# HG changeset patch # User ecology # Date 1701481750 0 # Node ID dc2dfad1627b6051387ad0cb01eeef33fb7c54e6 planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/EMLassemblyline commit ea09df299078ff13beda210b36b7edaa6a79c099 diff -r 000000000000 -r dc2dfad1627b eml2eal.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/eml2eal.R Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b geo_cov_temp.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/geo_cov_temp.R Sat Dec 02 01:49:10 2023 +0000 @@ -0,0 +1,37 @@ +#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,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 dc2dfad1627b geo_cov_template.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/geo_cov_template.xml Sat Dec 02 01:49:10 2023 +0000 @@ -0,0 +1,69 @@ + + for EML metadata creation + + r-base + r-emlassemblyline + + + + + + + + + + + + + + + + + + + + + + + + diff -r 000000000000 -r dc2dfad1627b make_eml.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/make_eml.R Sat Dec 02 01:49:10 2023 +0000 @@ -0,0 +1,161 @@ +##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_other <- args[5] + destable <- args[6] + desother <- args[7] + quote <- args[8] + table_url <- args[9] + other_url <- args[10] +} + + +###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_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_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,",") + } +} + +###Make EML### + +if (!is.null(table) && !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(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(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(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]])} + + + +old.names <- list.files(path=".", pattern=".xml") +print(old.names) +file.rename(from=old.names, to="eml.xml") + + + + + + diff -r 000000000000 -r dc2dfad1627b table_templates.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/table_templates.R Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b taxo_cov_template.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/taxo_cov_template.R Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b templates1.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/templates1.R Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b 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 Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b test-data/annotations.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotations.tabular Sat Dec 02 01:49:10 2023 +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 http://www.eionet.europa.eu/gemet/concept/4648 +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 Ecosystem http://www.eionet.europa.eu/gemet/concept/2519 +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 Animal ecology http://www.eionet.europa.eu/gemet/concept/420 +/data_blary_al.txt/X /dataTable/attribute data_blary_al.txt X is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type decimalLongitude http://rs.tdwg.org/dwc/terms/decimalLongitude +/data_blary_al.txt/Y /dataTable/attribute data_blary_al.txt Y is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type decimalLatitude http://rs.tdwg.org/dwc/terms/decimalLatitude diff -r 000000000000 -r dc2dfad1627b test-data/annotations.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotations.txt Sat Dec 02 01:49:10 2023 +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 http://www.eionet.europa.eu/gemet/concept/4648 +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 Ecosystem http://www.eionet.europa.eu/gemet/concept/2519 +/dataset /dataset eml dataset is about http://purl.obolibrary.org/obo/IAO_0000136 Animal ecology http://www.eionet.europa.eu/gemet/concept/420 +/data_blary_al.txt/X /dataTable/attribute data_blary_al.txt X is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type decimalLongitude http://rs.tdwg.org/dwc/terms/decimalLongitude +/data_blary_al.txt/Y /dataTable/attribute data_blary_al.txt Y is a http://www.w3.org/1999/02/22-rdf-syntax-ns#type decimalLatitude http://rs.tdwg.org/dwc/terms/decimalLatitude diff -r 000000000000 -r dc2dfad1627b 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 Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b 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 Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b 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 Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b 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 Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b test-data/datafile_1.