cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2kgbks
title: Occurrence Download

description: A dataset containing 143 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.decimallatitude &gt;= -23.90297
 AND occurrence.decimallatitude &lt;= -23.87857
 AND (occurrence.decimallongitude &gt;= 29.73274 AND occurrence.decimallongitude &lt;= 29.75093)
 AND GBIF_WITHIN('POLYGON ((29.73274 -23.88095, 29.73369 -23.89851, 29.74917 -23.90297, 29.74964 -23.89827, 29.75093 -23.88397, 29.74764 -23.88467, 29.74729 -23.87857, 29.74386 -23.87992, 29.73274 -23.87992, 29.73274 -23.88095))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND (CAST(occurrence.taxonkey AS INTEGER) = 212 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 212 OR CAST(occurrence.kingdomkey AS INTEGER) = 212 OR CAST(occurrence.phylumkey AS INTEGER) = 212 OR CAST(occurrence.classkey AS INTEGER) = 212 OR CAST(occurrence.orderkey AS INTEGER) = 212 OR CAST(occurrence.familykey AS INTEGER) = 212 OR CAST(occurrence.genuskey AS INTEGER) = 212 OR CAST(occurrence.specieskey AS INTEGER) = 212)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 143 records were derived from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023144-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17559631
title: Underwater images collected by  Scuba diving in Le-Morne, Mauritius - 2019-07-07

description: This dataset was collected by  Scuba diving in Le-Morne, Mauritius - 2019-07-07. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.15468/dl.ehqr89
title: Occurrence Download

description: A dataset containing 12181 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation)",
 "Geometry POLYGON((-180 -40,180 -40,180 55,-180 55,-180 -40))",
 "OccurrenceStatus is Present",
 "TaxonKey is Solanum tuberosum"
 ]
}

 The dataset includes 12181 records from 243 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023161-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yz2d4z
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Phoebe lichuanensis"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023162-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17559653
title: Data PLS Thematic Summary of Qualitative Data Analysis from ATLAS.ti

description: The file ATLAS.ti_Transcript_Quotes5.xlsx contains textual quotations extracted from interview transcripts or original documents analyzed through ATLAS.ti for qualitative research purposes.It records coded quotations linked to their corresponding codes and themes, representing key trends and insights derived from the data.This dataset allows researchers to trace data sources and present direct textual evidence supporting the analytical findings.
DOI: 10.6069/jgx0-br65
title: Age- and cell type- specific effects of histone variant H2BE in the brain

description: Transcription is regulated in part through histone proteins that bind DNA and control access to genes. Histones can be replaced with variant forms that are particularly critical in the brain and accumulate throughout lifespan. Recent findings defined the first broadly expressed H2B variant, H2BE, and demonstrated that it regulates chromatin structure, neuronal transcription, and mouse behavior. However, the role of H2BE in other cell types and its role throughout lifespan remain unknown. Here, we discovered that H2BE is enriched in astrocytes in the brain and accumulates with age in both astrocytes and neurons. Using single-nucleus RNA-sequencing, we demonstrate that H2BE promotes synaptic gene expression in neurons and astrocytes in young brains. Using microelectrode array recordings, we discovered that H2BE is required in both cell types for proper synaptic function. In aging brains, loss of H2BE similarly affects synaptic genes in neurons but also dampens some age-related transcriptional changes in both astrocytes and neurons. Lastly, behavioral testing demonstrates that H2BE loss disrupts long-term memory but improves working memory in aging mice. Together, these data provide novel links between histone variants, aging-related gene expression changes in both astrocytes and neurons, and memory.
DOI: 10.15468/dl.y3j9td
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((30.12382 40.53018,32.18672 40.53018,32.18672 41.35606,30.12382 41.35606,30.12382 40.53018))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fagus orientalis"
 ]
}

 The dataset includes 15 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023164-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7wdymt
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((30.63019 41.00581,31.05138 41.00581,31.05138 41.17443,30.63019 41.17443,30.63019 41.00581))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pancratium maritimum"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023165-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yfr2t2
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Trichopodus trichopterus, Trichogaster trichopterus)",
 "Country is Singapore",
 "OccurrenceStatus is Present",
 "EventDate is 1800,2026"
 ]
}

 The dataset includes 40 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023166-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17559230
title: Future projections of European maize yields using AquaCrop with an adaptive growing season

description: This repository provides the resources related to the paper "Future projections of European maize yields using AquaCrop with an adaptive growing season" accepeted for publication in the European journal of Agronomy.

Regarding the setup and the input, the reader can refer to the Zenodo repository with doi: 10.5281/zenodo.6760976, and to the publication in the European Journal of Agronomy.

