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

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.76577 24.45966, 88.688627 24.454083, 88.61340300000001 24.437491, 88.54196899999999 24.410296, 88.476097 24.373174, 88.417419 24.327048, 88.36738099999999 24.273063, 88.327213 24.212556, 88.297895 24.147025, 88.280134 24.07809, 88.274349 24.007452, 88.280664 23.936853, 88.298903 23.868028, 88.32860100000001 23.802668, 88.369012 23.742377, 88.419134 23.688631, 88.477728 23.642744, 88.543357 23.605838, 88.614411 23.578815, 88.68915699999999 23.562333, 88.76577 23.556793, 88.842383 23.562333, 88.917129 23.578815, 88.98818300000001 23.605838, 89.05381199999999 23.642744, 89.11240599999999 23.688631, 89.16252799999999 23.742377, 89.202939 23.802668, 89.232637 23.868028, 89.25087600000001 23.936853, 89.25719100000001 24.007452, 89.251406 24.07809, 89.233645 24.147025, 89.20432700000001 24.212556, 89.164159 24.273063, 89.114121 24.327048, 89.055443 24.373174, 88.989571 24.410296, 88.918137 24.437491, 88.842913 24.454083, 88.76577 24.45966))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004605-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8utwjs
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dactyloctenium aegyptium",
 "Year 1970-2000"
 ]
}

 The dataset includes 34 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004606-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48804/kk5lds
title: Frequency response function data of a car door under various damage scenarios

description: &lt;h1&gt;Frequency Response Function data of a car door under various damage scenarios&lt;/h1&gt;

&lt;p&gt;These files contain data collected to experimentally validate damage identification methods for complex plate-like structures, using low-frequency impact testing measurements.
&lt;/p&gt;
&lt;p&gt;
A car door was chosen for its industrial relevance, 3D geometry, and large Region of Interest including areas affected by edges and other significant geometrical features which are prone to reflections and scattering.
The data is composed of a full grid of baseline measurements, which allow to define a data-driven propagation model, along with a set of sparser measurements taken in a series of damage conditions, which allow to test damage imaging using the aforementioned data-driven model.
&lt;/p&gt;
&lt;p&gt;
More information can be found in README.md
&lt;/p&gt;
description: Simcenter Testlab
DOI: 10.5281/zenodo.18287970
title: Dataset and code for "OxiMoRa: Oxidation Monitoring of Electrochemical Steel Corrosion based on Harmonic Radar and Deep Learning"

description: Dataset and code for "OxiMoRa: Oxidation Monitoring of Electrochemical Steel Corrosion based on Harmonic Radar and Deep Learning". IQ samples collected with harmonic radar and converted to grayscale images for computer vision classification. Varying quantities of corroded items (Screws) were displayed in front of the SDR-based radar, and their electromagnetic fingerprints were recorded.

 

OxiMoRa-main.zip contains all the code for reproducibility of the results.

The rest of the dataset has been split into subfiles for upload.

 

Scenario 1 - Detection has a bag of rusty screws displayed in front of a radar:

--> noise_f25_1 to noise_f25_4

--> rustbag_f25_1 to rustbag_f25_4

Scenario 2- Screws amount has different recordings with a different number of rusty screws in front of a radar. The purpose is to see if the radar can quantify to some extent the corrosion severity.

--> 5 classes, one for each number of screws, e.g., 4 screws --> 4screw_1 to 4screw_3

Scenario 3 has different settings based on pipe testing: 

Pipe_corroded are files with rusty screws inside to mimic internal corrosion.

--> Pipe_corroded_1 to Pipe_corroded_4

Pipe_empty are files for baseline knowledge of the pipe.

--> Pipe_empty_f25_1 to Pipe_empty_f25_6

Pipe_normal_nails are files where healthy screws are introduced in the pipe (galvanized steel for both the screws and the pipe)

--> Pipe_healthy_nailsf25_1 to Pipe_health_nailsf25_4
DOI: 10.5281/zenodo.18327606
title: Dataset and code for "OxiMoRa: Oxidation Monitoring of Electrochemical Steel Corrosion based on Harmonic Radar and Deep Learning"

description: Dataset and code for "OxiMoRa: Oxidation Monitoring of Electrochemical Steel Corrosion based on Harmonic Radar and Deep Learning". IQ samples collected with harmonic radar and converted to grayscale images for computer vision classification. Varying quantities of corroded items (Screws) were displayed in front of the SDR-based radar, and their electromagnetic fingerprints were recorded.

 

OxiMoRa-main.zip contains all the code for reproducibility of the results.

