cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e240270
title: Digital Data for Land and climate change in Mexico and Texas reveals small effects on migratory habitat of monarch butterflies (Danaus plexippus).

description: The decline of the iconic monarch butterfly in North America has motivated research on the impacts of land use and land cover (LULC) change and climate variability on monarch habitat and population dynamics. We investigated spring and fall trends in LULC, milkweed and nectar resources over a 20-year period, and ~30 years of climate variables in Mexico and Texas, a key region supporting spring and fall migration during monarchs annual life cycle. We estimated a 2.9% decline in milkweed in Texas, but little to no change in Mexico. Fall and spring nectar resources declined &lt;1% in both countries. Vegetation greenness increased in both the fall and spring in Mexico while the other climate variables, for both countries, did not. Monarch habitat in Mexico and Texas appears relatively more intact than in other parts of their range, particularly the midwestern agricultural landscapes of the US. Given the relatively modest observed changes, it seems unlikely that habitat loss (quantity and quality) in Mexico and Texas has caused large declines in population size or survival during migration.
DOI: 10.3886/e240270v1
title: Digital Data for Land and climate change in Mexico and Texas reveals small effects on migratory habitat of monarch butterflies (Danaus plexippus).

description: The decline of the iconic monarch butterfly in North America has motivated research on the impacts of land use and land cover (LULC) change and climate variability on monarch habitat and population dynamics. We investigated spring and fall trends in LULC, milkweed and nectar resources over a 20-year period, and ~30 years of climate variables in Mexico and Texas, a key region supporting spring and fall migration during monarchs annual life cycle. We estimated a 2.9% decline in milkweed in Texas, but little to no change in Mexico. Fall and spring nectar resources declined &lt;1% in both countries. Vegetation greenness increased in both the fall and spring in Mexico while the other climate variables, for both countries, did not. Monarch habitat in Mexico and Texas appears relatively more intact than in other parts of their range, particularly the midwestern agricultural landscapes of the US. Given the relatively modest observed changes, it seems unlikely that habitat loss (quantity and quality) in Mexico and Texas has caused large declines in population size or survival during migration.
DOI: 10.15468/dl.xk3cku
title: Occurrence Download

description: A dataset containing 72 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-76.55977 40.07296,-65.70357 40.07296,-65.70357 47.45331,-76.55977 47.45331,-76.55977 40.07296))",
 "HasGeospatialIssue is false",
 "PublishingOrg is one of (493fe050-055d-11d8-b84f-b8a03c50a862, baa8ccb1-8d9d-4940-93a6-2241c8a04ba5)",
 "TaxonKey is Pekania pennanti"
 ]
}

 The dataset includes 72 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031652-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25326/jedh-sd22
title: water+dust + None - Cloud study - 2011-03-25

description: Publication of the dataset
DOI: 10.15468/dl.9xfv6r
title: Occurrence Download

description: A dataset containing 41627 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 Agrostis canina L.",
 "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 41627 records from 201 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010667-250525065834625/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/11885023
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.7wx2p5
title: Occurrence Download

description: A dataset containing 4331 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Türkiye",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Emberiza cia Linnaeus, 1767"
 ]
}

 The dataset includes 4331 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015965-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15579070
title: TА'LIMDА KREАTIV YONDАSHUV АSOSIDА TАLАBАLАRGА XАLQ АMАLIY SАN'АTINI O'RGАTIShNI RIVOJLАNTIRISHNING NАZАRIY MАSАLАLАRI
DOI: 10.5281/zenodo.15579071
title: TА'LIMDА KREАTIV YONDАSHUV АSOSIDА TАLАBАLАRGА XАLQ АMАLIY SАN'АTINI O'RGАTIShNI RIVOJLАNTIRISHNING NАZАRIY MАSАLАLАRI
DOI: 10.15468/dl.m27fc6
title: Occurrence Download

description: A dataset containing 14392 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Dataset of walvisstrandingen_nl"
 ]
}

 The dataset includes 14392 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001485-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4vv7v6
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Historical strandings of cetaceans on the Portuguese coast"
 ]
}

 The dataset includes 38 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001487-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ahn9zp
title: Occurrence Download

description: A dataset containing 217 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Romanzoffia thompsonii)"
 ]
}

 The dataset includes 217 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031654-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14974607
title: On the importance of soil texture for predicting future global soil respiration

description: Dear researchers and interested parties,

We are pleased to announce the publication of our recent research on Zenodo, which explores the role of soil texture in predicting future global soil respiration under climate change scenarios. Using a machine learning approach, we provide high-resolution (1 km) spatial models estimating total and heterotrophic soil respiration (Rs and Rh) up to the year 2100 across different Shared Socioeconomic Pathways (SSPs). Our findings highlight the importance of incorporating soil texture to improve the accuracy of soil carbon-climate feedback projections.

