cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.y48e4u
title: Occurrence Download

description: A dataset containing 145 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((99.05500000000001 8.743085000000001, 98.98391700000001 8.737513, 98.914591 8.720934, 98.84873399999999 8.693757, 98.787972 8.656655000000001, 98.73380299999999 8.610543, 98.687562 8.556559, 98.65038699999999 8.496036999999999, 98.62319100000001 8.430467999999999, 98.606638 8.361470000000001, 98.60113 8.290742, 98.606798 8.220026000000001, 98.623496 8.151064, 98.650807 8.085551000000001, 98.68805500000001 8.025099000000001, 98.73432099999999 7.971194, 98.788465 7.92516, 98.849153 7.888128, 98.914896 7.861008, 98.984078 7.844465, 99.05500000000001 7.838905, 99.125922 7.844465, 99.195104 7.861008, 99.260847 7.888128, 99.321535 7.92516, 99.37567900000001 7.971194, 99.42194499999999 8.025099000000001, 99.459193 8.085551000000001, 99.486504 8.151064, 99.503202 8.220026000000001, 99.50887 8.290742, 99.503362 8.361470000000001, 99.48680899999999 8.430467999999999, 99.459613 8.496036999999999, 99.422438 8.556559, 99.376197 8.610543, 99.322028 8.656655000000001, 99.26126600000001 8.693757, 99.195409 8.720934, 99.12608299999999 8.737513, 99.05500000000001 8.743085000000001))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 145 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005469-260120142942310/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.31129753
title: Postloss LCA risk factors

description: The study aimed to obtain different trajectories in the aftermath of loss using the Prolonged Grief Disorder (PGD), Depression and Posttraumatic Stress Disorder (PTSD) symptoms to form symptom clusters.
DOI: 10.6084/m9.figshare.31129753.v1
title: Postloss LCA risk factors

description: The study aimed to obtain different trajectories in the aftermath of loss using the Prolonged Grief Disorder (PGD), Depression and Posttraumatic Stress Disorder (PTSD) symptoms to form symptom clusters.
DOI: 10.15468/dl.4c6xn5
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.73184000000001 24.495318, 88.654675 24.489741, 88.579431 24.473148, 88.507976 24.445954, 88.442086 24.408832, 88.383391 24.362706, 88.33334000000001 24.308721, 88.293161 24.248214, 88.263835 24.182683, 88.24606900000001 24.113748, 88.24028300000001 24.043111, 88.2466 23.972512, 88.26484499999999 23.903687, 88.294551 23.838328, 88.334974 23.778037, 88.38511 23.724291, 88.44372 23.678405, 88.509366 23.6415, 88.58044099999999 23.614477, 88.65520600000001 23.597995, 88.73184000000001 23.592456, 88.808474 23.597995, 88.883239 23.614477, 88.954314 23.6415, 89.01996 23.678405, 89.07857 23.724291, 89.12870599999999 23.778037, 89.169129 23.838328, 89.198835 23.903687, 89.21708 23.972512, 89.22339700000001 24.043111, 89.21761100000001 24.113748, 89.199845 24.182683, 89.170519 24.248214, 89.13034 24.308721, 89.08028899999999 24.362706, 89.02159399999999 24.408832, 88.955704 24.445954, 88.884249 24.473148, 88.809005 24.489741, 88.73184000000001 24.495318))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005470-260120142942310/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.30670667
title: Applicability of a four-gene set for H-ARS severity prediction in peripheral blood samples of irradiated minipigs

description: The fast diagnosis of the life-threatening acute radiation syndrome is crucial, as the early prediction of the hematological acute radiation syndrome (H-ARS) can save lives. Previously, we validated a four-gene set (<i>FDXR</i>, <i>DDB2</i>, <i>POU2AF1</i>, <i>WNT3</i>) for H-ARS severity prediction in non-human primates and leukemia patients. In this study, we aim to validate this gene set in minipigs as a surrogate model. 12 Göttingen minipigs, irradiated with 1.8 or 2.1 Gray (LD<sub>≈40/30</sub>), were examined. METREPOL H-ARS severity degrees were determined using sequential blood cell count changes over time after irradiation. For quantitative Real-Time-PCR (qRT-PCR), peripheral whole blood was withdrawn before and on days 1, 3, and 10 after irradiation. Normalization was performed using <i>18S rRNA</i> and <i>PUM1</i> as housekeeping genes (HKG). Differential gene expression (DGE) relative to the pre-irradiated samples was calculated. All minipigs developed a 2–4 H-ARS-severity degree. <i>18S rRNA</i> revealed significantly (<i>p</i> = .0005) about two-fold higher variance of raw Ct-values than <i>PUM1</i>. DGE was calculated for all genes except <i>WNT3</i> (undetectable in most animals). All genes revealed a slight up-regulation over time in most animals, but DGE &gt; 2 regarding <i>FDXR</i> or <i>DDB2</i> and concomitant downregulation of <i>POU2AF1</i> (DGE &lt; 0.5) as expected given the H-ARS severity degrees, was not observed. None of the animals revealed the expected DGE pattern corresponding to a moderate to high H-ARS-severity degree. Hence, the Göttingen minipig did not qualify as another validation model for our specific gene set, which does not argue against their validity for other purposes.
DOI: 10.6084/m9.figshare.30670667.v1
title: Applicability of a four-gene set for H-ARS severity prediction in peripheral blood samples of irradiated minipigs

