cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.264sas
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Falco deiroleucus Temminck, 1825)",
 "Country is one of (Algeria, Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Congo, Democratic Republic of the, Côte d’Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, French Southern Territories, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Réunion, Rwanda, Saint Helena, Ascension and Tristan da Cunha, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Tanzania, United Republic of, Togo, Tunisia, Uganda, Western Sahara, Zambia, Zimbabwe)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015912-250525065834625/datasets/export for details.
DOI: 10.15468/dl.2x5etu
title: Occurrence Download

description: A dataset containing 744 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Acmispon americanus (Nutt.) Rydb.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 744 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010643-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13843495
title: List Benchmarks Kaggle
DOI: 10.34740/kaggle/dsv/13843496
title: ERC-20 Permit Drainer Scam Analysis
DOI: 10.15468/dl.j4qavt
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Orthogeomys cavator"
 ]
}

 The dataset includes 90 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004722-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x4atzb
title: Occurrence Download

description: A dataset containing 22654 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Malvaviscus arboreus",
 "HasCoordinate is true"
 ]
}

 The dataset includes 22654 records from 299 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004721-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17693453
title: Control software for STASIS open source RF exposure system

description: Open source software repository created within the STASIS project (https://www.ptb.de/stasis/). The software is used to control the open source hardware STASIS RF exposure system for parallel transmission measurements in magnetic resonance imaging: https://github.com/sOrzada/STASIS_Exposure_Hardware 

More information here: https://github.com/sOrzada/STASIS_Control
DOI: 10.5281/zenodo.17693454
title: Control software for STASIS open source RF exposure system

description: Open source software repository created within the STASIS project (https://www.ptb.de/stasis/). The software is used to control the open source hardware STASIS RF exposure system for parallel transmission measurements in magnetic resonance imaging: https://github.com/sOrzada/STASIS_Exposure_Hardware 

More information here: https://github.com/sOrzada/STASIS_Control
DOI: 10.15468/dl.8q6wfd
title: Occurrence Download

description: A dataset containing 1273 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Psychotria elata",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1273 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004723-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.95tm4b
title: Occurrence Download

description: A dataset containing 729 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Platyrrhinus umbratus"
 ]
}

 The dataset includes 729 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004724-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17693461
title: IEC-IEEE 62704-1 spatial averaging SAR algorithm

description: Open source software repository created within the STASIS project (https://www.ptb.de/stasis/).

More information here:  https://github.com/umbertozanovello/IEC-IEEE-62704-1-spatial-average-SAR
DOI: 10.5281/zenodo.17693462
title: IEC-IEEE 62704-1 spatial averaging SAR algorithm

description: Open source software repository created within the STASIS project (https://www.ptb.de/stasis/).

More information here:  https://github.com/umbertozanovello/IEC-IEEE-62704-1-spatial-average-SAR
DOI: 10.15468/dl.kkj5gc
title: Occurrence Download

description: A dataset containing 5033 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-14.52154 58.34059,-19.55567 45.02329,-7.35758 29.4004,35.23894 29.23158,46.46893 34.80919,27.88136 67.20996,21.87912 67.05949,13.1662 64.68704,-14.52154 58.34059))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mytilus galloprovincialis",
 "Year 2000-2025"
 ]
}

 The dataset includes 5033 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004727-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tywyj6
title: Occurrence Download

description: A dataset containing 4513 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pontoscolex"
 ]
}

 The dataset includes 4513 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004728-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.st3nrv16
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.15468/dl.wn398y
title: Occurrence Download

description: A dataset containing 3915 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heloderma suspectum"
 ]
}

 The dataset includes 3915 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004729-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17648887
title: ADB MRIO Panel Data (2000-2023) for adbmrio R Package

description: ADB Multi-Regional Input-Output (MRIO) Harmonized Master Panel (2000–2023)

Description: This dataset contains a pre-processed and harmonized version of the Asian Development Bank (ADB) Multi-Regional Input-Output (MRIO) tables. It unifies historical MRIO data with the latest environmentally extended tables into a single, consistent R list object.

The data covers 63 economies and 35 sectors in constant prices, enabling consistent time-series analysis of global value chains (GVCs).

Time Coverage: The dataset contains 17 specific years spanning the 2000–2023 period:





Data: 2000, 2007, 2008, 2009, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023(Note: Years 2001–2006 and 2010 are not available).