Available resources:

Soil respiration predictions:

We provide spatially explicit estimates of Rs and Rh, including associated uncertainties, under multiple IPCC SSP scenarios. The dataset includes mean values (g C m<sup>-2</sup> year<sup>-1</sup>) and coefficients of variation (%). All maps are available in "tif" format, using the Goode Homolosine - Land projection system (ESRI:54052).

Open-Source Code and Data: The entire analytical workflow, developed in R, is accessible through our GitHub repository, ensuring full reproducibility and transparency. Additional methodological details can be found in our publication:

Gomes, L., Moquedace, C. M., Souza, I. F., Vesterdal, L., Veloso, G. V., Francelino, M. R., Schaefer, C., Morris, K. A., Vargas, R., Bond-Lamberty, B., & Fernandes Filho, E. I. (2025). On the importance of soil texture for predicting future global soil respiration. Science of the Total Environment, xx(x), xx. DOI: 10.1016/xxxxxxxxxxx.

Availability objectives:

Advancing scientific collaboration:

We invite researchers and organizations to utilize our dataset to enhance studies on soil carbon dynamics and climate change feedbacks.

Supporting environmental understanding:

By providing open access to these models, we aim to contribute to the global understanding of future soil carbon fluxes and support the development of more accurate climate projections.

Fostering innovation:

Sharing our results and methodologies aims to stimulate advancements in spatial modeling and predictive capabilities for soil respiration under changing climatic conditions.

We appreciate your interest and collaboration. We look forward to fostering scientific exchange and contributing to a more comprehensive understanding of soil-climate interactions in the 21st century.
DOI: 10.5281/zenodo.14974608
title: On the importance of soil texture for predicting future global soil respiration

description: Dear researchers and interested parties,

We are pleased to announce the publication of our recent research on Zenodo, which explores the role of soil texture in predicting future global soil respiration under climate change scenarios. Using a machine learning approach, we provide high-resolution (1 km) spatial models estimating total and heterotrophic soil respiration (Rs and Rh) up to the year 2100 across different Shared Socioeconomic Pathways (SSPs). Our findings highlight the importance of incorporating soil texture to improve the accuracy of soil carbon-climate feedback projections.

Available resources:

Soil respiration predictions:

We provide spatially explicit estimates of Rs and Rh, including associated uncertainties, under multiple IPCC SSP scenarios. The dataset includes mean values (g C m<sup>-2</sup> year<sup>-1</sup>) and coefficients of variation (%). All maps are available in "tif" format, using the Goode Homolosine - Land projection system (ESRI:54052).

Open-Source Code and Data: The entire analytical workflow, developed in R, is accessible through our GitHub repository, ensuring full reproducibility and transparency. Additional methodological details can be found in our publication:

Gomes, L., Moquedace, C. M., Souza, I. F., Vesterdal, L., Veloso, G. V., Francelino, M. R., Schaefer, C., Morris, K. A., Vargas, R., Bond-Lamberty, B., & Fernandes Filho, E. I. (2025). On the importance of soil texture for predicting future global soil respiration. Science of the Total Environment, xx(x), xx. DOI: 10.1016/xxxxxxxxxxx.

Availability objectives:

Advancing scientific collaboration:

We invite researchers and organizations to utilize our dataset to enhance studies on soil carbon dynamics and climate change feedbacks.

Supporting environmental understanding:

By providing open access to these models, we aim to contribute to the global understanding of future soil carbon fluxes and support the development of more accurate climate projections.

Fostering innovation:

Sharing our results and methodologies aims to stimulate advancements in spatial modeling and predictive capabilities for soil respiration under changing climatic conditions.