description: The fast diagnosis of the life-threatening acute radiation syndrome is crucial, as the early prediction of the hematological acute radiation syndrome (H-ARS) can save lives. Previously, we validated a four-gene set (<i>FDXR</i>, <i>DDB2</i>, <i>POU2AF1</i>, <i>WNT3</i>) for H-ARS severity prediction in non-human primates and leukemia patients. In this study, we aim to validate this gene set in minipigs as a surrogate model. 12 Göttingen minipigs, irradiated with 1.8 or 2.1 Gray (LD<sub>≈40/30</sub>), were examined. METREPOL H-ARS severity degrees were determined using sequential blood cell count changes over time after irradiation. For quantitative Real-Time-PCR (qRT-PCR), peripheral whole blood was withdrawn before and on days 1, 3, and 10 after irradiation. Normalization was performed using <i>18S rRNA</i> and <i>PUM1</i> as housekeeping genes (HKG). Differential gene expression (DGE) relative to the pre-irradiated samples was calculated. All minipigs developed a 2–4 H-ARS-severity degree. <i>18S rRNA</i> revealed significantly (<i>p</i> = .0005) about two-fold higher variance of raw Ct-values than <i>PUM1</i>. DGE was calculated for all genes except <i>WNT3</i> (undetectable in most animals). All genes revealed a slight up-regulation over time in most animals, but DGE &gt; 2 regarding <i>FDXR</i> or <i>DDB2</i> and concomitant downregulation of <i>POU2AF1</i> (DGE &lt; 0.5) as expected given the H-ARS severity degrees, was not observed. None of the animals revealed the expected DGE pattern corresponding to a moderate to high H-ARS-severity degree. Hence, the Göttingen minipig did not qualify as another validation model for our specific gene set, which does not argue against their validity for other purposes.
DOI: 10.15468/dl.uhjznf
title: Occurrence Download

description: A dataset containing 731 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((98.432 8.744085, 98.360917 8.738512999999999, 98.291591 8.721933999999999, 98.22573300000001 8.694756999999999, 98.16497099999999 8.657655, 98.11080200000001 8.611542999999999, 98.064561 8.557558999999999, 98.02738600000001 8.497037000000001, 98.00019 8.431468000000001, 97.983637 8.36247, 97.978129 8.291741999999999, 97.983797 8.221026, 98.000495 8.152063999999999, 98.027806 8.086551, 98.065054 8.026099, 98.11132000000001 7.972194, 98.165464 7.92616, 98.226153 7.889128, 98.29189599999999 7.862008, 98.36107800000001 7.845465, 98.432 7.839905, 98.502922 7.845465, 98.572104 7.862008, 98.63784699999999 7.889128, 98.698536 7.92616, 98.75268 7.972194, 98.798946 8.026099, 98.83619400000001 8.086551, 98.863505 8.152063999999999, 98.88020299999999 8.221026, 98.88587099999999 8.291741999999999, 98.880363 8.36247, 98.86381 8.431468000000001, 98.836614 8.497037000000001, 98.79943900000001 8.557558999999999, 98.753198 8.611542999999999, 98.699029 8.657655, 98.638267 8.694756999999999, 98.57240899999999 8.721933999999999, 98.503083 8.738512999999999, 98.432 8.744085))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 731 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005477-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.whfy7s
title: Occurrence Download

description: A dataset containing 316 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations, Wetland Invertebrate and Environmental Data 2010 Sampling Events, Wetland Algae, Diversity of algae in wetlands, New Zealand Freshwater Fish Database (extended), New Zealand Freshwater Fish Database, Observation.org, Nature data from around the World)",
 "Geometry POLYGON((173.1522 -34.83198,173.13884 -34.8332,173.13455 -34.83685,173.14635 -34.86809,173.15914 -34.88705,173.16848 -34.89713,173.18324 -34.91367,173.19209 -34.92496,173.21057 -34.9338,173.22543 -34.93454,173.23286 -34.93679,173.25218 -34.93042,173.26882 -34.92235,173.27411 -34.90809,173.27865 -34.89879,173.2726 -34.88762,173.25898 -34.88266,173.231 -34.88017,173.20226 -34.87211,173.17503 -34.85784,173.1522 -34.83198))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (EX, EW, CR, EN, VU, NT, LC, DD, NE)",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (e2e717bf-551a-4917-bdc9-4fa0f342c530, 28eb1a3f-1c15-4a95-931a-4af90ecb574d, c58ac019-b413-4613-8772-39e57acbeb8e, c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f)",
 "Year 2000-2026"
 ]
}

