cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.vfcyf7
title: Occurrence Download

description: A dataset containing 2785 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Veniliornis nigriceps (d'Orbigny, 1841))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2785 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0297712-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g5suu6
title: Occurrence Download

description: A dataset containing 145414 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Canis lupus Linnaeus, 1758"
 ]
}

 The dataset includes 145414 records from 550 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0190358-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bnxsv5
title: Occurrence Download

description: A dataset containing 245495 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pistacia lentiscus L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 245495 records from 251 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0297711-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n6kecj
title: Occurrence Download

description: A dataset containing 50649 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Afromoths, online database of Afrotropical moth species (Lepidoptera)"
 ]
}

 The dataset includes 50649 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009091-240202131308920/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tmtwcc
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lobelia harrisii Urb."
 ]
}

 The dataset includes 14 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012183-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mdz5wh
title: Occurrence Download

description: A dataset containing 1122 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ecnomiohyla miotympanum (Cope, 1863)"
 ]
}

 The dataset includes 1122 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0193669-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hecs3e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xysticus audax (Schrank, 1803)",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0190289-220831081235567/datasets/export for details.
DOI: 10.15468/dl.ee6w6t
title: Occurrence Download

description: A dataset containing 486 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is B4C Road Kill Map"
 ]
}

 The dataset includes 486 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0122614-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c74xjm
title: Occurrence Download

description: A dataset containing 139806 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Odocoileus virginianus (Zimmermann, 1780)"
 ]
}

 The dataset includes 139806 records from 311 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0190283-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.axag72
title: Occurrence Download

description: A dataset containing 65030 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coprinus comatus (O.F.Müll.) Pers."
 ]
}

 The dataset includes 65030 records from 348 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0297737-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14677601
title: hackathon D4GEN 2024
DOI: 10.5281/zenodo.14677602
title: hackathon D4GEN 2024
DOI: 10.5281/zenodo.14679919
title: hackathon D4GEN 2024
DOI: 10.12770/659b5bec-3b35-4b5e-b66c-ca4b7aee2359
title: Inventaire de la macrofaune benthique dans les marais de Charente Maritime, entre 2019 et 2022

description: Caractérisation des communautés de macrofaune benthique dans les marais de Charente Maritime, entre 2019 et 2022 : Fier d'Ars, Brouage et Tasdon (La Rochelle), correspondant aux stations Pampas. les marais de Tasdon (La Rochelle) : printemps et automne 2019,  printemps 2022
le Fier d’Ars (île de Ré) : printemps et automne 2019
les marais de Brouage (sud Charente-Maritime) : printemps et automne 2021. 
Sur le terrain, les prélèvements sont effectués avec une benne Van Veen (tamisage sur 500 micromètres, conditionnement et étiquetage en pot avec éthanol absolu) suivant une méthode d'échantillonnage standardisée, quantitative, spatialemment hierarchisée.
En laboratoire, les échantillons sont nettoyés sur tamis de 500 um. Le refus de tamis est trié à l’œil nu pour extraire la macrofaune. Puis, l'identification et le comptage sont réalisés à loupe binoculaire et si nécessaire, au microscope. 
Les informations prinicpales sont : nom de la station (Pampas), sous-station, numéro échantillon, date de prélèvements, nom espèce (référence à la base de données Taxref) et abondance.
DOI: 10.15468/dl.jdv5y5
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plantae)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((1.694738 41.821071, 1.694738 41.835623, 1.718007 41.835623, 1.718007 41.821071, 1.694738 41.821071))"
 ]
}

 The dataset includes 71 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000144-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kfuce2
title: Occurrence Download

description: A dataset containing 653 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.88318 -18.25925,-69.01303 -18.25925,-69.01303 -13.68439,-75.88318 -13.68439,-75.88318 -18.25925))",
 "HasGeospatialIssue is false",
 "TaxonKey is Rollandia rolland (Quoy & Gaimard, 1824)",
 "Year 2021-2025"
 ]
}

 The dataset includes 653 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000142-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14679858
title: Russian drained peat bogs, hot spots 2002-2024 and analytics of this data.

description: Database for the operation of the Web resource https://peatfires.nextgis.com/, which is implemented within the framework of the program of the ANO "Center for the Prevention of Landscape Fires" to reduce the fire hazard of peat bogs.