We appreciate your interest and collaboration. We look forward to fostering scientific exchange and contributing to a more comprehensive understanding of soil-climate interactions in the 21st century.
DOI: 10.15468/dl.cgxdwm
title: Occurrence Download

description: A dataset containing 1004 species occurrences available 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 (Alouatta [GBIF Backbone Taxonomy])",
 "Year 2023-2025"
 ]
 }
 ]
}

 The dataset includes 1004 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031655-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t6adjd
title: Occurrence Download

description: A dataset containing 4521 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen, Material citation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Quiscalus mexicanus",
 "Year 1000-2024"
 ]
}

 The dataset includes 4521 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031656-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e228443
title: Data and code for: Robot Hubs and the Use of Robotics in US Manufacturing Establishments

description: In this paper, we present results on the distribution of robots in US manufacturing by establishment characteristics using new establishment-level data collected by the US Census Bureau’s ASM for reference year 2018. This establishment-level analysis of the use of robots in US manufacturing complements results on “robot hubs” previously reported in Brynjolfsson et al. (2023a). We find that establishments with robots tend to have a larger share of production workers, lower average wages, and lower labor share, controlling for industry, age, and size. We also find that establishments with robots tend to be located in areas with other robot-adopting establishments, controlling for industry, location, size, and age. These results suggest that there may be peer effects in robot adoption, which may stem from learning or other agglomeration effects. Given the cross-sectional nature of the data—the data are currently available for 2018, but additional years will soon be available—the correlations we observe are not necessarily causal. Nevertheless, the patterns in the data provide useful information about the distribution of robots across establishments. The patterns we find raise multiple questions for future research. For example, does the minimum efficient scale for robot adoption vary with establishment characteristics? What factors lead to agglomeration effects; is it the presence of specialized human capital, important third parties such as robotics integrators, or some type of learning across establishments and firms? Our hope is that the patterns in the data that we document in our paper spark further research in this area and are of use to scholars, practitioners, and policymakers.
DOI: 10.3886/e228443v1
title: Data and code for: Robot Hubs and the Use of Robotics in US Manufacturing Establishments

description: In this paper, we present results on the distribution of robots in US manufacturing by establishment characteristics using new establishment-level data collected by the US Census Bureau’s ASM for reference year 2018. This establishment-level analysis of the use of robots in US manufacturing complements results on “robot hubs” previously reported in Brynjolfsson et al. (2023a). We find that establishments with robots tend to have a larger share of production workers, lower average wages, and lower labor share, controlling for industry, age, and size. We also find that establishments with robots tend to be located in areas with other robot-adopting establishments, controlling for industry, location, size, and age. These results suggest that there may be peer effects in robot adoption, which may stem from learning or other agglomeration effects. Given the cross-sectional nature of the data—the data are currently available for 2018, but additional years will soon be available—the correlations we observe are not necessarily causal. Nevertheless, the patterns in the data provide useful information about the distribution of robots across establishments. The patterns we find raise multiple questions for future research. For example, does the minimum efficient scale for robot adoption vary with establishment characteristics? What factors lead to agglomeration effects; is it the presence of specialized human capital, important third parties such as robotics integrators, or some type of learning across establishments and firms? Our hope is that the patterns in the data that we document in our paper spark further research in this area and are of use to scholars, practitioners, and policymakers.
DOI: 10.5281/zenodo.17603600
title: Adaptive surface sensing enables mammalian sperm navigation in complex environment
DOI: 10.5281/zenodo.17603601
title: Adaptive surface sensing enables mammalian sperm navigation in complex environment
DOI: 10.15468/dl.a38ms4
title: Occurrence Download

description: A dataset containing 11036 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinus albicaulis"
 ]
}

 The dataset includes 11036 records from 151 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031659-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rydwzw
title: Occurrence Download

description: A dataset containing 131 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Issue is one of (Recorded date invalid, Multimedia URI invalid, Type status invalid, Elevation min max swapped, Geodetic datum invalid)",
 "TaxonKey is Encholirium spectabile"
 ]
}