 The dataset includes 316 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005476-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qbc74t
title: Occurrence Download

description: A dataset containing 3336 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chelonoidis carbonarius"
 ]
}

 The dataset includes 3336 records from 225 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005473-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.upzsf3
title: Occurrence Download

description: A dataset containing 4633 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Kinosternon scorpioides"
 ]
}

 The dataset includes 4633 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005472-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/ad35c80cafd87ba928d3f37fa124ccaf
title: Interagency Ecological Program: Over four decades of juvenile fish monitoring data from the San Francisco Estuary, collected by the Delta Juvenile Fish Monitoring Program, 1976-2025

description: The United States Fish and Wildlife Service Delta Juvenile Fish Monitoring Program (DJFMP) has monitored juvenile Chinook Salmon Oncorhynchus tshawytscha and other fish species within the San Francisco Estuary (Estuary) since 1976 using a combination of surface trawls and beach seines. Since 2000, three trawl sites and 58 beach seine sites have been sampled weekly or biweekly within the Estuary and lower Sacramento and San Joaquin Rivers. As part of the Interagency Ecological Program (IEP) that manages the Estuary, the DJFMP has tracked the relative abundance and distribution of naturally and hatchery produced juvenile Chinook Salmon of all races as they outmigrate through the Sacramento-San Joaquin Delta for over four decades. The data that DJFMP collected has been used not only to help inform the management of Chinook Salmon, but also to monitor the status of native species of interest such as the previously listed Sacramento Splittail Pogonichthys macrolepidotus and invasive species such as Mississippi Silverside Menidia audens and Largemouth Bass Micropterus salmoides. 
 DATA CORRECTION/UPDATE: Previous data versions 244.6, 244.7, and 244.8 contained an error and resulted in duplicated records of hatchery Chinook Salmon in the datasets. These datasets were removed from the data repository and the error was corrected in version 244.9 and after. 
 DNA Data: Full more details of the DNA methods and results for juvenile Chinook salmon included in this dataset, please check out Blankenship, S.M., J. Israel, E. Buttermore, and K. Reece. 2021. Knights Landing, California Department of Fish and Wildlife, Genetic Determination of Population of Origin 2017 through 2019 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/85fbc988c0b1362e84c318e69c7a939e.
 For more information on the Delta Juvenile Fish Monitoring Program: https://www.fws.gov/project/delta-juvenile-fish-monitoring-program
DOI: 10.15468/dl.23wsee
title: Occurrence Download

description: A dataset containing 316 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations, Wetland Invertebrate and Environmental Data 2010 Sampling Events, Wetland Algae, Diversity of algae in wetlands, New Zealand Freshwater Fish Database (extended), New Zealand Freshwater Fish Database, Observation.org, Nature data from around the World)",
 "Geometry POLYGON((173.1522 -34.83198,173.13884 -34.8332,173.13455 -34.83685,173.14635 -34.86809,173.15914 -34.88705,173.16848 -34.89713,173.18324 -34.91367,173.19209 -34.92496,173.21057 -34.9338,173.22543 -34.93454,173.23286 -34.93679,173.25218 -34.93042,173.26882 -34.92235,173.27411 -34.90809,173.27865 -34.89879,173.2726 -34.88762,173.25898 -34.88266,173.231 -34.88017,173.20226 -34.87211,173.17503 -34.85784,173.1522 -34.83198))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (EX, EW, CR, EN, VU, NT, LC, DD, NE)",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (e2e717bf-551a-4917-bdc9-4fa0f342c530, 28eb1a3f-1c15-4a95-931a-4af90ecb574d, c58ac019-b413-4613-8772-39e57acbeb8e, c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f)",
 "Year 2000-2026"
 ]
}

 The dataset includes 316 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005478-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14689868
title: Mexican Sign Language Alphabet (dynamic signs only)

description: Mexican Sign Language Alphabet (dynamic signs only)

Overview

The Dynamic Alphabet dataset for Mexican Sign Language (LSM) is a comprehensive and diverse collection of the signs J, K, Ñ, Q, X, and Z, in order to complement LSM static alphabet https://doi.org/10.5281/zenodo.10067509. This dataset is designed to facilitate research and the development of tools focused on both static and dynamic sign recognition.