The vector layer of the boundaries of drained peat bogs of this resource was created with the participation of volunteers and contains more than 13.5 thousand contours (polygons) of drained bogs. We also use a buffer 1 km wide from the boundary of the peat bog to display the current layer of thermal spots.

Peat bogs were identified by visual interpretation of satellite images based on a combination of features: characteristic drainage channels and the distances between them, former quarries of hydraulic mining and machine-molding peat extraction. The relief, hydrography, and signs of predominant vegetation were also assessed. In controversial cases, the history of drainage of a particular place was studied based on literary data.

Some contours may contain errors and inaccuracies in defining the boundaries in cases where at the time of creating the polygon there was no image of sufficiently high resolution.

2. Layer of hotspots

On the map, we show current MODIS and VIIRS hotspots within the contours of drained peatlands + a 1 km buffer zone. A hotspot in a contour gives reason to believe that there was an open fire of grass or forest on the drained peat, which could lead to the formation of smoldering soil foci.

Layers with historical hotspots since 2002 have been added. All hotspots are divided by year, season (spring and summer/fall) and types of satellite devices with characteristic errors.

Source of hotspot data: FIRMS

3. Automatically generated flammability indicators

Automatic graphs of the distribution of hotspots by years and seasons have been added. These graphs reflect the recurrence of fires, the probable transition of spring fires to summer fires within one year. In the case of spatial coincidence of thermal points in the fall of one year and in the spring of the next, it is possible to assume the presence of wintering smoldering centers.
DOI: 10.5281/zenodo.14679859
title: Russian drained peat bogs, hot spots 2002-2024 and analytics of this data.

description: Database for the operation of the Web resource https://peatfires.nextgis.com/, which is implemented within the framework of the program of the ANO "Center for the Prevention of Landscape Fires" to reduce the fire hazard of peat bogs.

The vector layer of the boundaries of drained peat bogs of this resource was created with the participation of volunteers and contains more than 13.5 thousand contours (polygons) of drained bogs. We also use a buffer 1 km wide from the boundary of the peat bog to display the current layer of thermal spots.

Peat bogs were identified by visual interpretation of satellite images based on a combination of features: characteristic drainage channels and the distances between them, former quarries of hydraulic mining and machine-molding peat extraction. The relief, hydrography, and signs of predominant vegetation were also assessed. In controversial cases, the history of drainage of a particular place was studied based on literary data.

Some contours may contain errors and inaccuracies in defining the boundaries in cases where at the time of creating the polygon there was no image of sufficiently high resolution.

2. Layer of hotspots

On the map, we show current MODIS and VIIRS hotspots within the contours of drained peatlands + a 1 km buffer zone. A hotspot in a contour gives reason to believe that there was an open fire of grass or forest on the drained peat, which could lead to the formation of smoldering soil foci.

Layers with historical hotspots since 2002 have been added. All hotspots are divided by year, season (spring and summer/fall) and types of satellite devices with characteristic errors.

Source of hotspot data: FIRMS

3. Automatically generated flammability indicators

Automatic graphs of the distribution of hotspots by years and seasons have been added. These graphs reflect the recurrence of fires, the probable transition of spring fires to summer fires within one year. In the case of spatial coincidence of thermal points in the fall of one year and in the spring of the next, it is possible to assume the presence of wintering smoldering centers.
DOI: 10.25810/5dxz-xm93.81
title: Latin American Newspaper Coverage of Climate Change or Global Warming, 2005-2024 - December 2024

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.15468/dl.tkv4zg
title: Occurrence Download

description: A dataset containing 8889 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-7.3836 37.35567,-5.13693 37.35567,-5.13693 42.71386,-7.3836 42.71386,-7.3836 37.35567))",
 "HasGeospatialIssue is false",
 "TaxonKey is Charadrius hiaticula Linnaeus, 1758"
 ]
}

