cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17235586
title: Data to support downscaling regional economic activity

description: This is a collection of open datasets that provide indicators, covariates and validation of downscaled (global) regional economic activity, processed to Parquet format for ease of access directly on cloud object storage.

References



International Food Policy Research Institute (IFPRI), 2024. Global Spatially-Disaggregated Crop Production Statistics Data for 2020 Version 1.0. https://doi.org/10.7910/DVN/SWPENT

Ru, Y., Blankespoor, B., Wood-Sichra, U., Thomas, T.S., You, L., Kalvelagen, E., 2023. Estimating local agricultural gross domestic product (AgGDP) across the world. Earth System Science Data 15, 1357-1387. https://doi.org/10.5194/essd-15-1357-2023

Maus, V., Giljum, S., Gutschlhofer, J., Da Silva, D.M., Probst, M., Gass, S.L.B., Luckeneder, S., Lieber, M., McCallum, I., 2020. A global-scale data set of mining areas. Sci Data 7, 289. https://doi.org/10.1038/s41597-020-00624-w

Global Integrated Power Tracker, Global Energy Monitor, September 2024 release. Available at: https://globalenergymonitor.org/projects/global-integrated-power-tracker/

Kummu, M., Kosonen, M. & Masoumzadeh Sayyar, S. 2025. Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022. Scientific Data 12: 178. https://doi.org/10.1038/s41597-025-04487-x

Copernicus Global Dynamic Land Cover. European Union's Copernicus Land Monitoring Service information. https://land.copernicus.eu/en/products/global-dynamic-land-cover

Schiavina, M., Melchiorri, M., Pesaresi, M., Politis, P., Carneiro Freire, S.M., Maffenini, L., Florio, P., Ehrlich, D., Goch, K., Carioli, A., Uhl, J., Tommasi, P. and Kemper, T., GHSL Data Package 2023, Publications Office of the European Union, Luxembourg, 2023, ISBN 978-92-68-02341-9, doi:10.2760/098587, JRC133256.

Overture Maps Foundation. 2024. Overture Maps Places v2024-10-23.0 Available at: overturemaps.org

Schlosser, I., Russell, T. 2025. DOSE-WDI v0.3 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17235586

Wenz, L., Carr, R.D., Kögel, N. et al. 2023. DOSE – Global data set of reported sub-national economic output. Sci Data 10, 425. https://doi.org/10.1038/s41597-023-02323-8

World Bank. 2024. World Development Indicators.Available online at: https://datatopics.worldbank.org/world-development-indicators/
description: Funding

This research received funding from the FCDO Climate Compatible Growth Programme.

Disclaimer

The views expressed in this website do not necessarily reflect the UK government's official policies.
DOI: 10.15468/dl.u2q66u
title: Occurrence Download

description: A dataset containing 7151 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Neopomacentrus"
 ]
}

 The dataset includes 7151 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033484-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n5hyrp
title: Occurrence Download

description: A dataset containing 47533 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Europe",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (January, February, March, April, May, June, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Zootoca vivipara, Lacerta vivipara, Lacerta vivipara vivipara)",
 "Year 1960-2025"
 ]
}

 The dataset includes 47533 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033485-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s976z5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pternistis hartlaubi",
 "Country is one of (Brunei Darussalam, Indonesia, Philippines, Timor-Leste, Singapore)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033488-250920141307145/datasets/export for details.
DOI: 10.15468/dl.urq5vp
title: Occurrence Download

description: A dataset listing the 8397 species recorded in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "TaxonKey is one of (Animalia [GBIF Backbone Taxonomy], Chordata [GBIF Backbone Taxonomy])",
 "Country is one of (Colombia)",
 "StateProvince is one of (Santander)"
 ]
 }
 ]
}

 The dataset's 8397 records were derived from 412 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033442-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pnfcwm
title: Occurrence Download

description: A dataset listing the 500 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Greece",
 "DatasetKey is EOD – eBird Observation Dataset",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset's 500 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033459-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a5pvac
title: Occurrence Download

description: A dataset containing 894 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is one of (Peru, India)",
 "TaxonKey is Zaratornis stresemanni"
 ]
}

