cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.xy664x
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chindongo elongatus (Fryer, 1956)",
 "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 ((28.1382 -6.9401,19.1382 -6.9401,19.1382 -15.9401,28.1382 -15.9401,28.1382 -6.9401))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060006-250525065834625/datasets/export for details.
DOI: 10.15468/dl.kns9t2
title: Occurrence Download

description: A dataset containing 1108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Laudakia vulgaris (Sonnini & Latreille, 1802) Karameta, Lymberakis, Grillitsch, Ilgaz, Avci, Kumlutaş, Candan, Wagner, Sfenthourakis, Pafilis & Poulakakis, 2022"
 ]
}

 The dataset includes 1108 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060007-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28582085
title: Data for: Breeding seasonality in Charadriiformes on the Andean altiplano is influenced by rain and possibly flood risk

description: Here, we combined records of citizen naturalistic databases, published literature and our own field observations to provide an up-to-date account on timing of breeding for 11 species of ground-nesting Charadriiformes breeding above 3000 m on the Andean Altiplano-Puna plateau.
DOI: 10.6084/m9.figshare.28582085.v1
title: Data for: Breeding seasonality in Charadriiformes on the Andean altiplano is influenced by rain and possibly flood risk

description: Here, we combined records of citizen naturalistic databases, published literature and our own field observations to provide an up-to-date account on timing of breeding for 11 species of ground-nesting Charadriiformes breeding above 3000 m on the Andean Altiplano-Puna plateau.
DOI: 10.15468/dl.um3p3h
title: Occurrence Download

description: A dataset containing 85 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alburnus filippii Kessler, 1877",
 "HasCoordinate is true"
 ]
}

 The dataset includes 85 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060008-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n3fbss
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Klebsormidium P.C.Silva, K.Mattox & W.Blackwell, 1972",
 "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 ((25.0214 -34.0501,25.5395 -34.1098,25.5395 -32.9007,20.509 -32.9007,20.509 -34.5676,20.524 -34.555,20.8859 -34.5419,20.9606 -34.4398,21.2995 -34.5131,21.5296 -34.4338,21.8696 -34.4439,22.0323 -34.2835,22.221 -34.2428,22.2566 -34.1304,22.5993 -34.0741,22.9652 -34.1674,23.4069 -34.1858,23.4955 -34.0792,23.6056 -34.0563,24.8471 -34.2877,24.9447 -34.2397,25.0214 -34.0501))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060009-250525065834625/datasets/export for details.
DOI: 10.15468/dl.4sqbe8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chindongo elongatus (Fryer, 1956)",
 "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 ((27.2291 -34.019,27.3628 -33.9401,19.1382 -33.9401,19.1382 -35.0665,20.0413 -35.3323,20.7102 -34.9578,21.9201 -34.8682,22.6598 -34.5181,23.406 -34.611,23.7098 -34.5029,24.8326 -34.7174,25.2572 -34.5102,25.849 -34.5138,26.1077 -34.3123,27.2291 -34.019))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060010-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15705539
title: Coastal Vulnerability Monitoring Dataset

description: The Coastal Vulnerability Monitoring Dataset comprises a rich, multi-dimensional collection of spatiotemporal records gathered from smart environmental monitoring systems deployed across three critical coastal regions between January 2019 and March 2025. This dataset contains over 1.7 million timestamped entries recorded at 15-minute intervals, covering diverse parameters relevant to coastal vulnerability assessment and real-time hazard forecasting.

Feature Categories





Oceanographic VariablesCaptures marine dynamics including sea surface temperature, wave height, tidal range, current speed/direction, salinity, turbidity, chlorophyll concentration, sea level anomalies, and ocean heat content.




Atmospheric IndicatorsIncludes surface-level air temperature, humidity, wind speed and direction, precipitation rate, barometric pressure, storm surge levels, UV index, and solar irradiance. These parameters help model weather-induced coastal hazards.




Geospatial and Topographical ContextFeatures such as GPS coordinates (latitude, longitude), elevation, slope angle, distance from shoreline, NDVI (vegetation index), shoreline change rate, erosion index, and categorical attributes like land cover type and soil type are incorporated to represent regional physical and ecological characteristics.