The rest of the dataset has been split into subfiles for upload.

 

Scenario 1 - Detection has a bag of rusty screws displayed in front of a radar:

--> noise_f25_1 to noise_f25_4

--> rustbag_f25_1 to rustbag_f25_4

Scenario 2- Screws amount has different recordings with a different number of rusty screws in front of a radar. The purpose is to see if the radar can quantify to some extent the corrosion severity.

--> 5 classes, one for each number of screws, e.g., 4 screws --> 4screw_1 to 4screw_3

Scenario 3 has different settings based on pipe testing: 

Pipe_corroded are files with rusty screws inside to mimic internal corrosion.

--> Pipe_corroded_1 to Pipe_corroded_4

Pipe_empty are files for baseline knowledge of the pipe.

--> Pipe_empty_f25_1 to Pipe_empty_f25_6

Pipe_normal_nails are files where healthy screws are introduced in the pipe (galvanized steel for both the screws and the pipe)

--> Pipe_healthy_nailsf25_1 to Pipe_health_nailsf25_4
DOI: 10.5281/zenodo.18148942
title: Antarctic Bottom Water decline from SatGEM-2

description: Data and codes for the creation of the figures in 'The observed circumpolar decline of Antarctic Bottom Water'.
DOI: 10.5281/zenodo.18148943
title: Antarctic Bottom Water decline from SatGEM-2

description: Data and codes for the creation of the figures in 'The observed circumpolar decline of Antarctic Bottom Water'.
DOI: 10.15468/dl.t36y8q
title: Occurrence Download

description: A dataset containing 168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Garrapatas duras (Acari: Ixodidae) colectadas en hospederos domésticos del Magdalena Medio Antioqueño",
 "Geometry POLYGON((-85.20500 -0.98884,-63.79500 -0.98884,-63.79500 13.98884,-85.20500 13.98884,-85.20500 -0.98884))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2018-2019"
 ]
}

 The dataset includes 168 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004607-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6092/unibo/amsacta/8742
title: ECLectic. WP3 Task 3.1. Versatile Electrochemiluminescence Imaging Platform for Multiplexed Biosensing and Point-of-Care Application

description: This dataset was produced as part of the ECLectic project and contains synchronized electrochemiluminescence (ECL) measurements of the [Ru(bpy)₃]²⁺/TPrA system acquired using a custom, compact ECL imaging platform. It includes cyclic voltammetry and chronoamperometry data, corresponding ECL image sequences and videos, and calibration experiments demonstrating electrochemical–optical synchronization, obtained from both homogeneous solution-phase assays and heterogeneous bead-based formats, including single-analyte and multiplexed spotted bead systems on screen-printed electrodes. The dataset also provides the Python scripts used to control the platform hardware and synchronization, as well as USAF 1951 resolution target images for optical performance assessment. Together, these data support reproducibility, quantitative analysis, and performance benchmarking of low-cost, spatially and temporally resolved ECL imaging platforms for biosensing and multiplexed assay applications.
DOI: 10.15468/dl.rwkg7g
title: Occurrence Download

description: A dataset containing 117 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Caragana tibetica"
 ]
}

 The dataset includes 117 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004608-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x6cb2n
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.76577 24.45966, 88.688627 24.454083, 88.61340300000001 24.437491, 88.54196899999999 24.410296, 88.476097 24.373174, 88.417419 24.327048, 88.36738099999999 24.273063, 88.327213 24.212556, 88.297895 24.147025, 88.280134 24.07809, 88.274349 24.007452, 88.280664 23.936853, 88.298903 23.868028, 88.32860100000001 23.802668, 88.369012 23.742377, 88.419134 23.688631, 88.477728 23.642744, 88.543357 23.605838, 88.614411 23.578815, 88.68915699999999 23.562333, 88.76577 23.556793, 88.842383 23.562333, 88.917129 23.578815, 88.98818300000001 23.605838, 89.05381199999999 23.642744, 89.11240599999999 23.688631, 89.16252799999999 23.742377, 89.202939 23.802668, 89.232637 23.868028, 89.25087600000001 23.936853, 89.25719100000001 24.007452, 89.251406 24.07809, 89.233645 24.147025, 89.20432700000001 24.212556, 89.164159 24.273063, 89.114121 24.327048, 89.055443 24.373174, 88.989571 24.410296, 88.918137 24.437491, 88.842913 24.454083, 88.76577 24.45966))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004609-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18335412
title: Large Language Model–Based Fault Diagnosis for Lithium-ion Batteries in Cloud-Edge Systems

