cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.b8wkjd
title: Occurrence Download

description: A dataset containing 69 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, China, Pakistan, Bhutan, Nepal, Afghanistan)",
 "Geometry POLYGON((96.41044 29.88817,94.41053 30.71851,89.92548 30.12986,83.71418 31.71528,82.08257 32.21565,79.10668 36.25718,75.17743 37.17404,71.49864 36.57423,73.25909 34.75717,73.23731 34.62027,73.32599 34.26091,73.80358 34.1349,73.88759 33.93422,73.98219 33.59626,74.0662 33.39558,74.01019 33.15911,74.40845 32.95999,74.80359 32.99732,74.74758 32.90243,75.14273 32.50417,75.60943 32.41083,75.73856 31.93323,76.11192 31.3623,76.53973 30.9796,76.90781 30.74946,77.54408 30.33098,78.14387 30.30538,78.40833 30.10626,78.97149 29.37975,79.06639 29.34242,79.24063 29.24285,79.55643 28.81815,80.13798 28.33372,80.51691 28.06255,80.65424 28.22768,80.97929 28.39108,81.24698 28.0469,81.3252 27.89046,81.39299 27.90958,81.43471 27.99823,82.74541 27.53594,83.08969 27.61188,83.68713 27.5815,83.97573 27.32329,84.16965 27.23946,84.57823 27.29291,85.33769 27.00432,85.99589 26.89799,86.46675 26.66509,86.79079 26.58408,87.21102 26.67522,87.80016 26.63934,88.14769 26.5132,88.71981 26.61952,89.38307 26.49295,89.92989 26.12334,90.15266 26.38156,90.19823 26.56889,91.04882 26.62459,91.85067 26.84805,92.87847 26.7316,93.74425 26.99995,97.72177 28.56164,96.41044 29.88817))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Phyllanthus emblica"
 ]
}

 The dataset includes 69 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006731-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14822807
title: Improving the visibility of waterway events influencing traffic along the Rhine, Main, and Danube

description: This novel dataset is introduced in the paper titled "Improving the visibility of waterway events influencing traffic along the Rhine, Main, and Danube." It contains Notices to Skippers (NtS) from Europe’s navigable inland waterways. Each NtS includes crucial details on infrastructure and traffic conditions and comprehensive information about all registered events on the waterways from January 2023. This information encompasses the start and end times of events, event types, descriptions, geographical coordinates, and identification codes for affected objects such as locks, berths, bridges, and navigational fairways.

The source of the NtS data is the EuRIS portal, which integrates River Information System (RIS) services to improve traffic and transport management in inland navigation. The portal aggregates and makes available data from inland waterway management organizations across 13 European countries. The NtS messages were systematically retrieved from the EuRIS API endpoint, which provides detailed information for each active NtS message, including the original XML message.
DOI: 10.5281/zenodo.14822808
title: Improving the visibility of waterway events influencing traffic along the Rhine, Main, and Danube

description: This novel dataset is introduced in the paper titled "Improving the visibility of waterway events influencing traffic along the Rhine, Main, and Danube." It contains Notices to Skippers (NtS) from Europe’s navigable inland waterways. Each NtS includes crucial details on infrastructure and traffic conditions and comprehensive information about all registered events on the waterways from January 2023. This information encompasses the start and end times of events, event types, descriptions, geographical coordinates, and identification codes for affected objects such as locks, berths, bridges, and navigational fairways.

The source of the NtS data is the EuRIS portal, which integrates River Information System (RIS) services to improve traffic and transport management in inland navigation. The portal aggregates and makes available data from inland waterway management organizations across 13 European countries. The NtS messages were systematically retrieved from the EuRIS API endpoint, which provides detailed information for each active NtS message, including the original XML message.
DOI: 10.15468/dl.ze463m
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Korea, Republic of",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Caccobius unicornis, Caccobius sordidus)"
 ]
}

 The dataset includes 40 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006732-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16967252
title: Allelopathic Trade-offs of Rye/Wheat Residues versus Purified Benzoxazolinone (BOA) in Cover Crop–Cotton Systems: Greenhouse Evidence and Management Implications
DOI: 10.15468/dl.t96dub
title: Occurrence Download

description: A dataset containing 234 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prionobutis microps"
 ]
}

