different 1000 m buffer sizes numeric percent "NA" "No information" +forest_1000m Percentage of land covered by forest in different 1000 m buffer sizes numeric percent "NA" "No information" +urban_1000m Percentage of land covered by urban area in different 1000 m buffer sizes numeric percent "NA" "No information" +grassland_1500 Percentage of land covered by grassland in different 1500 m buffer sizes numeric percent "NA" "No information" +forest_1500m Percentage of land covered by forest in different 1500 m buffer sizes numeric percent "NA" "No information" +urban_1500m Percentage of land covered by urban area in different 1500 m buffer sizes numeric percent "NA" "No information" +grassland_2000 Percentage of land covered by grassland in different 2000 m buffer sizes numeric percent "NA" "No information" +forest_2000m Description for forest_2000m numeric percent "NA" "No information" +urban_2000m Percentage of land covered by urban area in different 2000 m buffer sizes numeric percent "NA" "No information" +grassland_4000 Percentage of land covered by grassland in different 4000 m buffer sizes numeric percent "NA" "No information" +forest_4000m Percentage of land covered by forest in different 4000 m buffer sizes numeric percent "NA" "No information" +urban_4000m Percentage of land covered by urban area in different 4000 m buffer sizes numeric percent "NA" "No information" +perc_rapeseed Percentage of rapeseed crops in a 100 m radius around each site at the sampling date numeric percent "NA" "No information" +nb_crops Number of different crops in a 100 m radius around each site at the sampling date numeric number "NA" "No information" +date Description for date Date YYYY-MM-DD "NA" "No information" +X Sampling site longitude numeric dimensionless "NA" "No information" +Y Sampling site latitude numeric dimensionless "NA" "No information" +recorder Recorder ID used for data collection on the sampling site categorical "NA" "No information" +min_t Minimum temperature recorded on the sampling area during the sampling night (in celsius) numeric celsius "NA" "No information" +max_wind Maximum wind speed recorded on the sampling area during the sampling night (in km/h) numeric kilometerPerHour "NA" "No information" +CSI_ER05 Community specialization index calculated from species level activity on the 0.5 maximum error rate tolerance threshold numeric dimensionless "NA" "No information" +CSI_ER01 Community specialization index calculated from species level activity on the 0.1 maximum error rate tolerance threshold numeric dimensionless "NA" "No information" +richness_ER05 Species richness calculated from species level activity on the 0.5 maximum error rate tolerance threshold numeric dimensionless "NA" "No information" +richness_ER01 Species richness calculated from species level activity on the 0.1 maximum error rate tolerance threshold numeric dimensionless "NA" "No information" +Nyclei_ER05 Number of bat passes of Nyclei specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Nyclei_ER01 Number of bat passes of Nyclei specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Pippip_ER05 Number of bat passes of Pippip specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Pippip_ER01 Number of bat passes of Pippip specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Eptser_ER05 Number of bat passes of Epster specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Eptser_ER01 Number of bat passes of Epster specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Nycnoc_ER05 Number of bat passes of Nycnoc specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Nycnoc_ER01 Number of bat passes of Nycnoc specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Myo_ER05 Number of bat passes of Myo specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Myo_ER01 Number of bat passes of Myo specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" +Ple_ER05 Number of bat passes of Ple specie or group recorded during the sampling night on the 0.5 maximum error rate tolerance threshold numeric number "NA" "No information" +Ple_ER01 Number of bat passes of Ple specie or group recorded during the sampling night on the 0.1 maximum error rate tolerance threshold numeric number "NA" "No information" diff -r 000000000000 -r ad96b20423cf test-data/catvars_data_7.