 The dataset includes 131 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031660-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5pn8xv15
title: MassIVE MSV000099893 - 20251113_Dataset_2022MCF0011_Retina_GroundSquirrel
title: untargeted metabolomics data of retina tissues from thirteen-lined ground squirrel in positive and negative mode.
DOI: 10.5281/zenodo.17050388
title: Supplementary Excel File for "Optimal Recursive Algorithm for Moving Linear Regression"

description: This Excel file contains the implementation and simulation of the Optimal Recursive Algorithm for Moving Linear Regression. It includes all formulas, input parameters, and output results used to validate the algorithm's performance. The file is provided as supplementary material to the preprint titled "Optimal Recursive Algorithm for Moving Linear Regression", and supports full reproducibility of the numerical analysis.
DOI: 10.5281/zenodo.17050389
title: Supplementary Excel File for "Optimal Recursive Algorithm for Moving Linear Regression"

description: This Excel file contains the implementation and simulation of the Optimal Recursive Algorithm for Moving Linear Regression. It includes all formulas, input parameters, and output results used to validate the algorithm's performance. The file is provided as supplementary material to the preprint titled "Optimal Recursive Algorithm for Moving Linear Regression", and supports full reproducibility of the numerical analysis.
DOI: 10.15468/dl.xg4bm9
title: Occurrence Download

description: A dataset containing 1766178 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vulpes"
 ]
}

 The dataset includes 1766178 records from 4106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031638-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.6568195
title: Development of Deep Convolutional Neural Network Ensemble Models for 36-Month ENSO Forecasts (Datasets)

description: Processed input and output reanalysis datasets used used for evaluating the models' performances. Input dataset is used to feed the AI models for the ENSO forecast. Output dataset is used to evaluate the models' ENSO forecast performance.
description: * Corresponding Author
DOI: 10.5281/zenodo.6568196
title: Development of Deep Convolutional Neural Network Ensemble Models for 36-Month ENSO Forecasts (Datasets)

description: Processed input and output reanalysis datasets used used for evaluating the models' performances. Input dataset is used to feed the AI models for the ENSO forecast. Output dataset is used to evaluate the models' ENSO forecast performance.
description: * Corresponding Author
DOI: 10.15468/dl.bqdg7v
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Rubus palmensis A.Hansen"
 ]
}

 The dataset includes 37 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007214-250525065834625/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.30479519
title: The binaries for PLocator dataset, paper accepted by ToSEM'25

description: The PLocator dataset includes a collection of binaries that are essential for vulnerability detection tasks. These binaries are sourced from various open-source projects and are organized by architecture, optimization levels, and versioning to facilitate detailed analysis. The dataset contains both <b>original</b> and <b>stripped</b> versions of the binaries.For more details, please refer to Github
DOI: 10.6084/m9.figshare.30479519.v2
title: The binaries for PLocator dataset, paper accepted by ToSEM'25

description: The PLocator dataset includes a collection of binaries that are essential for vulnerability detection tasks. These binaries are sourced from various open-source projects and are organized by architecture, optimization levels, and versioning to facilitate detailed analysis. The dataset contains both <b>original</b> and <b>stripped</b> versions of the binaries.For more details, please refer to Github
DOI: 10.6084/m9.figshare.30479519.v1
title: The binaries for PLocator dataset

description: The PLocator dataset includes a collection of binaries that are essential for vulnerability detection tasks. These binaries are sourced from various open-source projects and are organized by architecture, optimization levels, and versioning to facilitate detailed analysis. The dataset contains both <b>original</b> and <b>stripped</b> versions of the binaries.
DOI: 10.4121/39942904-6bf2-45cf-9811-454beb584e34
title: Data underlying the publication: RCNet: A Hybrid Framework for PolSAR Image Classification via Real-Complex Polarimetric Feature Fusion

description: Three benchmark PolSAR datasets were utilized to assess the performance of the proposed method in this study. Oberpfaffenhofen, San Francisco and Barnaul. The first dataset was acquired by the L-band PolSAR data from the E-SAR sensor, capturing the Oberpfaffenhofen area in Germany with a size of 1200 × 1300 pixels, and spatial resolution is 3 × 3 meters. The primary land cover types in this region include dense urban areas, forests, and some regions without artificial markings. The secondary dataset was collected via the airborne L-band PolSAR sensor aboard NASA/JPL's AIRSAR platform, covering the San Francisco region This dataset exhibits a spatial extent of 1024 × 900 pixels with 10-meter resolution, encompassing diverse land cover categories including urban zones, vegetated areas, rocky terrain, oceanic surfaces, and exposed landforms.The third dataset is from China’s Gaofen-3 satellitecollected on May 5, 2018. The multi-look data consists of 1474 × 1310 pixels. The observed area includes various land cover types such as urban areas, forests, farmland.These datasets are provided in this repository.