 The dataset includes 234 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006733-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.67nbwj
title: Occurrence Download

description: A dataset containing 7168 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera tigris"
 ]
}

 The dataset includes 7168 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006734-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wzszuv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Chiloglanis carnosus, Chiloglanis congicus, Chiloglanis deckenii, Chiloglanis devosi, Chiloglanis dialloi, Chiloglanis disneyi, Chiloglanis elisabethianus, Chiloglanis emarginatus, Chiloglanis fasciatus, Chiloglanis harbinger, Chiloglanis kabaensis, Chiloglanis kalambo, Chiloglanis kerioensis, Chiloglanis kolente, Chiloglanis lamottei, Chiloglanis loffabrevum, Chiloglanis longibarbis, Chiloglanis lufirae, Chiloglanis lukugae, Chiloglanis macropterus, Chiloglanis marlieri, Chiloglanis mbozi, Chiloglanis microps, Chiloglanis modjensis, Chiloglanis mongoensis, Chiloglanis neumanni, Chiloglanis niger, Chiloglanis niloticus, Chiloglanis normani, Chiloglanis nzerekore, Chiloglanis occidentalis, Chiloglanis paratus, Chiloglanis pezoldi, Chiloglanis pojeri, Chiloglanis polyodon, Chiloglanis polypogon, Chiloglanis pretoriae, Chiloglanis reticulatus, Chiloglanis rukwaensis, Chiloglanis ruziziensis, Chiloglanis sanagaensis, Chiloglanis sardinhai, Chiloglanis somereni, Chiloglanis swierstrai, Chiloglanis trilobatus, Chiloglanis tweddlei, Chiloglanis voltae, Chiloglanis waterloti, Chilotilapia euchilus, Chilotilapia rhoadesii, Chindongo ater, Chindongo bellicosus, Chindongo cyaneus, Chindongo demasoni, Chindongo flavus, Chindongo heteropictus, Maylandia heteropicta, Chindongo longior, Chindongo minutus, Chindongo saulosi, Chromidotilapia cavalliensis, Chromidotilapia elongata, Chromidotilapia guntheri, Chromidotilapia kingsleyae, Chromidotilapia linkei, Chromidotilapia mamonekenei, Chromidotilapia melaniae, Chromidotilapia mrac, Chromidotilapia nana, Chromidotilapia regani, Chromidotilapia schoutedeni, Chrysichthys aluuensis, Chrysichthys ansorgii, Chrysichthys auratus, Chrysichthys bocagii, Chrysichthys brachynema, Chrysichthys brevibarbis, Chrysichthys cranchii, Chrysichthys dageti, Chrysichthys delhezi, Chrysichthys dendrophorus, Chrysichthys duttoni, Chrysichthys furcatus, Chrysichthys nigrodigitatus, Chrysichthys grandis, Chrysichthys graueri, Chrysichthys habereri, Chrysichthys helicophagus, Chrysichthys hildae, Chrysichthys johnelsi, Chrysichthys laticeps, Chrysichthys levequei, Chrysichthys longibarbis, Chrysichthys longidorsalis, Chrysichthys longipinnis, Chrysichthys mabusi, Chrysichthys macropterus, Chrysichthys maurus, Chrysichthys nyongensis)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((7.4788 0.8754,7.8749 1.2759,7.9249 1.8712,7.744 2.0599,7.0894 2.0599,6.9519 1.9585,6.7829 1.3956,6.9945 0.9362,7.4788 0.8754))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006735-250827131500795/datasets/export for details.
DOI: 10.21954/ou.rd.30005812
title: REDEFINE (Hungary): Interview with Trade Union specialist

description: This document forms part of the REDEFINE research project (ERC Grant No. 885475), funded by the European Research Council. REDEFINE focuses on the dynamics and effects of China’s large-scale infrastructure investments in Europe, with a focus on Germany, Greece, Hungary, and the UK. Through multi-sited fieldwork and interdisciplinary analysis, the project seeks to understand the evolving relations between China and Europe and to assess what this means for politics, economic growth and development more broadly.This anonymised transcript presents a semi-structured interview conducted with a Trade Union specialist. The conversation contributes to REDEFINE’s case study of Chinese investment in Budapest-Belgrade Railway upgrade. All identifying details have been removed, and informed consent for participation and data use was obtained.
DOI: 10.21954/ou.rd.30005812.v1
title: REDEFINE (Hungary): Interview with Trade Union specialist