Infrastructure and Exposure AttributesThis includes infrastructure type (e.g., port, seawall), population density, asset exposure index, drainage capacity, access to emergency response, number of critical installations, and a categorical flood defense grade. These features are crucial for modeling localized vulnerability and resilience.




IoT Metadata and Device HealthMetadata such as device ID, sensor type (e.g., buoy, drone, satellite), calibration status, battery level, data quality score, signal strength, latency, and privacy flags are provided for operational diagnostics and federated learning integration.




Temporal and Federated MetadataIncludes day-of-week, sensor cluster ID, rolling alert counts over 30 days, and scheduled maintenance events—enabling temporal pattern analysis and distributed system simulation.



Target Labels for Learning and Prediction





Coastal Vulnerability Level (Multi-Class: Low, Moderate, High, Severe)




Flood Risk Score (Continuous: 0.0–1.0)




Infrastructure Damage Index (Range: 0–100)




Erosion Threat Class (Multi-Class: Stable, Moderate, Severe)
DOI: 10.5281/zenodo.15705540
title: Coastal Vulnerability Monitoring Dataset

description: The Coastal Vulnerability Monitoring Dataset comprises a rich, multi-dimensional collection of spatiotemporal records gathered from smart environmental monitoring systems deployed across three critical coastal regions between January 2019 and March 2025. This dataset contains over 1.7 million timestamped entries recorded at 15-minute intervals, covering diverse parameters relevant to coastal vulnerability assessment and real-time hazard forecasting.

Feature Categories





Oceanographic VariablesCaptures marine dynamics including sea surface temperature, wave height, tidal range, current speed/direction, salinity, turbidity, chlorophyll concentration, sea level anomalies, and ocean heat content.




Atmospheric IndicatorsIncludes surface-level air temperature, humidity, wind speed and direction, precipitation rate, barometric pressure, storm surge levels, UV index, and solar irradiance. These parameters help model weather-induced coastal hazards.




Geospatial and Topographical ContextFeatures such as GPS coordinates (latitude, longitude), elevation, slope angle, distance from shoreline, NDVI (vegetation index), shoreline change rate, erosion index, and categorical attributes like land cover type and soil type are incorporated to represent regional physical and ecological characteristics.




Infrastructure and Exposure AttributesThis includes infrastructure type (e.g., port, seawall), population density, asset exposure index, drainage capacity, access to emergency response, number of critical installations, and a categorical flood defense grade. These features are crucial for modeling localized vulnerability and resilience.




IoT Metadata and Device HealthMetadata such as device ID, sensor type (e.g., buoy, drone, satellite), calibration status, battery level, data quality score, signal strength, latency, and privacy flags are provided for operational diagnostics and federated learning integration.




Temporal and Federated MetadataIncludes day-of-week, sensor cluster ID, rolling alert counts over 30 days, and scheduled maintenance events—enabling temporal pattern analysis and distributed system simulation.



Target Labels for Learning and Prediction





Coastal Vulnerability Level (Multi-Class: Low, Moderate, High, Severe)




Flood Risk Score (Continuous: 0.0–1.0)




Infrastructure Damage Index (Range: 0–100)




Erosion Threat Class (Multi-Class: Stable, Moderate, Severe)
DOI: 10.5281/zenodo.15705315
title: Bitcoin Market and Mining Dataset with Energy and Efficiency Metrics

description: This dataset was manually constructed by aggregating and engineering multiple categories of Bitcoin-related data from different sources. It includes:

- Market data (e.g., Open, Close, Volume, Fees, Transactions) collected from [Mempool](https://mempool.space/it/)- Energy data (e.g., estimated power consumption, efficiency metrics) retrieved via the [Electricity Maps API](https://portal.electricitymaps.com/docs/getting-started#geolocation)- Technical financial indicators (e.g., EMA, MACD, RSI, VWAP, TRIX, Bollinger Bands Width) computed using the [FinTa](https://github.com/peerchemist/finta) Python library- Derived metrics (e.g., momentum, volatility, normalization functions) calculated with custom Python functions

The dataset is structured with one row per day and includes both raw values and engineered features. It offers a detailed overview of Bitcoin's economic, market, and energy dimensions.