 The dataset includes 8889 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000145-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gcc3wb
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plantae)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((1.694738 41.821071, 1.694738 41.835623, 1.718007 41.835623, 1.718007 41.821071, 1.694738 41.821071))"
 ]
}

 The dataset includes 71 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000146-250117142028555/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.28218101.v1
title: FBHsArea_China_01deg.zip

description: A gridded dataset of forest biohazard occurrence area in China at 0.1° resolution for the period of 1900-2000 with one period of 20 years and a total of six periods was generated by using superimposed integrated learning and geospatial data of longer time series and Chinese municipal forest biohazard statistics for the period of 2003-2017. Municipal FBHs statistics in China from 2003-2017 were first collected as the dependent variables for model inputs, and 10 proxy variables were used as the independent variables for model inputs. Subsequently, three machine learning algorithms (RF, XGBoost and LightGBM) were selected as the basic models to create and train the stacked integrated learning model. Then, 10 proxy variables for six years, namely 1900, 1920, 1940, 1960, 1980 and 2000, were introduced into the trained integrated learning model to generate a gridded forest biohazard occurrence area dataset (0.1° × 0.1°) for China from 1900 to 2000. Finally, we collected forest biohazard record points from historical literature and reports to compare with them, and the average incidence area grids of forest biohazards in China for the years 1900-1950 and 1950-2000 overlapped with the historical occurrence points by 81.82% and 83.33%, respectively.
DOI: 10.17632/df5zf9jhgs.1
title: Lending through the technology lifecycle: Strategies for information search by banks in renewable energy project finance

description: The code replicates the results of the manuscript: Lending through the technology lifecycle: Strategies for information search by banks in renewable energy project finance.

The data for the code is sourced from Bloomberg New Energy Finance (BNEF), and the Bloomberg Terminal. The authors do not have the right to share the raw data. Readers are recommended to download the raw data from Bloomberg New Energy Finance and the Bloomberg Terminal before running the scripts. 

For any questions and requests, please contact Anurag Gumber (corresponding author).
DOI: 10.17632/jp5zcmcdt8.1
title: Improving identification of determinants of evidence-based practice implementation - Barriers Survey

description: This study leveraged an existing implementation effort in Kaiser Permanente Washington to increase access to cancer genetic services. Using OPTICC Stage I and Stage II methods (see OPTICC.org), this study matched implementation strategies to high priority determinants for routine hereditary cancer risk assessment and delivery of genetic testing, and evaluated a staged, stakeholder-driven approach to program implementation planning. This dataset consists of responses to our barrier survey, based on a 4-point Likert scale (Example: not at all influential, somewhat influential, influential, extremely influential). The purpose of this survey was to gauge participants' thoughts on how influential and persistent different barriers to care are. These data were collected in July 2023. Six participants responded to this survey.
DOI: 10.17632/p2vffrkc5v.2
title: Gaseous elemental mercury data during the summertime MOSAiC expedition

description: This dataset includes hourly gaseous elemental mercury (GEM) concentrations that measured during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, and the corresponding meteorological, date and location data.
DOI: 10.17632/2sxpw8x6jd.1
title: Improving identification of determinants of evidence-based practice implementation - Barrier Rankings

description: This study leveraged an existing implementation effort in Kaiser Permanente Washington to increase access to cancer genetic services. Using OPTICC Stage I and Stage II methods (see OPTICC.org), this study matched implementation strategies to high priority determinants for routine hereditary cancer risk assessment and delivery of genetic testing, and evaluated a staged, stakeholder-driven approach to program implementation planning. This dataset includes aggregated responses from the Barriers Survey. It includes the mean score as well as score range for impact and persistence, as well as overall score. It then includes the study teams analysis ranking, based on the responses. For the survey responses (middle 3 columns), the higher the number the more impactful/persistent the barrier (1 = not at all influential/has never been a problem, 4= extremely influential/has always been a problem). For the study team ranking/analysis (last column), 1 = most important (so opposite of the survey responses). This data was collected in July 2023. Six participants responded to the survey that fed into this analysis
• This data includes aggregate quantitative data
DOI: 10.17632/v8k6c5s9cy.2
title: Credal Bird-2

description: Most datasets used for classification use hard labels. Credal Bird-2 was labeled uncertainly and imprecisely by contributors during crowdsourcing campaigns. Resulting in richer labels, modeled with the theory of belief funtions, which generalizes several reasoning frameworks with uncertainty. These datasets can be used with classical models using hard labels but also with probabilistic, fuzzy or even evidential models.