description: This document forms part of the REDEFINE research project (ERC Grant No. 885475), funded by the European Research Council. REDEFINE focuses on the dynamics and effects of China’s large-scale infrastructure investments in Europe, with a focus on Germany, Greece, Hungary, and the UK. Through multi-sited fieldwork and interdisciplinary analysis, the project seeks to understand the evolving relations between China and Europe and to assess what this means for politics, economic growth and development more broadly.This anonymised transcript presents a semi-structured interview conducted with a Trade Union specialist. The conversation contributes to REDEFINE’s case study of Chinese investment in Budapest-Belgrade Railway upgrade. All identifying details have been removed, and informed consent for participation and data use was obtained.
DOI: 10.5281/zenodo.15388336
title: Pan-Arctic weather data set from publicly available in situ measurements from 1990-2023

description: These datasets are collected from publicly available in situ measurements of 36 ground and surface climatic variables from 16 different data sources, focusing on the period 1990-2023, but with some sources providing data much further back in time (early 1900s). 
The variables collected are: Air temperature, Snow depth, Precipitation, Longwave Incoming radiation, Longwave Outgoing Radiation, Shortwave Incoming Radiation, Shortwave outgoing radiation, Relative Humidity, Surface temperature (below the snow), Soil temperature at the depths 5, 10, 15, 20, 25, 30, 40, 50, 60, 65, 70, 75, 80, 90, 110, 140, 150 and 200 cm and Soil moisture (vol%) at depths 5, 10, 15, 20, 25, 30, 40, 50 and 80 cm. 
The data set consists of 719 unique locations in total, with data coverage varying between sites and over time. It is available in two versions: In the first 'raw' version, data was restructured and reformatted from each original source into a common format, but was not tested for quality. In the second, Quality Checked version, the data set has additionally been run through a five-module Quality Check involving 1) removing common error values, 2) evaluating physically impossible values, 3) outlier-detection and evaluation, 4) unit conversions and 5) evaluation of likely instrument and/or calibration artifacts. The code for import, normalization and quality check with optional modules is made available in addition to the data and metadata.
When using the data, the original data source should always be cited (found in readme and metadata document). Abbreviations used: AWI: Alfred Wegener Institute, Potsdam; CALM: Circumpolar Active Layer Monitoring through Arctic Data Center; CEN/Nordicana: Center for Northern Studies, Canada; FMI: Finnish Meteorological Institute; GEM: Greenland Ecosystem Monitoring; IARC: International Arctic Research Center, Fairbanks, US; NGEE: Next Generation Ecosystem Experiments, Fairbanks, US; NMI: Norwegian Meteorological Institute; SILA/Nordicana: SILA Network at Center for Northern Studies, Canada; SMHI: Swedish Meteorological and Hydrological Institute; WMO: World Meteorological Organization via Meteostat.net; DMI: Danish Meteorological Institut; NOAA: National Oceanic and Atmospheric Administration Global Monitoring Laboratory.
DOI: 10.5281/zenodo.15388335
title: Pan-Arctic weather data set from publicly available in situ measurements from 1990-2023