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/catvars_data_7.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,13 @@ +attributeName code definition +PROGRAMME DCE Value: DCE for attribute: PROGRAMME +PROGRAMME ELV Value: ELV for attribute: PROGRAMME +PROGRAMME DCE/ELV Value: DCE/ELV for attribute: PROGRAMME +PROGRAMME REBENT Value: REBENT for attribute: PROGRAMME +PROGRAMME REBENT/DCE Value: REBENT/DCE for attribute: PROGRAMME +OPERATEUR_ MNHN CC Value: MNHN CC for attribute: OPERATEUR_ +OPERATEUR_ Station de Wimer Value: Station de Wimer for attribute: OPERATEUR_ +OPERATEUR_ Ifremer Anglet Value: Ifremer Anglet for attribute: OPERATEUR_ +OPERATEUR_ Bio-Littoral Value: Bio-Littoral for attribute: OPERATEUR_ +OPERATEUR_ Ecosub Value: Ecosub for attribute: OPERATEUR_ +STATUT_DU_ actif Value: actif for attribute: STATUT_DU_ +STATUT_DU_ inactif Value: inactif for attribute: STATUT_DU_ diff -r 000000000000 -r ad96b20423cf test-data/catvars_data_blary_al.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/catvars_data_blary_al.tabular Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,16 @@ +attributeName code definition +recorder 10250 Value: 10250 for attribute: recorder +recorder 11693 Value: 11693 for attribute: recorder +recorder 13289 Value: 13289 for attribute: recorder +recorder 13676 Value: 13676 for attribute: recorder +recorder 13733 Value: 13733 for attribute: recorder +recorder 13740 Value: 13740 for attribute: recorder +recorder 17232 Value: 17232 for attribute: recorder +recorder 17329 Value: 17329 for attribute: recorder +recorder 17573 Value: 17573 for attribute: recorder +recorder 17578 Value: 17578 for attribute: recorder +recorder 17598 Value: 17598 for attribute: recorder +recorder 17601 Value: 17601 for attribute: recorder +recorder 17607 Value: 17607 for attribute: recorder +recorder 17608 Value: 17608 for attribute: recorder +recorder 3760 Value: 3760 for attribute: recorder diff -r 000000000000 -r ad96b20423cf test-data/catvars_data_blary_al.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/catvars_data_blary_al.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,16 @@ +attributeName code definition +recorder 10250 Value: 10250 for attribute: recorder +recorder 11693 Value: 11693 for attribute: recorder +recorder 13289 Value: 13289 for attribute: recorder +recorder 13676 Value: 13676 for attribute: recorder +recorder 13733 Value: 13733 for attribute: recorder +recorder 13740 Value: 13740 for attribute: recorder +recorder 17232 Value: 17232 for attribute: recorder +recorder 17329 Value: 17329 for attribute: recorder +recorder 17573 Value: 17573 for attribute: recorder +recorder 17578 Value: 17578 for attribute: recorder +recorder 17598 Value: 17598 for attribute: recorder +recorder 17601 Value: 17601 for attribute: recorder +recorder 17607 Value: 17607 for attribute: recorder +recorder 17608 Value: 17608 for attribute: recorder +recorder 3760 Value: 3760 for attribute: recorder diff -r 000000000000 -r ad96b20423cf test-data/custom_units.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/custom_units.