cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.xhqz4k
title: Occurrence Download

description: A dataset containing 212682 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Silene vallesia L., Silene glareosa Jord., Silene vulgaris (Moench) Garcke)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 212682 records from 637 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0327978-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sfyzwe
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Afroedura hawequensis Mouton & Mostet, 1985)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054083-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e2v7ws
title: Occurrence Download

description: A dataset containing 2651 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cryptogramma R.Br.",
 "BasisOfRecord is one of (Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (United States of America, Canada)"
 ]
}

 The dataset includes 2651 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0328012-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bgqs84
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ambystoma flavipiperatum Dixon, 1963"
 ]
}

 The dataset includes 40 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034163-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ntxdyh
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Pedetes surdaster Thomas, 1902",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 54 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034165-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/d7ndyb637p.1
title: Transgender Individuals and the Workplace: The Nature and Implications of Media Representations in Europe

description: This study explores the complex impact that media representations
have on transgender people working in European environments. It examines
how public perspectives, workplace interactions, and legal frameworks are
shaped by media depictions. Through an investigation of the media's
involvement in supporting or contradicting preconceptions, both positively
and negatively, this study sheds light on important implications for creating
inclusive work environments and furthering advocacy campaigns. This study
provides a thorough examination of how media images intersect with
transgender rights, ultimately influencing the dynamics of workplaces across
Europe. It does this by drawing on a combination of scientific research and
authoritative observations.
DOI: 10.17632/f64nr6n2ym.1
title: Good and Bad classification of tok palong leavs(Sour spinach)

description: ### Data Overview:
1. *Sample Size*: 
 - Total Samples: 1,000
 - Good Samples: 500
 - Bad Samples: 500

2. *Features*:
 - *Visual Characteristics*: Images or attributes describing the visual appearance of the leaves, such as color, size, shape, and presence of spots or discoloration.
 - *Texture*: Surface texture details, including smoothness or roughness, which may be critical in determining the quality.
 - *Chemical Composition*: Levels of key chemical components (if available) that might indicate spoilage or nutrient content.
 - *Environmental Factors*: Data on the conditions in which the leaves were grown, such as soil type, water quality, and exposure to pests or diseases.
 - *Harvesting Data*: Information on when and how the leaves were harvested, which could affect their quality.

### Classification Criteria:
- *Good Leaves*: Leaves that are healthy, vibrant in color, free from significant blemishes, and have the expected chemical composition and texture indicative of freshness.
- *Bad Leaves*: Leaves that show signs of spoilage, such as discoloration, wilting, presence of mold, or other physical defects, and any deviations in chemical composition suggesting degradation.

### Data Collection Method:
- *Manual Inspection*: Expert evaluation of each leaf sample to determine its classification.
- *Automated Imaging*: Use of high-resolution cameras and image processing algorithms to capture and analyze visual features.
- *Chemical Analysis*: Laboratory tests to measure the chemical composition of the leaves.
- *Environmental and Harvest Records*: Logs maintained by growers and harvesters to provide context to the growing and harvesting conditions.

### Purpose:
The goal of this project is to develop a robust machine learning model capable of accurately classifying Tok Palong leaves into good or bad categories based on the provided features. This classification will assist farmers, suppliers, and retailers in ensuring the quality of their produce, minimizing waste, and maximizing consumer satisfaction.

### Applications:
- *Quality Control*: Automated inspection systems at harvest or before distribution.
- *Supply Chain Optimization*: Better sorting and handling processes based on leaf quality.
- *Consumer Assurance*: Providing end-users with high-quality produce, enhancing trust and satisfaction.

### Data Processing and Model Development:
1. *Preprocessing*: Cleaning and normalizing the data to ensure consistency and accuracy.
2. *Feature Engineering*: Extracting and selecting the most relevant features for classification.
3. *Model Training*: Using various machine learning algorithms to train and validate models on the dataset.
4. *Evaluation*: Assessing
DOI: 10.17632/49799kh6kz.1
title: Disclosing and Cooling-Off: An Analysis of Insider Trading Rules

description: The code in this replication package constructs the analysis for the paper, “Disclosing and Cooling-Off: An Analysis of Insider Trading Rules”. Five main Matlab programs run all of the code to generate the four figures (Figures 2-5) and one table (Table 1) in the paper. 

Description of programs/code
 Step 1：Program in &lt;1. Calibration_Parameters.zip&gt; calibrates the insider’s hedge need Σ_z and the noiser trader’s hedge need Σ_u in Table 1.
 Step 2：Programs in &lt;2. Baseline_model.zip&gt; plot Figures 2 and 3 for the baseline model. 
 Step 3：Programs in &lt;3. Information_leakage_model.zip&gt; plot Figures 4 and 5 for the information leakage model. 