description: These datasets are collected from publicly available in situ measurements of 36 ground and surface climatic variables from 16 different data sources, focusing on the period 1990-2023, but with some sources providing data much further back in time (early 1900s). 
The variables collected are: Air temperature, Snow depth, Precipitation, Longwave Incoming radiation, Longwave Outgoing Radiation, Shortwave Incoming Radiation, Shortwave outgoing radiation, Relative Humidity, Surface temperature (below the snow), Soil temperature at the depths 5, 10, 15, 20, 25, 30, 40, 50, 60, 65, 70, 75, 80, 90, 110, 140, 150 and 200 cm and Soil moisture (vol%) at depths 5, 10, 15, 20, 25, 30, 40, 50 and 80 cm. 
The data set consists of 719 unique locations in total, with data coverage varying between sites and over time. It is available in two versions: In the first 'raw' version, data was restructured and reformatted from each original source into a common format, but was not tested for quality. In the second, Quality Checked version, the data set has additionally been run through a five-module Quality Check involving 1) removing common error values, 2) evaluating physically impossible values, 3) outlier-detection and evaluation, 4) unit conversions and 5) evaluation of likely instrument and/or calibration artifacts. The code for import, normalization and quality check with optional modules is made available in addition to the data and metadata.
When using the data, the original data source should always be cited (found in readme and metadata document). Abbreviations used: AWI: Alfred Wegener Institute, Potsdam; CALM: Circumpolar Active Layer Monitoring through Arctic Data Center; CEN/Nordicana: Center for Northern Studies, Canada; FMI: Finnish Meteorological Institute; GEM: Greenland Ecosystem Monitoring; IARC: International Arctic Research Center, Fairbanks, US; NGEE: Next Generation Ecosystem Experiments, Fairbanks, US; NMI: Norwegian Meteorological Institute; SILA/Nordicana: SILA Network at Center for Northern Studies, Canada; SMHI: Swedish Meteorological and Hydrological Institute; WMO: World Meteorological Organization via Meteostat.net; DMI: Danish Meteorological Institut; NOAA: National Oceanic and Atmospheric Administration Global Monitoring Laboratory.
DOI: 10.48573/e8b2-hz38
title: CHILL: Questionnaire Dataset (m-Path app)
DOI: 10.48573/yz74-ap73
title: CHILL: Questionnaire Dataset (m-Path app)
DOI: 10.15468/dl.2rauyv
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, China, Pakistan, Bhutan, Nepal, Afghanistan)",
 "Geometry POLYGON((96.41044 29.88817,94.41053 30.71851,89.92548 30.12986,83.71418 31.71528,82.08257 32.21565,79.10668 36.25718,75.17743 37.17404,71.49864 36.57423,73.25909 34.75717,73.23731 34.62027,73.32599 34.26091,73.80358 34.1349,73.88759 33.93422,73.98219 33.59626,74.0662 33.39558,74.01019 33.15911,74.40845 32.95999,74.80359 32.99732,74.74758 32.90243,75.14273 32.50417,75.60943 32.41083,75.73856 31.93323,76.11192 31.3623,76.53973 30.9796,76.90781 30.74946,77.54408 30.33098,78.14387 30.30538,78.40833 30.10626,78.97149 29.37975,79.06639 29.34242,79.24063 29.24285,79.55643 28.81815,80.13798 28.33372,80.51691 28.06255,80.65424 28.22768,80.97929 28.39108,81.24698 28.0469,81.3252 27.89046,81.39299 27.90958,81.43471 27.99823,82.74541 27.53594,83.08969 27.61188,83.68713 27.5815,83.97573 27.32329,84.16965 27.23946,84.57823 27.29291,85.33769 27.00432,85.99589 26.89799,86.46675 26.66509,86.79079 26.58408,87.21102 26.67522,87.80016 26.63934,88.14769 26.5132,88.71981 26.61952,89.38307 26.49295,89.92989 26.12334,90.15266 26.38156,90.19823 26.56889,91.04882 26.62459,91.85067 26.84805,92.87847 26.7316,93.74425 26.99995,97.72177 28.56164,96.41044 29.88817))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Hedychium spicatum"
 ]
}