Key Features:





Harmonized Timeline: Variables and matrix dimensions are fully standardized across the 17-year span (2000–2023)




Environmental Extensions:





2017–2023: Contains ADB's Environmentally Extended Multiregional Input-Output Tables (EE-MRIOTs) CO₂ emission data with both a raw emissions vector (CO2_raw) and intensity coefficients (CO2_coeff).




2000–2016: Emission-related variables contain zero-value placeholder (dummy) matrices, allowing computational code to run without interruption while returning zeros for environmental indicators in these years.






Optimized Format: Stored as a compressed .rds file utilizing sparse matrices for memory efficiency and high-performance linear algebra operations.



Purpose:This dataset is the official input for the adbmrio R package. Its structure supports Bilateral Trade Decomposition and GVC analysis following the frameworks of Zhang et al. (2017).

Technical Specifications:





File Name: ADB_MRIO_Merged_Panel_2000_2023.rds




Format: R Data Serialization (List Object)




Dimensions:





Countries (S): 63




Sectors (N): 35




Global matrix size (GN): 2,205 × 2,205





Variable Dictionary:Within mrio_panel$years[["YYYY"]], each year’s object includes:





Z: Intermediate Consumption Matrix (2,205 × 2,205)




Y: Final Demand Matrix (2,205 × 315)




X: Gross Output Vector (2,205 × 1)




VA_raw: Value Added Vector (2,205 × 1)




A: Technical Coefficient Matrix




B: Global Leontief Inverse ((I - A)^-1)




L: Domestic Leontief Inverse ((I - A_D)^-1)




CO2_raw: CO₂ Emissions Vector (real values for 2017–2023, zeros for 2000–2016)



Related Software:





R Package: https://github.com/BallavBabu/adbmrio



Data Attribution & Disclaimer:This dataset is a processed and harmonized derivative of the official ADB Multi-Regional Input-Output tables. While the code and harmonization logic are my own, the underlying economic data remains the intellectual property of the Asian Development Bank.
DOI: 10.5281/zenodo.17693487
title: IEC-IEEE 62704-1 spatial SAR averaging algorithm

description: Open source software repository created within the STASIS project (https://www.ptb.de/stasis/).

More information here: https://github.com/umbertozanovello/IEC-IEEE-62704-1-spatial-average-SAR
DOI: 10.5281/zenodo.17693488
title: IEC-IEEE 62704-1 spatial SAR averaging algorithm

description: Open source software repository created within the STASIS project (https://www.ptb.de/stasis/).

More information here: https://github.com/umbertozanovello/IEC-IEEE-62704-1-spatial-average-SAR
DOI: 10.15468/dl.3b96hx
title: Occurrence Download

description: A dataset containing 7764 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mytilus galloprovincialis",
 "HasCoordinate is true",
 "Geometry POLYGON((-14.52154 58.34059,-19.55567 45.02329,-7.35758 29.4004,35.23894 29.23158,46.46893 34.80919,27.88136 67.20996,21.87912 67.05949,13.1662 64.68704,-14.52154 58.34059))"
 ]
}

 The dataset includes 7764 records from 155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004730-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q9yk8d
title: Occurrence Download

description: A dataset containing 17125 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Living Specimen, Occurrence evidence)",
 "Geometry POLYGON((20 -60,146 -60,146 32,20 32,20 -60))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Elapidae",
 "Year 2000-2014"
 ]
}

 The dataset includes 17125 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004731-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13843537
title: ERC-20 Permit Drainer Scam Analysis
DOI: 10.15468/dl.9dbspa
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heteromys oresterus"
 ]
}

 The dataset includes 60 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004732-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tpnkqv
title: Occurrence Download

description: A dataset containing 1114 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Equus grevyi"
 ]
}