The replicator would run Step 1 first to get the calibration parameters that are the input for Step 2 and Step 3.
DOI: 10.17632/njzj278dz3.1
title: R codes for the analysis carried out in the article: Effectiveness of exercise-based interventions for reducing the injury incidence in youth team-sport players: A systematic review with network meta-analysis

description: R codes for the analysis carried out in the article "Effectiveness of exercise-based interventions for reducing the injury incidence in youth team-sport players: A systematic review with network meta-analysis" (Robles-Palazón et al.).
DOI: 10.17632/njzj278dz3.2
title: R codes for the analysis carried out in Part 1 of the article: Effectiveness of exercise-based interventions for reducing the injury incidence in youth team-sport players: A systematic review with network meta-analysis

description: R codes for the analysis carried out in Part 1 of the article "Effectiveness of exercise-based interventions for reducing the injury incidence in youth team-sport players: A systematic review with network meta-analysis" (Robles-Palazón et al.).
DOI: 10.17632/5gb2bx3r4k.1
title: CMV sequence data

description: transcriptome analysis
DOI: 10.15468/dl.jeb4f7
title: Occurrence Download

description: A dataset containing 1036675 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2024-05-21",
 "EventDate is less than or equal to 2024-06-20",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 1036675 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026893-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wf352ctzb4.1
title: Skin Self-Exam Survey

description: Evaluating the support of pigmented lesions experts for the use of skin self-examinations
DOI: 10.15468/dl.nunt48
title: Occurrence Download

description: A dataset containing 142244 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Apiaceae",
 {
 "or" : [
 "Country is Canada",
 "Country is United States of America"
 ]
 },
 {
 "or" : [
 "StateProvince is Ontario",
 "StateProvince is Quebec",
 "StateProvince is New Brunswick",
 "StateProvince is Illinois",
 "StateProvince is Indiana",
 "StateProvince is Tennessee",
 "StateProvince is West virginia",
 "StateProvince is Vermont",
 "StateProvince is Florida",
 "StateProvince is Alabama",
 "StateProvince is Georgia",
 "StateProvince is Lousiana",
 "StateProvince is Mississippi",
 "StateProvince is South Carolina",
 "StateProvince is Connecticut",
 "StateProvince is Kentucky",
 "StateProvince is Massachusetts",
 "StateProvince is Maryland",
 "StateProvince is North Carolina",
 "StateProvince is New Jersey",
 "StateProvince is New York",
 "StateProvince is Ohio",
 "StateProvince is Pennsylvania",
 "StateProvince is Rhode Island",
 "StateProvince is Virginia",
 "StateProvince is Wisconsin",
 "StateProvince is Michigan",
 "StateProvince is Maine",
 "StateProvince is Minnesota",
 "StateProvince is New Hampshire",
 "StateProvince is Delaware"
 ]
 },
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 142244 records from 172 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026902-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6vxt3kgp7r.1
title: Supplementary data: Testing the sequence of successional processes in miniature ecosystems

description: Supplemental Information 1: Digital, 3D-Printable model of the Ecosystems on a Plate used in this study.
DOI: 10.17632/6vxt3kgp7r.2
title: Supplementary data: Testing the sequence of successional processes in miniature ecosystems

description: Supplemental Information 1: Digital, 3D-Printable model of the Ecosystems on a Plate used in this study.
DOI: 10.17632/cjtxfhdtmm.1
title: Data for: Liking Cenozoic magmatism in the north-central Tibetan Plateau with plateau growth

description: Table S1. The geochemical compositions and calculated crustal thicknesses of the Cenozoic magmatic rocks from the north-central Tibetan Plateau
Table S2. High MgO basaltic rocks from the north-central Tibetan Plateau and their equilibrium pressures
DOI: 10.17632/mfjtttk585.1
title: Insects dynamics assesment of rice field using chemical fertilizers

description: Over two years (2019-20 and 2020-21), an experiment conducted at Bangladesh Rice Research Institute (BRRI), Habiganj, during the boro season aimed to identify the most effective multidimensional treatment (EMT) by testing various combinations of chemical fertilizers and its effect on rice insects.
DOI: 10.15468/dl.8kbacw
title: Occurrence Download

description: A dataset containing 300493768 species occurrences available in GBIF matching the query:
{
 "Year" : [
 "2023-2024"
 ]
}

 The dataset includes 300493768 records from 3899 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026862-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pra8ts
title: Occurrence Download

description: A dataset listing the 5 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "TaxonKey is Vulpes macrotis"
 ]
}

 The dataset's 5 records were derived from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056361-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p95bnwt3
title: United States Gulf Coast Basin Curated Wells and Logs Database (ver. 4.0, September 2025)

description: The United States (US) Gulf Coast Basin Curated Wells and Logs Database (CWLDB) is an online repository with stratigraphic information for petroleum wells in the United States portion of the onshore US Gulf Coast Basin that provides several of the following attributes: a) deep penetrations (generally, total depth of 10,000 feet or more), b) high quality and diverse geophysical well log suites, c) lithostratigraphic logs, d) biostratigraphic units (biozones) and reports, and/or e) core or cuttings samples.
 