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/datafile_1.tsv Sat Dec 02 01:49:10 2023 +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 +ID19 22.68310089 18.12266606 1 1 13733 16.9 13 +ID20 0 10.71630644 0.946424061 2 17608 NA NA +ID21 0 7.492501839 1 1 17608 14.3 30 +ID22 0 2.455167732 1 1 17578 8.1 13 +ID23 0 0.215005092 0.619357094 2 17608 13.3 0 +ID24 0 20.5942275 0.530977409 2 17573 12.8 24 +ID25 0 0.00565931 1 1 10250 9 13 +ID26 0 4.61669024 1 1 3760 8.1 13 +ID27 0 0.650746944 1 1 13289 NA NA +ID28 11.55094443 4.256523462 0.534748923 2 17608 17.4 15 +ID29 25.29249263 0.333785924 0 3 11693 16.9 13 +ID30 5.659985665 3.927116342 1 1 17578 16.9 13 +ID31 0 0.011314137 0 2 13740 20 17 +ID32 0 6.724401426 0.262229612 3 17578 8.5 7 +ID33 0 10.20546782 0.456408314 2 13676 17.8 13 +ID34 0 0.005662514 1 1 13289 9 13 +ID35 0 12.50424544 1 1 13733 13.3 0 +ID36 41.24173899 0.028288543 0 2 17607 NA NA +ID37 0 0.707453733 0 2 13733 17.8 13 +ID38 7.278017273 8.411585021 0.521433594 2 17601 17.4 15 +ID39 26.46665163 3.174693 1 1 17598 11.7 15 +ID40 21.65723654 8.846001472 0.672489759 2 17232 9 13 +ID41 0 7.296088753 0.033790965 2 17601 9 13 +ID42 0 0.848848396 0 3 17608 17.4 14 +ID43 0 17.47693701 1 1 NA 8.1 13 +ID44 0 8.040770102 1 1 17329 8.1 13 +ID45 0 9.227200724 1 1 17607 8.1 13 +ID46 0 4.089829166 0 1 17607 17.4 15 +ID47 16.24258106 12.9081546 0.519677866 2 17232 14.3 17 +ID48 0 28.17682685 0 3 13733 14.2 9 +ID49 0 11.13752122 0.568145458 2 17329 12.8 24 +ID50 0 12.15482119 0.744442086 2 13676 17.4 14 +ID51 0 1.075147125 0.126547756 2 17329 17.8 13 +ID52 0 0.639429606 0.318501019 3 17573 17.8 13 +ID53 0 11.34563151 1 1 13289 9 13 +ID54 0 6.070721358 1 1 11693 8.1 13 +ID55 0 2.534940304 1 1 3760 NA NA +ID56 0 0 1 1 17608 14.9 13 +ID57 23.6734518 9.73526417 1 1 13733 14.9 13 +ID58 22.77461434 2.30834512 0 2 3760 17.4 15 +ID59 0 9.034849513 1 1 NA 14.3 30 +ID60 0 1.61299451 1 1 17598 8.1 13 +ID61 0 1.550212164 1 1 17601 8.1 13 +ID62 16.6417688 0.198064626 1 1 13676 13.3 0 +ID63 0 0.147167035 0.503759276 2 13740 14.9 13 +ID64 0 1.126075147 1 1 17329 14.2 9 +ID65 0 1.166213768 0.486555056 2 10250 17.4 14 +ID66 0 37.17890687 0.241246715 3 11693 12.3 11 +ID67 0 1.01318843 1 1 NA 11.7 15 +ID68 0 14.97426034 0.513275979 3 17578 8.5 7 +ID69 0 0.050933786 1 1 10250 13.3 0 +ID70 0 1.60733488 1 1 11693 13.3 0 +ID71 0 0.15274085 0.616607437 2 11693 14.9 13 +ID72 0 4.745186863 0.427826338 3 17578 14.9 13 +ID73 0 0.005658029 1 1 13289 17.4 15 +ID74 20.10097018 1.120479882 1 1 17329 9 13 +ID75 0 1.833512535 1 1 11693 9 13 +ID76 0 1.964892412 1 1 3760 14.9 13 +ID77 0 1.024508972 1 1 17598 13.3 0 +ID78 13.02008677 2.446206116 0 2 17601 17.8 13 +ID79 0 5.849408836 0.506514891 2 17601 12.3 11 +ID80 0 0.82602546 1 1 17607 20 17 +ID81 0 6.632335465 1 1 13733 17.8 13 +ID82 9.774097667 2.083923212 0.147137616 2 17329 17.4 14 +ID83 0 0.843046283 1 1 17598 11.7 15 +ID84 0 17.16743054 1 1 3760 9 13 +ID85 0 2.347816248 1 1 17607 13.3 0 +ID86 0 2.089467724 1 1 17608 8.1 13 +ID87 14.70497236 1.782279054 1 1 17329 8.1 13 +ID88 0 1.828267391 1 1 10250 8.1 13 +ID89 19.54258479 2.829334541 1 1 3760 14.3 30 +ID90 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 dc2dfad1627b test-data/geographic_coverage.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/geographic_coverage.tabular Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b test-data/geographic_coverage.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/geographic_coverage.txt Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b test-data/keywords.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keywords.tabular Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b test-data/keywords.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keywords.txt Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b test-data/metadataoutput.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/metadataoutput.xml Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b test-data/personnel.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/personnel.tabular Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b test-data/personnel.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/personnel.txt Sat Dec 02 01:49:10 2023 +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 dc2dfad1627b test-data/taxa.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/taxa.tsv Sat Dec 02 01:49:10 2023 +0000 @@ -0,0 +1,5 @@ +taxa site +Achelia_sp 1 +Achelia_spicata 2 +Achelia_suflata 2 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