 The dataset includes 894 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033490-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17233601
title: Data for "Streamline Controlled Rectification of Supercurrent in Thin-Film Asymmetric Weak Links"

description: This repository contains the experimental data supporting the figures presented in the paper "Streamline Controlled Rectification of Supercurrent in Thin-Film Asymmetric Weak Links".The data are organized according to the figure numbering in the main text. Each file or folder corresponds to one figure and includes a header (first line, commented out) indicating which physical quantity or measurement is represented in each column.Further details on the context and interpretation of the data can be found in the corresponding figure captions in the paper.
DOI: 10.5281/zenodo.17233600
title: Data for "Streamline Controlled Rectification of Supercurrent in Thin-Film Asymmetric Weak Links"

description: This repository contains the experimental data supporting the figures presented in the paper "Streamline Controlled Rectification of Supercurrent in Thin-Film Asymmetric Weak Links".The data are organized according to the figure numbering in the main text. Each file or folder corresponds to one figure and includes a header (first line, commented out) indicating which physical quantity or measurement is represented in each column.Further details on the context and interpretation of the data can be found in the corresponding figure captions in the paper.
DOI: 10.5066/p14ghtae
title: Hydrology data from selected Louisiana freshwater forested wetlands 2004-2022

description: Infinity water level recorders were installed in 2004 to monitor hydrologic conditions in five freshwater forested wetlands in Louisiana: Bayou Chevreuil, Bayou Teche National Wildlife Refuge, Mandalay National Wildlife Refuge, Jean Lafitte National and Historical Park, and Treasure Island. Hourly measurements were collected continuously on a 24- hour basis through April 2022, producing an extensive record of wetland hydrology. The dataset shows daily changes, flooding, and long-term shifts in water levels across the sites. It also provides a foundation for evaluating the impacts of climate variability and extreme events on Louisiana’s freshwater ecosystems.
DOI: 10.15468/dl.f2c9u2
title: Occurrence Download

description: A dataset containing 10710 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-5.24482 36.33216,-3.59107 36.33216,-3.59107 37.37777,-5.24482 37.37777,-5.24482 36.33216))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Gyps fulvus"
 ]
}

 The dataset includes 10710 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033491-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9nd61hi
title: Surveyed coordinates and elevations in a 2020 resurvey of previously established cross sections on the Green River between Jensen and Ouray, Utah

description: These data were compiled for/to National Park Service and Upper Colorado River Endangered Fish Recovery Program. Objective(s) of our study were to resurvey previously established cross sections on the Green River, Utah, in, and upstream, from Ouray National Wildlife Refuge to document changes in channel width, depth, and cross-sectional area. These data represent the 2020 resurvey coordinates and elevations of previously established cross sections (1994 - 1996) on the Green River. These data were collected in the Green River between Jensen and Ouray, Utah from October 11-16, 2020. These data were collected by Ronald Griffiths and Joel Unema from the U.S. Geological Survey using real-time kinematic (RTK) GPS positioning. These data can be used to locate previously surveyed cross sections compute channel changes.
DOI: 10.6084/m9.figshare.29787788
title: Debiased Calibration Estimation Using Generalized Entropy in Survey Sampling

description: Incorporating auxiliary information into the survey estimation is a fundamental problem in survey sampling. Calibration weighting is a widely used technique to integrate such information by adjusting design weights to meet benchmarking constraints. Traditional methods, such as those proposed by <i>Deville and Särndal</i>, solve this problem by minimizing a distance between calibrated and design weights. In this article, we propose a novel calibration framework that instead maximizes a generalized entropy function subject to two constraints: a benchmarking constraint to improve efficiency and a debiasing constraint involving design weights to ensure design consistency. This approach avoids placing design weights in the objective function and instead incorporates them through the constraint structure. We establish the asymptotic properties of the proposed estimator, including design consistency and asymptotic normality, and demonstrate that under Poisson sampling, a specific contrast-entropy function minimizes the asymptotic variance among a broad class of entropy functions. Simulation studies and an empirical application to agricultural survey data illustrate the advantages of our method, particularly in the presence of model misspecification or informative sampling designs. We demonstrate a real-life application using agricultural survey data collected from Kynetec, Inc. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29787788.v2
title: Debiased Calibration Estimation Using Generalized Entropy in Survey Sampling