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,1 @@ +id unitType parentSI multiplierToSI description diff -r 000000000000 -r ad96b20423cf test-data/data_7.GeoJSON --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/data_7.GeoJSON Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,72 @@ +{ +"type": "FeatureCollection", +"name": "shapefile", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "LIB_SITE": "Rohein", "X": -2.630417, "Y": 48.64695, "PROGRAMME": "DCE", "OPERATEUR_": "MNHN CC", 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"properties": { "LIB_SITE": "Baguenaud SR", "X": -2.35355, "Y": 47.232017, "PROGRAMME": "DCE\/ELV", "OPERATEUR_": "Bio-Littoral", "STATUT_DU_": "actif" }, "geometry": { "type": "Point", "coordinates": [ -2.35355, 47.232017 ] } }, +{ "type": "Feature", "properties": { "LIB_SITE": "La Banche Nord SR", "X": -2.4672, "Y": 47.184617, "PROGRAMME": "DCE\/ELV", "OPERATEUR_": "Bio-Littoral", "STATUT_DU_": "actif" }, "geometry": { "type": "Point", "coordinates": [ -2.4672, 47.184617 ] } }, +{ "type": "Feature", "properties": { "LIB_SITE": "La Pointe Saint Gildas", "X": -2.253433, "Y": 47.134083, "PROGRAMME": "DCE\/ELV", "OPERATEUR_": "Bio-Littoral", "STATUT_DU_": "actif" }, "geometry": { "type": "Point", "coordinates": [ -2.253433, 47.134083 ] } }, +{ "type": "Feature", "properties": { "LIB_SITE": "La Banche Sud SR", "X": -2.467883, "Y": 47.17335, "PROGRAMME": "DCE\/ELV", "OPERATEUR_": "Bio-Littoral", "STATUT_DU_": "actif" }, "geometry": { "type": "Point", "coordinates": [ -2.467883, 47.17335 ] } } +] +} diff -r 000000000000 -r ad96b20423cf test-data/datafile_1.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/datafile_1.tsv Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,113 @@ +Id density_boats distance_coast percentage_turbidity nb_individual recorder min_t max_wind +ID1 0 7.083851986 1 1 13740 9 13 +ID2 0 5.057419245 1 1 3760 11.7 15 +ID3 0 14.81271925 1 1 17598 9 13 +ID4 23.38130966 17.39474876 0 1 17607 12.8 24 +ID5 0 13.8788222 1 1 17608 14.3 17 +ID6 0 20.55593297 1 1 13733 20 17 +ID7 0 5.228906117 1 1 10250 14.3 30 +ID8 0 8.18336163 0.963336783 2 13740 11.7 15 +ID9 0 14.77478497 1 1 13733 11.7 15 +ID10 0 18.23789045 1 1 17573 9 13 +ID11 0 30.97770737 0.934057759 2 17608 8.1 13 +ID12 0 14.72135785 0 2 13733 8.1 13 +ID13 0 26.97792869 1 1 17578 8.1 13 +ID14 0 8.63834361 1 1 17329 11.7 15 +ID15 23.42372625 2.371250707 0.611485317 2 17232 14.3 30 +ID16 0 2.058590657 1 1 NA 11.7 15 +ID17 0 14.5557442 1 1 11693 14.3 17 +ID18 0 15.31628381 0.484376584 2 10250 14.2 9 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22.89867383 0.50376408 1 1 17573 14.3 30 +ID91 0 7.486419194 1 1 13676 14.2 9 +ID92 0 4.612337295 1 1 17601 11.7 15 +ID93 0 20.34521788 1 1 17232 11.7 15 +ID94 0 13.69731259 0.558754442 2 13289 14.3 17 +ID95 0 0.481041313 0.601822162 2 17607 14.3 17 +ID96 0 19.55721647 0.856473518 3 17232 16.9 13 +ID97 0 9.467489106 1 1 10250 17.4 15 +ID98 0 22.78746039 0.460271388 3 11693 14.3 30 +ID99 0 8.380520951 0.769578171 2 17607 9 13 +ID100 32.43763517 0.486507892 0.297384797 4 17573 12.3 11 +ID101 39.87341993 18.47598574 0.377696294 3 17329 NA NA +ID102 0 1.482655198 0.420523151 2 17598 NA NA +ID103 0 1.188455008 1 1 13289 8.1 13 +ID104 0 2.981949867 1 1 17232 13.3 0 +ID105 6.048310547 0.06224536 1 1 17578 17.4 14 +ID106 0 0.067957866 1 1 17232 17.4 14 +ID107 0 0.305516266 0 1 17598 17.4 14 +ID108 0 1.375212224 0.512278187 2 17601 20 17 +ID109 23.18323585 0.045282164 0.355925541 2 13676 17.4 14 +ID110 0 0 0.599181014 2 17232 13.3 0 +ID111 0 0.050922259 1 1 17329 14.9 13 +ID112 0 0.763920326 1 1 13740 13.3 0 diff -r 000000000000 -r ad96b20423cf test-data/entities.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/entities.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,4 @@ +objectName variable value +data_7.GeoJSON geometry Point +data_7.GeoJSON geometricObjectCount 65 +data_7.GeoJSON spatialReference GCS_WGS_1984 diff -r 000000000000 -r ad96b20423cf test-data/geographic_coverage.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/geographic_coverage.tabular Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,2 @@ +geographicDescription northBoundingCoordinate southBoundingCoordinate eastBoundingCoordinate westBoundingCoordinate +Yvelines - Essonne - Seine et Marne 49.08428 48.12266 3.56409 1.60296 diff -r 000000000000 -r ad96b20423cf test-data/geographic_coverage.