 The dataset includes 88 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006739-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48573/4h0x-ww09
title: CHILL: Intervention Dataset (m-Path app)
DOI: 10.48573/bzfv-np71
title: CHILL: Mobile Sensing Dataset (m-Path app)
DOI: 10.15468/dl.usjxme
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Chiloglanis carnosus, Chiloglanis congicus, Chiloglanis deckenii, Chiloglanis devosi, Chiloglanis dialloi, Chiloglanis disneyi, Chiloglanis elisabethianus, Chiloglanis emarginatus, Chiloglanis fasciatus, Chiloglanis harbinger, Chiloglanis kabaensis, Chiloglanis kalambo, Chiloglanis kerioensis, Chiloglanis kolente, Chiloglanis lamottei, Chiloglanis loffabrevum, Chiloglanis longibarbis, Chiloglanis lufirae, Chiloglanis lukugae, Chiloglanis macropterus, Chiloglanis marlieri, Chiloglanis mbozi, Chiloglanis microps, Chiloglanis modjensis, Chiloglanis mongoensis, Chiloglanis neumanni, Chiloglanis niger, Chiloglanis niloticus, Chiloglanis normani, Chiloglanis nzerekore, Chiloglanis occidentalis, Chiloglanis paratus, Chiloglanis pezoldi, Chiloglanis pojeri, Chiloglanis polyodon, Chiloglanis polypogon, Chiloglanis pretoriae, Chiloglanis reticulatus, Chiloglanis rukwaensis, Chiloglanis ruziziensis, Chiloglanis sanagaensis, Chiloglanis sardinhai, Chiloglanis somereni, Chiloglanis swierstrai, Chiloglanis trilobatus, Chiloglanis tweddlei, Chiloglanis voltae, Chiloglanis waterloti, Chilotilapia euchilus, Chilotilapia rhoadesii, Chindongo ater, Chindongo bellicosus, Chindongo cyaneus, Chindongo demasoni, Chindongo flavus, Chindongo heteropictus, Maylandia heteropicta, Chindongo longior, Chindongo minutus, Chindongo saulosi, Chromidotilapia cavalliensis, Chromidotilapia elongata, Chromidotilapia guntheri, Chromidotilapia kingsleyae, Chromidotilapia linkei, Chromidotilapia mamonekenei, Chromidotilapia melaniae, Chromidotilapia mrac, Chromidotilapia nana, Chromidotilapia regani, Chromidotilapia schoutedeni, Chrysichthys aluuensis, Chrysichthys ansorgii, Chrysichthys auratus, Chrysichthys bocagii, Chrysichthys brachynema, Chrysichthys brevibarbis, Chrysichthys cranchii, Chrysichthys dageti, Chrysichthys delhezi, Chrysichthys dendrophorus, Chrysichthys duttoni, Chrysichthys furcatus, Chrysichthys nigrodigitatus, Chrysichthys grandis, Chrysichthys graueri, Chrysichthys habereri, Chrysichthys helicophagus, Chrysichthys hildae, Chrysichthys johnelsi, Chrysichthys laticeps, Chrysichthys levequei, Chrysichthys longibarbis, Chrysichthys longidorsalis, Chrysichthys longipinnis, Chrysichthys mabusi, Chrysichthys macropterus, Chrysichthys maurus, Chrysichthys nyongensis)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((7.2484 2.1771,7.0894 2.0599,7.744 2.0599,7.6712 2.1357,7.2484 2.1771))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006740-250827131500795/datasets/export for details.
DOI: 10.34740/kaggle/dsv/12904182
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.48573/vrq4-5y46
title: CHILL: Online Survey Dataset (formR software)
DOI: 10.21942/uva.30009436
title: Raw data downloaded from Qualtrics

description: Data collected on management control systems changes following the switch to working from home in the corona period.
DOI: 10.21942/uva.30009436.v1
title: Raw data downloaded from Qualtrics

description: Data collected on management control systems changes following the switch to working from home in the corona period.
DOI: 10.15468/dl.3r3zp8
title: Occurrence Download

description: A dataset containing 0 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 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.datasetkey = '7bfbd7bb-fdd8-44ff-ab28-7f92c2d55ee3'
 AND occurrence.hasgeospatialissues = FALSE
 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 0 records were derived from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006721-250827131500795/datasets/export for details.
DOI: 10.5284/1135480
title: Digital Archive from an Archaeological Watching Brief at the Former Gales Yard, Melbourne, Derbyshire, May 2024

description: This collection contains the site records, photographs and GIS data produced during a watching brief at the former Gales Yard, Melbourne, Derbyshire. Works were conducted by Archaeological Research Services Ltd in May 2024. The trenching revealed a series of buried Victorian and later deposits across the yard, which likely had removed any underlying archaeological features.
DOI: 10.57745/5htcwh
title: T1235_T290_combined_scores.tab

description: :unav
DOI: 10.57745/8sm5gb
title: T290_CAPRI_assessment.tab

description: :unav
DOI: 10.57745/daoltg
title: MassiveFold_CASP16_Abstract.pdf

description: :unav
DOI: 10.57745/dvvtzw
title: README.txt

description: :unav
DOI: 10.57745/ggzbca
title: T1235_T290_MassiveFold.tar.gz

description: :unav
DOI: 10.57745/grskos
title: T290_CAPRI_assessment.tab

description: :unav