 The dataset includes 1114 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004733-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13843539
title: ERC-20 Permit Drainer Scam Analysis
DOI: 10.6084/m9.figshare.29043155
title: <b>GeoXCP: Uncertainty Quantification in Spatial Explanations from Explainable AI</b>

description: This repository contains the code and data used in our submitted work <i>“</i><b>GeoXCP: Uncertainty Quantification in Spatial Explanations from Explainable AI</b><i>.”</i>In this study, we propose <b>GeoXCP</b>, a <b>model-agnostic framework</b> for quantifying uncertainty in geospatial explanations derived from explainable AI (XAI) methods.We validate the effectiveness of GeoXCP through a case study on housing price prediction. The provided files are organized as follows:<br>📁<b> 1-model</b>Contains the core implementation of <b>GeoXCP</b>, including the following modules:• GeoConformal• GeoConformalizedExplainer<br>📁<b> 2-data_Housing_price</b>Includes the housing price dataset for Seattle:• seattle_sample_3k.csv<br>📁<b> 3-codes_Housing_price</b>Contains code used for applying GeoXCP to the housing price prediction task:• SeattleHomeSaleUncertaintyGeoX.ipynb: End-to-end implementation for uncertainty quantification using GeoXCP. All results from the main experiment can be reproduced here.• seattle_house_price_coverage_bootstrap.ipynb: Demonstrates how GeoXCP achieves reliable coverage through bootstrap-based validation.📁<b> 4-codes_simulation_study</b>Contains code used for applying GeoXCP to the simulated dataset:• simulation_study_linear_SHAP.ipynb: Evaluate GeoXCP with linear function and SHAP.• simulation_study_nonlinear_SHAP.ipynb: Evaluate GeoXCP with nonlinear function and SHAP.• simulation_study_linear_LIME.ipynb: Evaluate GeoXCP with linear function and LIME.<br><br><br>
DOI: 10.6084/m9.figshare.29043155.v2
title: <b>GeoXCP: Uncertainty Quantification in Spatial Explanations from Explainable AI</b>

description: This repository contains the code and data used in our submitted work <i>“</i><b>GeoXCP: Uncertainty Quantification in Spatial Explanations from Explainable AI</b><i>.”</i>In this study, we propose <b>GeoXCP</b>, a <b>model-agnostic framework</b> for quantifying uncertainty in geospatial explanations derived from explainable AI (XAI) methods.We validate the effectiveness of GeoXCP through a case study on housing price prediction. The provided files are organized as follows:<br>📁<b> 1-model</b>Contains the core implementation of <b>GeoXCP</b>, including the following modules:• GeoConformal• GeoConformalizedExplainer<br>📁<b> 2-data_Housing_price</b>Includes the housing price dataset for Seattle:• seattle_sample_3k.csv<br>📁<b> 3-codes_Housing_price</b>Contains code used for applying GeoXCP to the housing price prediction task:• SeattleHomeSaleUncertaintyGeoX.ipynb: End-to-end implementation for uncertainty quantification using GeoXCP. All results from the main experiment can be reproduced here.• seattle_house_price_coverage_bootstrap.ipynb: Demonstrates how GeoXCP achieves reliable coverage through bootstrap-based validation.📁<b> 4-codes_simulation_study</b>Contains code used for applying GeoXCP to the simulated dataset:• simulation_study_linear_SHAP.ipynb: Evaluate GeoXCP with linear function and SHAP.• simulation_study_nonlinear_SHAP.ipynb: Evaluate GeoXCP with nonlinear function and SHAP.• simulation_study_linear_LIME.ipynb: Evaluate GeoXCP with linear function and LIME.<br><br><br>
DOI: 10.6084/m9.figshare.29043155.v1
title: <b>GeoXCP: Uncertainty Quantification in Spatial Explanations from Explainable AI</b>

description: This repository contains the code and data used in our submitted work <i>“</i><b>GeoXCP: Uncertainty Quantification in Spatial Explanations from Explainable AI</b><i>.”</i>In this study, we propose <b>GeoXCP</b>, a <b>model-agnostic framework</b> for quantifying uncertainty in geospatial explanations derived from explainable AI (XAI) methods.We validate the effectiveness of GeoXCP through a case study on housing price prediction. The provided files are organized as follows:<br>📁<b> 1-model</b>Contains the core implementation of <b>GeoXCP</b>, including the following modules:• GeoConformal• GeoConformalizedExplainer<br>📁<b> 2-data_Housing_price</b>Includes the housing price dataset for Seattle:• seattle_sample_3k.csv<br>📁<b> 3-codes_Housing_price</b>Contains code used for applying GeoXCP to the housing price prediction task:• SeattleHomeSaleUncertaintyGeoX.ipynb: End-to-end implementation for uncertainty quantification using GeoXCP. All results from the main experiment can be reproduced here.• seattle_house_price_coverage_bootstrap.ipynb: Demonstrates how GeoXCP achieves reliable coverage through bootstrap-based validation.📁<b> 4-codes_simulation_study</b>Contains code used for applying GeoXCP to the simulated dataset:• simulation_study_linear_SHAP.ipynb: Evaluate GeoXCP with linear function and SHAP.• simulation_study_nonlinear_SHAP.ipynb: Evaluate GeoXCP with nonlinear function and SHAP.• simulation_study_linear_LIME.ipynb: Evaluate GeoXCP with linear function and LIME.<br><br><br>
DOI: 10.15468/dl.d5gx5p
title: Occurrence Download