This dataset is suitable for use in machine learning models, time series forecasting, and the study of relationships between Bitcoin market behavior and its underlying energy consumption patterns.
DOI: 10.5281/zenodo.15705316
title: Bitcoin Market and Mining Dataset with Energy and Efficiency Metrics

description: This dataset was manually constructed by aggregating and engineering multiple categories of Bitcoin-related data from different sources. It includes:

- Market data (e.g., Open, Close, Volume, Fees, Transactions) collected from [Mempool](https://mempool.space/it/)- Energy data (e.g., estimated power consumption, efficiency metrics) retrieved via the [Electricity Maps API](https://portal.electricitymaps.com/docs/getting-started#geolocation)- Technical financial indicators (e.g., EMA, MACD, RSI, VWAP, TRIX, Bollinger Bands Width) computed using the [FinTa](https://github.com/peerchemist/finta) Python library- Derived metrics (e.g., momentum, volatility, normalization functions) calculated with custom Python functions

The dataset is structured with one row per day and includes both raw values and engineered features. It offers a detailed overview of Bitcoin's economic, market, and energy dimensions.

This dataset is suitable for use in machine learning models, time series forecasting, and the study of relationships between Bitcoin market behavior and its underlying energy consumption patterns.
DOI: 10.15468/dl.4sa3xx
title: Occurrence Download

description: A dataset containing 4898 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Geometry POLYGON((-114.67688 35.83507,-114.6362 35.54633,-114.45272 35.67908,-114.48991 35.80267,-114.45317 35.87029,-114.52168 35.92342,-114.4089 35.93877,-114.36523 35.88731,-114.22629 35.84818,-114.18838 35.98792,-114.3255 35.97572,-114.37445 36.0936,-114.45921 36.02789,-114.56151 36.14127,-114.65777 36.10544,-114.68165 36.07557,-114.7169 36.0725,-114.68787 36.04465,-114.72033 36.02006,-114.71585 35.97179,-114.64754 35.86308,-114.69178 35.83441,-114.67688 35.83507))",
 "Geometry POLYGON((-114.91117 35.67541,-114.81556 35.58171,-114.69735 35.54539,-114.73188 35.69359,-114.91117 35.67541))"
 ]
 },
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 4898 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060011-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.my6fq6
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Klebsormidium P.C.Silva, K.Mattox & W.Blackwell, 1972",
 "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 ((32.064 -28.9416,32.4847 -28.5786,30.5701 -28.5786,30.5701 -30.7329,30.6252 -30.6659,31.4179 -29.4257,31.8178 -29.0299,32.064 -28.9416))"
 ]
}

 The dataset includes 19 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060012-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dxd24n
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chindongo elongatus (Fryer, 1956)",
 "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 ((27.3628 -33.9401,28.1382 -33.4828,28.1382 -24.9401,19.1382 -24.9401,19.1382 -33.9401,27.3628 -33.9401))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060013-250525065834625/datasets/export for details.
DOI: 10.15468/dl.fpxnad
title: Occurrence Download

description: A dataset containing 185170 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Impatiens parviflora DC."
 ]
}

 The dataset includes 185170 records from 589 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059995-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5zrxq2
title: Occurrence Download

description: A dataset containing 2513 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Anolis limifrons Cope, 1862"
 ]
}