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/geographic_coverage.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,5 @@ +geographicDescription northBoundingCoordinate southBoundingCoordinate eastBoundingCoordinate westBoundingCoordinate +1 -65.999946 -65.999946 142.3360535 142.3360535 +2 -66.394359 -66.394359 140.4840125 140.4840125 +3 -66.023545 -66.023545 142.745584 142.745584 +4 -66.3379125 -66.3379125 144.011634 144.011634 diff -r 000000000000 -r ad96b20423cf test-data/keywords.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keywords.tabular Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,7 @@ +keyword keywordThesaurus +Acoustic monitoring Keywords +Bat community Keywords +Farmland biodiversity Keywords +Field borders Keywords +Habitat specialisation Keywords +Landscape composition Keywords diff -r 000000000000 -r ad96b20423cf test-data/keywords.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keywords.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,7 @@ +keyword keywordThesaurus +Acoustic monitoring Keywords +Bat community Keywords +Farmland biodiversity Keywords +Field borders Keywords +Habitat specialisation Keywords +Landscape composition Keywords diff -r 000000000000 -r ad96b20423cf test-data/metadataoutput.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/metadataoutput.xml Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,153 @@ + + + + + + Constance + Blary + + CEFE + constance.blary@cefe.cnrs.fr + + + + Kévin + Barré + + CESCO MNHN + kevin.barre@mnhn.fr + + + + Christian + Kerbiriou + + CESCO + christian.kerbiriou@mnhn.fr + + + + Isabelle + Le Viol + + CESCO + isabelle.le-viol@mnhn.fr + + 2023-07-23 + + Acoustic monitoring + Bat community + Farmland biodiversity + Field borders + Habitat specialisation + Landscape composition + Keywords + + + + Yvelines - Essonne - Seine et Marne + + 1.60296 + 3.56409 + 49.08428 + 48.12266 + + + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_33949 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_4560 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_1560 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_1560 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://www.eionet.europa.eu/gemet/concept/4648 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://www.eionet.europa.eu/gemet/concept/2519 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://www.eionet.europa.eu/gemet/concept/420 + + + + Constance + Blary + + CEFE + constance.blary@cefe.cnrs.fr + + + + Kévin + Barré + + CESCO MNHN + kevin.barre@mnhn.fr + + + Effect of agricultural landscape features on bats + + + Kévin + Barré + + CESCO MNHN + kevin.barre@mnhn.fr + Principal Investigator + + DIM ASTREA Région Ile-de-France, FRB + + + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_33949 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_4560 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_1560 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://aims.fao.org/aos/agrovoc/c_1560 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://www.eionet.europa.eu/gemet/concept/4648 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://www.eionet.europa.eu/gemet/concept/2519 + + + http://purl.obolibrary.org/obo/IAO_0000136 + http://www.eionet.europa.eu/gemet/concept/420 + + + + + + EMLassemblyline + 3.5.5 + + + + diff -r 000000000000 -r ad96b20423cf test-data/personnel.