cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.vh5rfe
title: Occurrence Download

description: A dataset containing 531 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Heloderma horridum",
 "Year 1970-2025"
 ]
}

 The dataset includes 531 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008216-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.trrh6t
title: Occurrence Download

description: A dataset containing 74857 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Silybum marianum"
 ]
}

 The dataset includes 74857 records from 1133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008149-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5164/iab.mup-bhp1023.de.en.v1
title: Mannheimer Unternehmenspanel verknüpft mit dem Betriebs-Historik-Panel (MUP-BHP) – Version 1023 v1
title: Mannheim Enterprise Panel linked to the Establishment History Panel (MUP-BHP) – Version 1023 v1
title: MUP-BHP 1023 v1

description: Das Mannheimer Unternehmenspanel verknüpft mit dem Betriebs-Historik-Panel (MUP-BHP) setzt sich aus Querschnittsdatensätzen ab dem Jahr 2010 zusammen. Jeder Querschnitt umfasst alle Gesellschaften mit beschränkter Haftung (GmbH) und Unternehmergesellschaften (UG) aus dem Mannheimer Unternehmenspanel (MUP) und deren verknüpfbare Betriebsstätten des gesamtdeutschen Raumes, die im Betriebs-Historik-Panel (BHP) des Instituts für Arbeitsmarkt- und Berufsforschung (IAB) zum Stichtag 31.12. erfasst sind. Diese Verknüpfung von Unternehmen und Betrieben liegt ein Record-Linkage der Adressdaten des vom Leibniz Institut für Europäische Wirtschaftsforschung (ZEW) gehaltenen MUP und der Betriebsadressdaten am IAB zugrunde. Die einzelnen Querschnittsdatensätze enthalten Informationen zu den Betrieben und Unternehmen sowie eine Zusatzdatei zu Gesellschafterinnen und Gesellschaftern. Die Datensätze können zu einem Panel verbunden werden.
description: The Mannheim Enterprise Panel linked to the Establishment History Panel (MUP-BHP) is made up of cross-sectional data sets from 2010 onwards. Each cross-section includes all limited liability companies (GmbH) and limited liability entrepreneurial companies (UG) from the Mannheim Company Panel (MUP) and their linked establishments throughout Germany that are recorded in the Establishment History Panel (BHP) of the Institute for Employment Research (IAB) as of 31.12. This linking of establishments and businesses is based on a record linkage of the address data of the MUP held by the Leibniz Centre for European Economic Research (ZEW) and the business address data at the IAB. The individual cross-sectional data sets contain information on the establishments and companies as well as an additional file on shareholders. The data sets can be linked to form a panel.
DOI: 10.25442/hku.28978565
title: Supporting data for Using music-mediated intergroup contact in a practical design to promote outgroup attitude and empathy of high socioeconomic status (SES) secondary students for low SES primary students.

description: This dataset is created to support the research thesis titled 'Using music-mediated intergroup contact in a practical design to promote outgroup attitude and empathy of high socioeconomic status (SES) secondary students for low SES primary students'. This research thesis aims to develop an innovative implicit measure of SES-related attitude for adolescents, and examine the effectiveness of a music-mediated intergroup contact intervention in promoting outgroup SES-related attitudes of high SES secondary students towards lower SES primary students. To ensure the data quality, this research also embraced partition analysis to deal with the data interpretation dilemma and shed light on future social science studies to consider partitioning the incomparable datasets into homogeneous categories for valid data interpretation. This dataset includes data from the pilot study and studies 1-3 for the research thesis 'Using music-mediated intergroup contact in a practical design to promote outgroup attitude and empathy of high socioeconomic status (SES) secondary students for low SES primary students'.The pilot study data, with a sample size of 5, corresponds to Chapter 4, study 1 data, with a sample size of 328, corresponds to Chapter 5, study 2 data, with a sample size of 275 corresponds to Chapter 6, and study 3 data, with a sample size of 36, corresponds to Chapter 7 in the submitted thesis. The targeted participants are secondary students from Hong Kong Direct Subsidy Schools.
DOI: 10.5281/zenodo.14910195
title: SHNITSEL - Surface Hopping Nested Instances Training Set for Excited-state Learning

description: SHNITSEL

The Surface Hopping Nested Instances Training Set for Excited-State Learning (SHNITSEL) is a comprehensive data repository designed to support the development and benchmarking of excited-state dynamics methods.

Configuration Space

SHNITSEL contains datasets for nine organic molecules that represent a diverse range of photochemical behaviors. The following molecules are included in the dataset:



Alkenes: ethene (A01), propene (A02), 2-butene (A03)

Ring structures: fulvene (R01), 1,3-cyclohexadiene (R02), tyrosine (R03)

Other molecules: methylenimmonium cation (I01), methanethione (T01), diiodomethane (H01)


Property Space

These datasets provide key electronic properties for singlet and triplet states, including energies, forces, dipole moments, transition dipole moments, nonadiabatic couplings, and spin-orbit couplings, computed at the multi-reference ab initio level. The data is categorized into static and dynamic data, based on its origin and purpose.



Static data (#147,169 data points in total) consists of sampled molecular structures without time-dependent information, covering relevant vibrational and conformational spaces. These datasets are provided for eight molecules: A01, A02, A03, R01, R03, I01, T01, and H01



Dynamic data (#444,581 data points in total) originates from surface hopping simulations and captures the evolution of molecular structures and properties over time, as they propagate on potential energy surfaces according to Newton’s equations of motion. These datasets are provided for five molecules: A01, A02, A03, R02, and I01



Data Structure and Workflow

The data is stored in xarray format, using xarray.Dataset objects for efficient handling of multidimensional data. Key dimensions include electronic states, couplings, atoms, and time frames for dynamic data. The dataset is scalable and compatible with large datasets, stored in NetCDF4 format within HDF5 for optimal performance. Tools for data processing, visualization, and integration into machine learning workflows are provided by the shnitsel Python package published on Github (shnitsel-tools) .(https://github.com/SHNITSEL/shnitsel-tools).