The output of the yield simulations is provided in this repository and is organized as follows:



ISIMIP3a (re-analysis, 2011-2016): Ya.nc and Yw.nc (actual and water-limited potential yield for 6 years)

ISIMIP3b (projections, 2021-2099): Ya, Yp, Yw directories, each with 20 netcdf files *scenario*_*GCM_name*_*yield_type*.nc



scenario: historical, ssp126, ssp370, ssp585

GCM_name: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL

yield_type: Ya, Yp, Yw




The netcdf are provided on the ISIMIP lat-lon grid having a resolution of 0.5° for the study area [34.75°N to 59.75°N latitude and -10.75°E to 41.25°E longitude]. Note that for ISMIP3b, only the years from 2030 through 2059 were used for the analysis presented in the publication.

For any questions related to this repository, please contact the louise.busschaert@kuleuven.be.
DOI: 10.5281/zenodo.17559229
title: Future projections of European maize yields using AquaCrop with an adaptive growing season

description: This repository provides the resources related to the paper "Future projections of European maize yields using AquaCrop with an adaptive growing season" accepeted for publication in the European journal of Agronomy.

Regarding the setup and the input, the reader can refer to the Zenodo repository with doi: 10.5281/zenodo.6760976, and to the publication in the European Journal of Agronomy.

The output of the yield simulations is provided in this repository and is organized as follows:



ISIMIP3a (re-analysis, 2011-2016): Ya.nc and Yw.nc (actual and water-limited potential yield for 6 years)

ISIMIP3b (projections, 2021-2099): Ya, Yp, Yw directories, each with 20 netcdf files *scenario*_*GCM_name*_*yield_type*.nc



scenario: historical, ssp126, ssp370, ssp585

GCM_name: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL

yield_type: Ya, Yp, Yw




The netcdf are provided on the ISIMIP lat-lon grid having a resolution of 0.5° for the study area [34.75°N to 59.75°N latitude and -10.75°E to 41.25°E longitude]. Note that for ISMIP3b, only the years from 2030 through 2059 were used for the analysis presented in the publication.

For any questions related to this repository, please contact the louise.busschaert@kuleuven.be.
DOI: 10.15468/dl.bubjdf
title: Occurrence Download

description: A dataset containing 65 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Phoebe macrocarpa"
 ]
}

 The dataset includes 65 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023167-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17559674
title: Classified Transcript Quotations from Qualitative Analysis using ATLAS.ti

description: The file ATLAS.ti_Transcript_Quotes_Classified6.xlsx contains a set of classified quotations extracted from interview transcripts or original documents, analyzed using ATLAS.ti within a qualitative research framework.It presents organized quotations according to analytical categories or themes, with corresponding codes and analytical memos that highlight the main patterns derived from the data.This dataset supports the research findings with direct textual evidence and demonstrates how field data were categorized and interpreted through qualitative coding
DOI: 10.5281/zenodo.17559630
title: Underwater images collected by  Scuba diving in Le-Morne, Mauritius - 2019-07-07

description: This dataset was collected by  Scuba diving in Le-Morne, Mauritius - 2019-07-07. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.5281/zenodo.17559649
title: Underwater images collected by  Scuba diving in Le-Morne, Mauritius - 2019-07-07

description: This dataset was collected by  Scuba diving in Le-Morne, Mauritius - 2019-07-07. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.15468/dl.3vq64x
title: Occurrence Download

description: A dataset containing 1113981 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Apis mellifera"
 ]
}

 The dataset includes 1113981 records from 1771 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023138-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/cc8x0sy
title: CCDC 265260: Experimental Crystal Structure Determination

description: Related Article: J.Langer, H.Gorls, G.Gillies, D.Walther|2005|Z.Anorg.Allg.Chem.|631|2719|doi:10.1002/zaac.200500114
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc8x0tz
title: CCDC 265261: Experimental Crystal Structure Determination

description: Related Article: J.Langer, H.Gorls, G.Gillies, D.Walther|2005|Z.Anorg.Allg.Chem.|631|2719|doi:10.1002/zaac.200500114
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc8x0v0
title: CCDC 265262: Experimental Crystal Structure Determination

description: Related Article: J.Langer, H.Gorls, G.Gillies, D.Walther|2005|Z.Anorg.Allg.Chem.|631|2719|doi:10.1002/zaac.200500114
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc8x0w1
title: CCDC 265263: Experimental Crystal Structure Determination

description: Related Article: J.Langer, H.Gorls, G.Gillies, D.Walther|2005|Z.Anorg.Allg.Chem.|631|2719|doi:10.1002/zaac.200500114
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.