Data Representation



The dataset consists of videos of a hand performing the sign, captured in a controlled environment with good lighting and a uniform green background.

Since these signs are dynamic, they are represented by videos that allow visualization of how the sign evolves over time.

In total, data were collected from 20 participants, ensuring a wide variety of sign representations within this set.


Data Collection



Data were gathered in a controlled environment. The subject stands in a fixed position and extends their hand sufficiently far from the body to obtain a clearer view of the sign.

Two views respect to the camera were recorded: (i) frontal, and (ii) 45°-right-rotated perspective.

As mentioned previously, these signs were recorded against a uniform green background, ensuring minimal loss in detection.

Each participant performed each sign five times.


Data Partitioning

The dataset is divided into two subfolders, "train" and "test":



The "train" folder contains data from the first 18 participants.

The "test" folder contains data from the remaining 2 participants.



Each video has the following naming convention: S<SubjectId>-<Letter>-<View>-<RepetitionNumber>.
DOI: 10.5281/zenodo.14689869
title: Mexican Sign Language Alphabet (dynamic signs only)

description: Mexican Sign Language Alphabet (dynamic signs only)

Overview

The Dynamic Alphabet dataset for Mexican Sign Language (LSM) is a comprehensive and diverse collection of the signs J, K, Ñ, Q, X, and Z, in order to complement LSM static alphabet https://doi.org/10.5281/zenodo.10067509. This dataset is designed to facilitate research and the development of tools focused on both static and dynamic sign recognition.

Data Representation



The dataset consists of videos of a hand performing the sign, captured in a controlled environment with good lighting and a uniform green background.

Since these signs are dynamic, they are represented by videos that allow visualization of how the sign evolves over time.

In total, data were collected from 20 participants, ensuring a wide variety of sign representations within this set.


Data Collection



Data were gathered in a controlled environment. The subject stands in a fixed position and extends their hand sufficiently far from the body to obtain a clearer view of the sign.

Two views respect to the camera were recorded: (i) frontal, and (ii) 45°-right-rotated perspective.

As mentioned previously, these signs were recorded against a uniform green background, ensuring minimal loss in detection.

Each participant performed each sign five times.


Data Partitioning

The dataset is divided into two subfolders, "train" and "test":



The "train" folder contains data from the first 18 participants.

The "test" folder contains data from the remaining 2 participants.



Each video has the following naming convention: S<SubjectId>-<Letter>-<View>-<RepetitionNumber>.
DOI: 10.5281/zenodo.18330564
title: Mexican Sign Language Glosses (Dynamic Signs)

description: Mexican Sign Language Glosses (Dynamic Signs)

Overview

The Mexican Sign Language (LSM) Glosses Dataset is a specialized collection of 121 dynamic signs designed to facilitate research in sign language recognition within healthcare contexts. This dataset covers a comprehensive lexicon tailored for medical emergencies, accident scenarios, common symptoms, and polite expressions essential for effective patient-provider interactions.

 

Data Representation



Format: High-definition video files in .mp4 format.

Content: Each video represents a single gloss (dynamic sign) executed from start to finish.

Environment: All recordings were captured in a controlled environment featuring optimized lighting conditions and a uniform green background to simplify background subtraction and keypoint extraction.

Perspective: All signs were recorded from a frontal view to provide a direct representation of hand shapes and body movements.


 

Data Collection and Participants

The dataset includes contributions from 12 participants, offering a balanced mix of expertise:



Experts: Two key subjects were included to ensure linguistic accuracy—a professional LSM interpreter (Subject 0) and a Deaf signer (Subject 11).

Non-Experts: Ten subjects (non-signers) were included to provide variability in sign execution, which is crucial for training robust machine learning models.

Repetitions: Each subject performed each of the 121 signs once, resulting in a diverse set of executions for each gloss.


 

Repository Structure

The data is organized by gloss name to simplify the training pipeline. Each folder contains the video files corresponding to that specific sign:



Mexican Sign Language Glosses/

├── <Gloss_Name>/ │    ├── <Gloss_Name>_<SubjectID>.mp4 │    └── ... └── ...


Naming Convention: <Gloss>_<SubjectID>.mp4



Gloss: The specific medical or polite expression (e.g., "Dolor", "Ayuda", "Hospital").

SubjectID: Numerical identifier (0–11).


 

Data Partitioning

As this is a primary corpus, no predefined train/test split is provided. This allows researchers to implement their own validation strategies, such as Leave-One-Subject-Out cross-validation, which is highly recommended given the participant-dependent nature of sign language.