description: This dataset was used in the paper Large Language Model–Based Fault Diagnosis for Lithium-ion Batteries in Cloud-Edge Systems. It comprises laboratory experimental data and real-world energy storage system (ESS)data.he laboratory data record battery current, voltage, and (in some cases) temperature during fault experiments, as well as the fault severity and fault type. The dataset includes both raw measurements and data obtained through augmentation. For fault cases, the samples retained are those closest to the time of fault occurrence, whereas samples farther from the fault time are labeled as normal.he real-world ESS data consist of current, voltage, and temperature measurements collected during the operation of an actual ESS power station. For commercial reasons, we do not disclose the exact fault locations or labels; however, the data are confirmed to include faults such as self-discharge and inconsistency. Note that, in the real-world ESS data, a positive current indicates battery discharge.
DOI: 10.5281/zenodo.18335411
title: Large Language Model–Based Fault Diagnosis for Lithium-ion Batteries in Cloud-Edge Systems

description: This dataset was used in the paper Large Language Model–Based Fault Diagnosis for Lithium-ion Batteries in Cloud-Edge Systems. It comprises laboratory experimental data and real-world energy storage system (ESS)data.he laboratory data record battery current, voltage, and (in some cases) temperature during fault experiments, as well as the fault severity and fault type. The dataset includes both raw measurements and data obtained through augmentation. For fault cases, the samples retained are those closest to the time of fault occurrence, whereas samples farther from the fault time are labeled as normal.he real-world ESS data consist of current, voltage, and temperature measurements collected during the operation of an actual ESS power station. For commercial reasons, we do not disclose the exact fault locations or labels; however, the data are confirmed to include faults such as self-discharge and inconsistency. Note that, in the real-world ESS data, a positive current indicates battery discharge.
DOI: 10.5281/zenodo.18335390
title: An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4AGIIRS
DOI: 10.5281/zenodo.18335391
title: An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4AGIIRS
DOI: 10.15468/dl.ujd7sr
title: Occurrence Download

description: A dataset containing 11604 records in GBIF matching the query:
SELECT
 '78' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= -20.3845909579754
 AND decimalLatitude &lt;= -16.25916876999866
 AND decimalLongitude &gt;= 20.464951930498312
 AND decimalLongitude &lt;= 25.961512180631523
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 11604 records were derived from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004598-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18335442
title: Raw Data-The Association Between Upward Appearance Comparison and Body Dissatisfaction Among University Students

description: The dataset, "Raw Data on the Relationship between Upward Appearance Comparison and Body Dissatisfaction among University Students," was collected via the Wenjuanxing platform (https://www.wjx.cn/) from undergraduate students across multiple universities in China between October and November 2025. A total of 710 valid questionnaires were retained for analysis. Grounded in objectification theory, this study measured key variables including upward appearance comparison, body dissatisfaction, body shame, and negative perfectionism to examine the relationship between upward appearance comparison and body dissatisfaction. 

These data not only offer insights into the mechanisms underlying body dissatisfaction among university students but also provide important references for promoting healthy body image and self-perception among contemporary university students.
DOI: 10.5281/zenodo.18335443
title: Raw Data-The Association Between Upward Appearance Comparison and Body Dissatisfaction Among University Students

description: The dataset, "Raw Data on the Relationship between Upward Appearance Comparison and Body Dissatisfaction among University Students," was collected via the Wenjuanxing platform (https://www.wjx.cn/) from undergraduate students across multiple universities in China between October and November 2025. A total of 710 valid questionnaires were retained for analysis. Grounded in objectification theory, this study measured key variables including upward appearance comparison, body dissatisfaction, body shame, and negative perfectionism to examine the relationship between upward appearance comparison and body dissatisfaction. 

These data not only offer insights into the mechanisms underlying body dissatisfaction among university students but also provide important references for promoting healthy body image and self-perception among contemporary university students.
DOI: 10.15468/dl.r2fudw
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.76826 24.46013, 88.69111700000001 24.454553, 88.615893 24.437961, 88.54445800000001 24.410766, 88.47858600000001 24.373644, 88.41990800000001 24.327518, 88.36987000000001 24.273533, 88.329702 24.213026, 88.300383 24.147495, 88.282622 24.07856, 88.276837 24.007922, 88.283152 23.937323, 88.301391 23.868498, 88.33108900000001 23.803138, 88.37150099999999 23.742847, 88.421623 23.689101, 88.480217 23.643214, 88.545846 23.606309, 88.616901 23.579285, 88.691647 23.562803, 88.76826 23.557264, 88.84487300000001 23.562803, 88.919619 23.579285, 88.990674 23.606309, 89.056303 23.643214, 89.114897 23.689101, 89.165019 23.742847, 89.205431 23.803138, 89.235129 23.868498, 89.25336799999999 23.937323, 89.259683 24.007922, 89.25389800000001 24.07856, 89.236137 24.147495, 89.206818 24.213026, 89.16665 24.273533, 89.116612 24.327518, 89.057934 24.373644, 88.992062 24.410766, 88.920627 24.437961, 88.845403 24.454553, 88.76826 24.46013))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004611-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2qxpg3
title: Occurrence Download

description: A dataset containing 2592699 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Pl@ntNet observations"
 ]
}