Dataset:
10 classes, 200 observations, 30 features (or raw pictures)

When using the dataset please cite:
A. Hoarau, C. Thierry, A. Martin, J.-C. Dubois, Y. Le Gall, "Datasets with rich labels for machine learning", in: FUZZ-IEEE, 2023.

Credal Bird-2 dataset
├── data
│ ├── classes.csv: Classes of the dataset
│ ├── X.csv: Features of the dataset
│ ├── X_512.csv: Learge 512 features vector of the dataset
│ ├── X_pictures.csv: Raw features (Pictures themselves)
│ ├── y.csv: Rich labels
│ └── y_true.csv: True labels
└── extra
 ├── y_hard.csv: Hard labels given during a new campaign
 ├── DATA_imperfect.csv: Imperfect answers given during the campaign
 ├── ITERATION_imperfect.csv: Imperfect 2nd step answers given during the campaign
 ├── EVENT_imperfect.csv: Contributors events
 ├── ID_imperfect.csv: Contributors IDs
 └── DATA_perfect.csv: Precise answers given during a new campaign

Credal Dog-7:
https://data.mendeley.com/datasets/4hz3wx6wm5
Credal Dog-4:
https://data.mendeley.com/datasets/44zpkbrpxb
Credal Dog-2:
https://data.mendeley.com/datasets/95mmjfrsyh
Credal Bird-10:
https://data.mendeley.com/datasets/9sz5vm8g7h
DOI: 10.17632/dcztd63cft.1
title: Improving identification of determinants of evidence-based practice implementation - Strategies Survey

description: This study leveraged an existing implementation effort in Kaiser Permanente Washington to increase access to cancer genetic services. Using OPTICC Stage I and Stage II methods (see OPTICC.org), this study matched implementation strategies to high priority determinants for routine hereditary cancer risk assessment and delivery of genetic testing, and evaluated a staged, stakeholder-driven approach to program implementation planning. This dataset consists of responses to our strategies survey, based on a 4-point Likert scale (Example: not at all workable, somewhat workable, workable, extremely workable). The purpose of this survey was to gauge participants' thoughts on how workable and effective different implementation strategies would be. Five participants responded to this survey. This data was collected in August 2023. This document includes quantitative data
DOI: 10.17632/fcsjwd9gr6.1
title: Forest Fire Dataset

description: This dataset comprises information related to forest fires and is intended for training algorithms designed for forest fire detection, alongside data for object detection. The section dedicated to fire classification consists of 2974 images, divided into two categories: the first category includes images depicting forest fires, while the second category contains images of intact forests without fires. As for the object detection data, it encompasses 1690 images, suitable for object detection purposes. These data have been distributed across training, validation, and test sets with proportions of 80%, 15%, and 5%, respectively. [1] A. Khan and B. Hassan, “Dataset for forest fire detection,” Mendeley Data, vol. 1, p. 2020, 2020, Accessed: Nov. 18, 2023. [Online]. Available: https://data.mendeley.com/datasets/gjmr63rz2r/1 [2] https://www.kaggle.com/datasets/phylake1337/fire-dataset These data were gathered from various sources on the internet and were manually filtered to ensure data integrity. Additionally, a portion of this data was generated manually by simulating forest fires after obtaining the necessary approvals from relevant authorities. This project is part of a master's thesis titled "Development of a Deep Learning-Based Surveillance System for Forest Fire Detection and Monitoring using UAV (İHA KULLANILARAK ORMAN YANGINLARININ TESPİTİ VE GÖRÜNTÜLENMESİ İÇİN DERİN ÖĞRENME TABANLI GÖZETLEME SİSTEMİNİN GELİŞTİRİLMESİ)" at Karabuk University in Turkey, conducted by the student Ibrahim Shmata and supervised by Dr. Batıkan Erdem Demir.