First release: March 2023
Revised: November 2023 (ver. 2.0)
Revised: July 2024 (ver. 3.0)
Revised: September 2025 (ver. 4.0)
DOI: 10.15468/dl.qsgnpg
title: Occurrence Download

description: A dataset containing 20509 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pilosella caespitosa"
 ]
}

 The dataset includes 20509 records from 430 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056399-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/f7789985a94ed6160d3c09ab518e066d
title: Baltimore Ecosystem Study: Stream chemistry for core sites in Gwynns Falls, 1998- ongoing

description: In the Baltimore urban long-term ecological research (LTER) project, (Baltimore Ecosystem Study, BES) we use the watershed approach to evaluate integrated ecosystem function.
 The LTER research is centered on the Gwynns Falls watershed, a 17,150 ha catchment that traverses a gradient from the urban core of Baltimore, through older urban residential (1900 - 1950) and suburban (1950- 1980) zones, rapidly suburbanizing areas and a rural/suburban fringe.
 Our long-term sampling network includes four longitudinal sampling sites along the Gwynns Falls as well as several small (40 - 100 ha) watersheds located within or near to the Gwynns Falls. The longitudinal sites provide data on water and nutrient fluxes in the different land use zones of the watershed (rural/suburban, rapidly suburbanizing, old suburban, urban core) and the small watersheds provide more focused data on specific land use areas (forest, agriculture, rural/suburban, urban). Each of the gaging sites is continuously monitored for discharge and is sampled weekly for chemistry. Additional chemical sampling is carried out in a supplemental set of sites to provide a greater range of land use. Weekly analyses includes nitrate, phosphate, total nitrogen, total phosphorus, chloride and sulfate, turbidity, fecal coliforms, temperature, dissolved oxygen and pH. Cations, dissolved organic carbon and nitrogen and metals are measured on selected samples.
 Streamflow data for this site are posted at: http://waterdata.usgs.gov/md/nwis/nwisman?site_no=015835701
DOI: 10.15468/dl.2xqjmq
title: Occurrence Download

description: A dataset containing 6256 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Juglans mandshurica"
 ]
}

 The dataset includes 6256 records from 232 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056398-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xpnwse
title: Occurrence Download

description: A dataset containing 127069 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is NSW AVH feed",
 "Year 1000-1913"
 ]
}

 The dataset includes 127069 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056362-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d7ac5a
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Diluvarca ferruginea",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056402-250827131500795/datasets/export for details.
DOI: 10.15468/dl.tq6xy9
title: Occurrence Download

description: A dataset containing 27575 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gymnocephalus cernua",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((40.81373 29.26705, 40.81373 50.94, -6.6 50.94, -6.6 29.26705, 40.81373 29.26705))",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Material sample)"
 ]
}

 The dataset includes 27575 records from 145 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056401-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17048864
title: Greenhouse Gas Emissions By Industry in the United States 2019-2023

description: The 2019-2023 National-Level Greenhouse Gas Emission Totals by Industry dataset contains ~23 thousand records of GHGs directly emitted in the U.S. by economic sectors and households across two Excel files, one for each level of industry detail. The aggregate level provides data for all U.S. industries and households in 118 categories. The detail provides data for all U.S. industries and households in 527 categories. Sectors are defined using the codes from the 2017 version of the North American Industry Classification System (NAICS). The 2025 version of the U.S. GHG Inventory (GHGI) is the primary source for the emissions quantities in the dataset (U.S. EPA 2025). The 19 unique GHG chemicals or groups are reported using the nomenclature from the Federal LCA Commons Elementary Flow List v1.2.0 (Edelen et al. 2019), where all individual GHGs present in the GHGI are reported. The dataset uses the Flow-By-Sector collapsed format (Birney et al. 2022). Emissions of each GHG are reported in kilograms (kg) per year, except for groups of hydrofluorocarbons (HFCs) and perfluorohydrocarbons (PFCs) where the original data were only available in CO2-equivalents (CO2e). Even more detailed versions of the Excel datasets are available in Apache parquet files, where each record provides a total for a given GHG, sector, year, location, and data source and provides all the fields in the Flow-By-Sector collapsed format. The Excel versions of the data provide records aggregated at the level of GHG, sector, and year. The Excel versions also provide supporting definitions of sector codes. The geographic scope of the emission totals include the U.S. States and District of Columbia. The code used to generate the datasets is available in v2.1.0 of the FLOWSA tool.

These data are an update to 2012-2022 National-Level Greenhouse Gas Emission Totals by Industry (Young and Ingwersen 2024). Data for 2023 are added and data for 2019-2022 are updated. Data for years prior to 2019 are not included in this release. Please see the related publication for more background information and methodological detail (Young, Birney, and Ingwersen 2024), along with the release notes below.
DOI: 10.34740/kaggle/dsv/13040873
title: Peer-to-Peer Boliviano (BOB) Exchange Data