description: Incorporating auxiliary information into the survey estimation is a fundamental problem in survey sampling. Calibration weighting is a widely used technique to integrate such information by adjusting design weights to meet benchmarking constraints. Traditional methods, such as those proposed by <i>Deville and Särndal</i>, solve this problem by minimizing a distance between calibrated and design weights. In this article, we propose a novel calibration framework that instead maximizes a generalized entropy function subject to two constraints: a benchmarking constraint to improve efficiency and a debiasing constraint involving design weights to ensure design consistency. This approach avoids placing design weights in the objective function and instead incorporates them through the constraint structure. We establish the asymptotic properties of the proposed estimator, including design consistency and asymptotic normality, and demonstrate that under Poisson sampling, a specific contrast-entropy function minimizes the asymptotic variance among a broad class of entropy functions. Simulation studies and an empirical application to agricultural survey data illustrate the advantages of our method, particularly in the presence of model misspecification or informative sampling designs. We demonstrate a real-life application using agricultural survey data collected from Kynetec, Inc. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29787788.v1
title: Debiased calibration estimation using generalized entropy in survey sampling

description: Incorporating auxiliary information into the survey estimation is a fundamental problem in survey sampling. Calibration weighting is a widely used technique to integrate such information by adjusting design weights to meet benchmarking constraints. Traditional methods, such as those proposed by Deville and Särndal (1992), solve this problem by minimizing a distance between calibrated and design weights. In this paper, we propose a novel calibration framework that instead maximizes a generalized entropy function subject to two constraints: a benchmarking constraint to improve efficiency and a debiasing constraint involving design weights to ensure design consistency. This approach avoids placing design weights in the objective function and instead incorporates them through the constraint structure. We establish the asymptotic properties of the proposed estimator, including design consistency and asymptotic normality, and demonstrate that under Poisson sampling, a specific contrast-entropy function minimizes the asymptotic variance among a broad class of entropy functions. Simulation studies and an empirical application to agricultural survey data illustrate the advantages of our method, particularly in the presence of model misspecification or informative sampling designs. We demonstrate a real-life application using agricultural survey data collected from Kynetec, Inc.
DOI: 10.6084/m9.figshare.29637724
title: Estimation of Out-of-Sample Sharpe Ratio for High Dimensional Portfolio Optimization

description: Portfolio optimization aims at constructing a realistic portfolio with significant out-of-sample performance, which is typically measured by the out-of-sample Sharpe ratio. However, due to in-sample optimism, it is inappropriate to use the in-sample estimated covariance to evaluate the out-of-sample Sharpe, especially in the high dimensional settings. In this article, we propose a novel method to estimate the out-of-sample Sharpe ratio using only in-sample data, based on random matrix theory. Furthermore, portfolio managers can use the estimated out-of-sample Sharpe as a criterion to decide the best tuning for constructing their portfolios. Specifically, we consider the classical framework of Markowits mean-variance portfolio optimization under high dimensional regime of p/n→c∈(0,∞), where <i>p</i> is the portfolio dimension and <i>n</i> is the number of samples or time points. We propose to correct the sample covariance by a regularization matrix and provide a consistent estimator of its Sharpe ratio. The new estimator works well under either of the following conditions: (a) bounded covariance spectrum, (b) arbitrary number of diverging spikes when c&lt;1, and (c) fixed number of diverging spikes with weak requirement on their diverging speed when c≥1. We can also extend the results to construct global minimum variance portfolio and correct out-of-sample efficient frontier. We demonstrate the effectiveness of our approach through comprehensive simulations and real data experiments. Our results highlight the potential of this methodology as a useful tool for portfolio optimization in high dimensional settings. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29637724.v1
title: Estimation of Out-of-Sample Sharpe Ratio for High Dimensional Portfolio Optimization