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/personnel.tabular Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,8 @@ +givenName middleInitial surName organizationName electronicMailAddress userId role projectTitle fundingAgency fundingNumber +Constance Blary CEFE constance.blary@cefe.cnrs.fr https://orcid.org/0000-0001-6204-9983 creator +Kévin Barré CESCO MNHN kevin.barre@mnhn.fr https://orcid.org/0000-0001-5368-4053 creator +Christian Kerbiriou CESCO christian.kerbiriou@mnhn.fr https://orcid.org/0000-0001-6080-4762 creator +Isabelle Le Viol CESCO isabelle.le-viol@mnhn.fr creator +Constance "" Blary CEFE constance.blary@cefe.cnrs.fr https://orcid.org/0000-0001-6204-9983 contact +Kévin "" Barré CESCO MNHN kevin.barre@mnhn.fr https://orcid.org/0000-0001-5368-4053 contact +Kévin "" Barré CESCO MNHN kevin.barre@mnhn.fr https://orcid.org/0000-0001-5368-4053 PI Effect of agricultural landscape features on bats DIM ASTREA Région Ile-de-France, FRB diff -r 000000000000 -r ad96b20423cf test-data/personnel.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/personnel.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,8 @@ +givenName middleInitial surName organizationName electronicMailAddress userId role projectTitle fundingAgency fundingNumber +Constance Blary CEFE constance.blary@cefe.cnrs.fr https://orcid.org/0000-0001-6204-9983 creator +Kévin Barré CESCO MNHN kevin.barre@mnhn.fr https://orcid.org/0000-0001-5368-4053 creator +Christian Kerbiriou CESCO christian.kerbiriou@mnhn.fr https://orcid.org/0000-0001-6080-4762 creator +Isabelle Le Viol CESCO isabelle.le-viol@mnhn.fr creator +Constance "" Blary CEFE constance.blary@cefe.cnrs.fr https://orcid.org/0000-0001-6204-9983 contact +Kévin "" Barré CESCO MNHN kevin.barre@mnhn.fr https://orcid.org/0000-0001-5368-4053 contact +Kévin "" Barré CESCO MNHN kevin.barre@mnhn.fr https://orcid.org/0000-0001-5368-4053 PI Effect of agricultural landscape features on bats DIM ASTREA Région Ile-de-France, FRB diff -r 000000000000 -r ad96b20423cf test-data/report.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,2 @@ +Your EML is not valid. You can improve it by correcting this error: +[1] "Element 'creator': This element is not expected. Expected is one of ( references, alternateIdentifier, shortName, title )." diff -r 000000000000 -r ad96b20423cf test-data/taxa.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/taxa.tsv Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,5 @@ +taxa site +Achelia_sp 1 +Achelia_spicata 2 +Achelia_suflata 2 +Ammothea_adunca 3 diff -r 000000000000 -r ad96b20423cf test-data/taxonomic_coverage.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/taxonomic_coverage.txt Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,5 @@ +name name_type name_resolved authority_system authority_id +Achelia_sp scientific Achelia GBIF Backbone Taxonomy 2118396 +Achelia_spicata scientific Achelia spicata GBIF Backbone Taxonomy 4338694 +Achelia_suflata scientific Achelia suflata GBIF Backbone Taxonomy NA +Ammothea_adunca scientific Ammothea adunca GBIF Backbone Taxonomy 5720487 diff -r 000000000000 -r ad96b20423cf test-data/test-geocov.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test-geocov.tabular Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,5 @@ +site lat long +1 -65.999946 142.3360535 +2 -66.394359 140.4840125 +3 -66.023545 142.745584 +4 -66.3379125 144.011634 diff -r 000000000000 -r ad96b20423cf test-data/test_geocov.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test_geocov.tsv Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,5 @@ +site lat long +1 -65.999946 142.3360535 +2 -66.394359 140.4840125 +3 -66.023545 142.745584 +4 -66.3379125 144.011634 diff -r 000000000000 -r ad96b20423cf vector_templates.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/vector_templates.R Fri Sep 27 13:01:04 2024 +0000 @@ -0,0 +1,22 @@ +#15/12/2023 #SEGUINEAU Pauline + +#Load packages + +library(EMLassemblyline) + +#Load arguments + +if (length(commandArgs(trailingOnly = TRUE)) > 0) { + data_vector <- commandArgs(trailingOnly = TRUE)[1] +} + +#Transform arguments + +vector = strsplit(data_vector," ") + +#Make templates to describe data raster + +template_vector_attributes(path = ".",data.path= "data_files", vector.file = vector[[1]]) +template_categorical_variables(path = ".", data.path = "data_files") + +