description: A dataset containing 1928 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-5000.0",
 "Geometry POLYGON((-3.16118 37.24247,-2.97424 37.24247,-2.97424 37.37249,-3.16118 37.37249,-3.16118 37.24247))",
 "HasGeospatialIssue is false",
 "TaxonKey is Chordata"
 ]
}

 The dataset includes 1928 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004736-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ad8t95
title: Occurrence Download

description: A dataset containing 20516 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Equus quagga"
 ]
}

 The dataset includes 20516 records from 113 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004734-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dqm3ck
title: Occurrence Download

description: A dataset containing 1617 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Equus zebra"
 ]
}

 The dataset includes 1617 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004737-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uz9x6t
title: Occurrence Download

description: A dataset containing 266 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is one of (Brazil, Peru, Bolivia (Plurinational State of), Colombia)",
 "PublishingOrg is one of (c0d9c91a-0f83-4819-bf7e-bb5e1184eb6b, c461e0ec-aaa8-4601-bae9-4b08d6e804f1, b081673d-44bb-437a-8e69-750950afa49c, d073b448-6b80-4364-8c38-18d08b88797f, bc092ff0-02e4-11dc-991f-b8a03c50a862, e6c2fab8-8716-48b8-ae62-dacbc73b35b5, 192a9ab0-5565-11d8-b290-b8a03c50a862, 6cfcf69e-9ec1-48e1-be34-5201bdb63bd7, 658207e3-b01f-4007-b14d-64b13022cd5a, eac88d99-9f6c-4031-8fc4-8088f0e0dfe7)",
 "TaxonKey is Scoloplax"
 ]
}

 The dataset includes 266 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004738-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yqx8je
title: Occurrence Download

description: A dataset containing 4665 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oryx gazella"
 ]
}

 The dataset includes 4665 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004739-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17666701
title: ExactCN: Predicting Exact Copy Numbers on Whole Exome Sequencing Data

description: The quantification of the precise copy number variations (CNVs) is crucial to understandingthe effects of gene dosage, disease severity, and therapeutic response. Although whole-exome sequencing(WES) offers a cost-effective solution for CNV detection in a clinical setting, it introduces several biases,including those related to sequence length, GC content, and the use of targeting probes. Consequently,estimating exact copy numbers remains challenging, especially for WES data. Here, we present Ex-actCN, a deep learning–based method for estimation of exact copy numbers from WES data per exon.The architecture integrates convolutional layers that extract local read-depth patterns with transformerencoder blocks that capture genomic context and handle sequencing noise. ExactCN is trained on WESsamples from the 1000 Genomes Project, using matching WGS-based calls as semi–ground truth. Inbenchmarks, ExactCN improves the state-of-the-art integer CNV calling performance by reducing themacro-averaged mean absolute error (MAE) from 0.91 to 0.62 and the macro-averaged root meansquared error (RMSE) from 1.31 to 0.78. It also achieves an overall Pearson correlation of 0.669 andSpearman correlation of 0.550, improving the second-best method by 0.641 and 0.482, respectively.Furthermore, a fine-tuned version of ExactCN demonstrated an overall F1-score of 0.657 for ag-gregate CNV detection performance on the clinically important duplicated genes SMN1/2, demon-strating its applicability to both research and clinical genomic analyses.
DOI: 10.15468/dl.j8xs4e
title: Occurrence Download

description: A dataset containing 1109394 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Belgium, Germany, Netherlands)",
 "TaxonKey is one of (Ondatra zibethicus, Myocastor coypus)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1109394 records from 79 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004725-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13843548
title: Peer-to-Peer Boliviano (BOB) Exchange Data