 The dataset includes 2592699 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004602-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18335275
title: Leader Age at Accession and Economic Growth - Replication Package

description: Leader Age at Accession and Economic Growth - Replication Package

This repository contains data and Python code to reproduce all tables and figures in the paper “Leader Age at Accession and Economic Growth”. The package includes (i) a leader-spell analysis dataset, (ii) a curated country-year extract to construct the Digital Repression Index (DRI) and validate leader ages, (iii) scripts to download additional third‑party datasets (GDP per capita, V‑Dem polyarchy, Polity5 executive constraints) from pinned or versioned sources, and (iv) scripts that generate all results.

 

How to run

1) Install dependencies: `pip install -r requirements.txt` 

2) Download external datasets: `python code/00_download_external_data.py` 

3) Reproduce all results: `python code/run_all.py` 

Outputs are written to `outputs/tables/` and `outputs/figures/`.

 

External data

Large third‑party datasets are not bundled; they are downloaded on demand and cached locally, to keep the replication bundle lightweight while preserving transparency.

This replication package relies on several external datasets. Please cite the corresponding sources when using the data and respect each provider's license/terms.

- World Bank World Development Indicators (GDP per capita, PPP, constant international-$); Downloaded via an archived OWID Grapher snapshot (`gdp-per-capita-worldbank`).

- V‑Dem (Varieties of Democracy) polyarchy index (`v2x_polyarchy`); Downloaded via an archived OWID Grapher snapshot (`electoral-democracy-index`).

- Polity5 (Systemic Peace) annual dataset (includes executive constraints `XCONST`); Downloaded from Systemic Peace (`p5v2018d.xls`).

The scripts cache downloads locally in `data/external/` for reproducibility.

 

Reproducibility tips

- If you rerun scripts, they overwrite derived datasets and outputs.

- Downloaded files are cached; re-running the download script will skip files that already exist.

- For exact bootstrap p-values, keep the seed fixed (see `code/config.py`).
DOI: 10.5281/zenodo.18335276
title: Leader Age at Accession and Economic Growth - Replication Package

description: Leader Age at Accession and Economic Growth - Replication Package

This repository contains data and Python code to reproduce all tables and figures in the paper “Leader Age at Accession and Economic Growth”. The package includes (i) a leader-spell analysis dataset, (ii) a curated country-year extract to construct the Digital Repression Index (DRI) and validate leader ages, (iii) scripts to download additional third‑party datasets (GDP per capita, V‑Dem polyarchy, Polity5 executive constraints) from pinned or versioned sources, and (iv) scripts that generate all results.

 

How to run

1) Install dependencies: `pip install -r requirements.txt` 

2) Download external datasets: `python code/00_download_external_data.py` 

3) Reproduce all results: `python code/run_all.py` 

Outputs are written to `outputs/tables/` and `outputs/figures/`.

 

External data

Large third‑party datasets are not bundled; they are downloaded on demand and cached locally, to keep the replication bundle lightweight while preserving transparency.

This replication package relies on several external datasets. Please cite the corresponding sources when using the data and respect each provider's license/terms.

- World Bank World Development Indicators (GDP per capita, PPP, constant international-$); Downloaded via an archived OWID Grapher snapshot (`gdp-per-capita-worldbank`).

- V‑Dem (Varieties of Democracy) polyarchy index (`v2x_polyarchy`); Downloaded via an archived OWID Grapher snapshot (`electoral-democracy-index`).

- Polity5 (Systemic Peace) annual dataset (includes executive constraints `XCONST`); Downloaded from Systemic Peace (`p5v2018d.xls`).

The scripts cache downloads locally in `data/external/` for reproducibility.

 

Reproducibility tips

- If you rerun scripts, they overwrite derived datasets and outputs.

- Downloaded files are cached; re-running the download script will skip files that already exist.

- For exact bootstrap p-values, keep the seed fixed (see `code/config.py`).