description: Portfolio optimization aims at constructing a realistic portfolio with significant out-of-sample performance, which is typically measured by the out-of-sample Sharpe ratio. However, due to in-sample optimism, it is inappropriate to use the in-sample estimated covariance to evaluate the out-of-sample Sharpe, especially in the high dimensional settings. In this article, we propose a novel method to estimate the out-of-sample Sharpe ratio using only in-sample data, based on random matrix theory. Furthermore, portfolio managers can use the estimated out-of-sample Sharpe as a criterion to decide the best tuning for constructing their portfolios. Specifically, we consider the classical framework of Markowits mean-variance portfolio optimization under high dimensional regime of p/n→c∈(0,∞), where <i>p</i> is the portfolio dimension and <i>n</i> is the number of samples or time points. We propose to correct the sample covariance by a regularization matrix and provide a consistent estimator of its Sharpe ratio. The new estimator works well under either of the following conditions: (a) bounded covariance spectrum, (b) arbitrary number of diverging spikes when c&lt;1, and (c) fixed number of diverging spikes with weak requirement on their diverging speed when c≥1. We can also extend the results to construct global minimum variance portfolio and correct out-of-sample efficient frontier. We demonstrate the effectiveness of our approach through comprehensive simulations and real data experiments. Our results highlight the potential of this methodology as a useful tool for portfolio optimization in high dimensional settings. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.34894/mk0msg
title: Capturing Nature Connectedness
title: Data for Validity and Utility of the Dutch Nature Connection Index

description: Data supporting the article Capturing Nature Connectedness: Validity and Utility of the Dutch Nature Connection Index, published in Environmental Psychology
DOI: 10.15468/dl.cb5cet
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Schoenoplectiella perrieri",
 "Country is Comoros",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033492-250920141307145/datasets/export for details.
DOI: 10.48361/ptbggs
title: Stable isotope ratios and elemental contents of C, N and S in benthic organisms sampled during the Belgica 121 campaign in the Gerlache Strait, West Antarctic Peninsula (2019).

description: "Stable isotope ratios and elemental contents of C, N and S in benthic organisms sampled during the Belgica 121 campaign in the Gerlache Strait, West Antarctic Peninsula (2019)" is a sampling event type dataset published by SCAR-AntOBIS. This dataset supported the elaboration of the paper entitled "Food web structure in a rapidly changing coastal environment: the West Antarctic Peninsula." by VOISIN A., LEPOINT G., DANIS B., GUILLAUMOT C., KRISTIANSEN A., PASOTTI F., SAUCÈDE T. and MICHEL L. N., submitted in the Belgian Journal of Zoology. The report (in english) is available at: 
DOI: 10.13140/RG.2.2.23850.06084This dataset contains records based on SCUBA diving collection during the Belgica121 campaign in the Gerlache Strait, along the West Antarctic Peninsula from 2019-02-23 to 2019-03-24.The data is published as a standardized Darwin Core Archive and includes for each sampling event an eventID, date, time, location, depth range when applicable, sampling protocol (in the event core). For each occurrence, an occurrenceID, eventID, status (present/absent), institution code, scientific name, taxa classification, preparations whenever applicable are specified in the occurrence extension. Each measurement associated with a sampling event or an occurrence record will have a measurementID, measurement type, measurement value and measurement unit and their corresponding identifiers if any (in the extended measurement or fact extension).This dataset is part of the RECTO and vERSO projects funded by the Belgian Science Policy Office (BELSPO project BR/154/A1/RECTO).This dataset is published as open data with technical support provided by SCAR Antarctic Biodiversity Portal (biodiversity.aq) (BELSPO project RT/23/ADVANCE). Please follow the guidelines from the SCAR Data Policy (SCAR, 2023) when using the data. Please contact us via the contact information provided in the metadata or via data-biodiversity-aq@naturalsciences.be for any inquiry about this dataset. Issues with dataset can be reported at https://github.com/biodiversity-aq/data-publication/
DOI: 10.15468/dl.py7aam
title: Occurrence Download

description: A dataset containing 175 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is AMNH Bird Collection",
 "TaxonKey is Sturnus vulgaris"
 ]
}

 The dataset includes 175 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033493-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17235969
title: Simmons Park Viewshed Analysis

description: Updated with imperial to metric conversions and other parameter changes.
DOI: 10.15468/dl.zpc9s5
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Schoenoplectiella perrieri",
 "Country is Madagascar",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033494-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zdk5zp
title: Occurrence Download

description: A dataset containing 734 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-59.50396 -38.589,-63.80746 -27.09058,-66.26053 -24.33835,-70.65182 -31.69916,-73.00517 -40.50943,-74.66373 -53.19592,-66.48735 -53.10863,-59.50396 -38.589))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (November, December, January)",
 "OccurrenceStatus is Present",
 "TaxonKey is Agriornis murinus",
 "Year 1970-2025"
 ]
}

 The dataset includes 734 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033495-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7266/03vf33zt
title: Land use projection for Perdido Bay watershed in 2025