 The dataset includes 2513 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060015-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cfnxcj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Klebsormidium P.C.Silva, K.Mattox & W.Blackwell, 1972",
 "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 ((35.5808 -24.13,35.5909 -24.0715,30.5701 -24.0715,30.5701 -28.5786,32.4847 -28.5786,32.55 -28.5222,33.0346 -26.8668,32.9741 -26.7838,33.0415 -26.059,32.9304 -26.0011,32.7994 -26.1281,32.6834 -26.0015,32.813 -25.8728,32.8513 -25.7166,33.3048 -25.3731,35.1489 -24.6699,35.5808 -24.13))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060016-250525065834625/datasets/export for details.
DOI: 10.15468/dl.havxab
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chindongo elongatus (Fryer, 1956)",
 "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 ((29.1409 31.3397,28.1382 31.6271,28.1382 29.0599,35.2765 29.0599,35.4107 29.4968,34.5044 31.7516,34.206 31.8338,33.641 31.6189,33.1338 31.7313,32.728 31.6056,32.0818 31.9994,31.0088 32.1001,30.2163 31.954,29.1409 31.3397))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060018-250525065834625/datasets/export for details.
DOI: 10.15468/dl.y4snkd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Klebsormidium P.C.Silva, K.Mattox & W.Blackwell, 1972",
 "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 ((31.8862 -21.3285,30.5701 -20.3186,30.5701 -24.0715,35.5909 -24.0715,35.6007 -24.0157,35.6007 -23.8125,35.4969 -23.7035,35.6007 -23.165,35.6007 -22.9702,35.227 -22.2482,34.6862 -21.6944,34.013 -21.3095,33.5 -21.1593,32.9675 -21.1092,32.4352 -21.1612,31.8862 -21.3285))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060019-250525065834625/datasets/export for details.
DOI: 10.15468/dl.s8fr7z
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chindongo elongatus (Fryer, 1956)",
 "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 ((37.1382 20.0599,28.1382 20.0599,28.1382 11.0599,37.1382 11.0599,37.1382 20.0599))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060020-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15705556
title: Raw and Meta-data for coherence measurements with trapped Ca-ions

description: The data is the outcome of several scans over the phase of a ramsey-interferometer in an experimental 40Ca+ ion trapping apparatus.

It was used to perform contrast scans in order to retrieve the coherence time of different interrogation schemes.

The datasets scan-parameter, scan-excitation scan-parity can be directly used to access the evaluated measurement data, also all other meta-data of the experiment is present.

For further information on the parameter meanings contact the authors.
DOI: 10.5281/zenodo.15705557
title: Raw and Meta-data for coherence measurements with trapped Ca-ions

description: The data is the outcome of several scans over the phase of a ramsey-interferometer in an experimental 40Ca+ ion trapping apparatus.

It was used to perform contrast scans in order to retrieve the coherence time of different interrogation schemes.

The datasets scan-parameter, scan-excitation scan-parity can be directly used to access the evaluated measurement data, also all other meta-data of the experiment is present.

For further information on the parameter meanings contact the authors.
DOI: 10.15468/dl.hf7rpp
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alburnus carianorum Freyhof, Kaya, Bayçelebi, Geiger & Turan, 2019",
 "HasCoordinate is true"
 ]
}

 The dataset includes 25 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060022-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zm22vb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chindongo elongatus (Fryer, 1956)",
 "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 ((34.8752 27.7528,35.2765 29.0599,28.1382 29.0599,28.1382 20.0599,37.1382 20.0599,37.1382 22.4869,36.4196 23.1029,36.7113 23.3743,36.7246 23.7183,35.8613 24.5048,35.1435 25.653,35.0599 25.8405,35.3478 26.1707,35.2694 26.5949,34.978 26.7967,34.4974 26.7726,34.5022 27.2926,34.8752 27.7528))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060021-250525065834625/datasets/export for details.
DOI: 10.15468/dl.xr9t9e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Klebsormidium P.C.Silva, K.Mattox & W.Blackwell, 1972",
 "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 ((25.5395 -23.2724,25.1306 -24.0715,25.5395 -24.0715,25.5395 -23.2724))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060023-250525065834625/datasets/export for details.
DOI: 10.23640/07243.8121218
title: ORCID 2019 Community Survey Anonymized Dataset

description: This is the anonymized dataset of the responses to ORCID's 2019 community survey (see references for a link to the full survey report).
DOI: 10.23640/07243.8121218.v1
title: ORCID 2019 Community Survey Anonymized Dataset

description: This is the anonymized dataset of the responses to ORCID's 2019 community survey (see references for a link to the full survey report).
DOI: 10.23640/07243.8009192
title: Blog: What’s New at ORCID? A Joint Library Carpentries/ORCID Webinar!

description: We’re delighted to announce the first Library Carpentries/ORCID co-hosted webinar, where you can learn about the great new features in ORCID’s new API v3.0.
DOI: 10.23640/07243.8009192.v1
title: Blog: What’s New at ORCID? A Joint Library Carpentries/ORCID Webinar!

description: We’re delighted to announce the first Library Carpentries/ORCID co-hosted webinar, where you can learn about the great new features in ORCID’s new API v3.0.