An overview of the molecular structures and visualizations of key properties (from trajectory data) are compiled on the SHNITSEL webpage (https://shnitsel.github.io/).
DOI: 10.5281/zenodo.15482819
title: SHNITSEL - Surface Hopping Nested Instances Training Set for Excited-state Learning

description: SHNITSEL

The Surface Hopping Nested Instances Training Set for Excited-State Learning (SHNITSEL) is a comprehensive data repository designed to support the development and benchmarking of excited-state dynamics methods.

Configuration Space

SHNITSEL contains datasets for nine organic molecules that represent a diverse range of photochemical behaviors. The following molecules are included in the dataset:



Alkenes: ethene (A01), propene (A02), 2-butene (A03)

Ring structures: fulvene (R01), 1,3-cyclohexadiene (R02), tyrosine (R03)

Other molecules: methylenimmonium cation (I01), methanethione (T01), diiodomethane (H01)


Property Space

These datasets provide key electronic properties for singlet and triplet states, including energies, forces, dipole moments, transition dipole moments, nonadiabatic couplings, and spin-orbit couplings, computed at the multi-reference ab initio level. The data is categorized into static and dynamic data, based on its origin and purpose.



Static data (#147,169 data points in total) consists of sampled molecular structures without time-dependent information, covering relevant vibrational and conformational spaces. These datasets are provided for eight molecules: A01, A02, A03, R01, R03, I01, T01, and H01



Dynamic data (#271,700 data points in total) originates from surface hopping simulations and captures the evolution of molecular structures and properties over time, as they propagate on potential energy surfaces according to Newton’s equations of motion. These datasets are provided for five molecules: A01, A02, A03, R02, and I01



Data Structure and Workflow

The data is stored in xarray format, using xarray.Dataset objects for efficient handling of multidimensional data. Key dimensions include electronic states, couplings, atoms, and time frames for dynamic data. The dataset is scalable and compatible with large datasets, stored in NetCDF4 format within HDF5 for optimal performance. 

An overview of the molecular structures and visualizations of key properties (from trajectory data) are compiled on the SHNITSEL webpage (https://shnitsel.github.io/).
DOI: 10.6084/m9.figshare.30688334
title: Music Questionnaire

description: 本研究以多群体受访者为调研对象，聚焦音乐与文化及个体发展的关联，系统开展以下研究内容： 1. 基础信息与音乐相关特征调研：全面收集受访者性别、年龄、学历、学校类型（综合类大学、艺术类院校等）、院系专业等人口统计学信息，同时记录音乐偏好（古典乐、流行乐、爵士乐、世界音乐等）、音乐活动参与频率（每周3次及以上、每月一次等）、音乐在日常中的角色（情感寄托、休闲娱乐、专业、多重角色等）。2. 音乐与文化关联探究：通过评分与态度调研，分析受访者对“音乐与文化认同感有联系”“音乐能作为跨文化交流媒介”“音乐能反映文化背景”“音乐能促进文化理解”等观点的认同程度，同步收集音乐文化认同感量表评分、跨文化交流传媒认同度、文化自信量表评分等量化数据。3. 音乐对个体的多维度影响分析：调研重要生活事件、音乐偏好变化情况，探讨音乐对受访者文化连结体验、文化认同感的影响（增强、无变化等），同时收集心理健康量表评分，综合分析音乐在个体文化认知、心理状态发展中的作用机制。
DOI: 10.6084/m9.figshare.30688334.v1
title: Music Questionnaire

description: 本研究以多群体受访者为调研对象，聚焦音乐与文化及个体发展的关联，系统开展以下研究内容： 1. 基础信息与音乐相关特征调研：全面收集受访者性别、年龄、学历、学校类型（综合类大学、艺术类院校等）、院系专业等人口统计学信息，同时记录音乐偏好（古典乐、流行乐、爵士乐、世界音乐等）、音乐活动参与频率（每周3次及以上、每月一次等）、音乐在日常中的角色（情感寄托、休闲娱乐、专业、多重角色等）。2. 音乐与文化关联探究：通过评分与态度调研，分析受访者对“音乐与文化认同感有联系”“音乐能作为跨文化交流媒介”“音乐能反映文化背景”“音乐能促进文化理解”等观点的认同程度，同步收集音乐文化认同感量表评分、跨文化交流传媒认同度、文化自信量表评分等量化数据。3. 音乐对个体的多维度影响分析：调研重要生活事件、音乐偏好变化情况，探讨音乐对受访者文化连结体验、文化认同感的影响（增强、无变化等），同时收集心理健康量表评分，综合分析音乐在个体文化认知、心理状态发展中的作用机制。
DOI: 10.15468/dl.35zpx2
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Two new species of Thrypticus Gerstäcker from Turkey (Diptera, Dolichopodidae) with checklist and key to Palaearctic species"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003859-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17686165
title: Ipseity Daily Data

description: At what rate do individuals say they are X?

Begining on 2025-07-08, I surveyed a small random sample of American adults on a daily cadence.  They responded to many items of the form: Does <signifier> describe you today?

The file ipseity-daily-aggregated.csv in this repository contains the aggregated results through 2025-07-28.  The columns are as follows:



signifier - a word, phrase or emoji

yes_count - the number of respondents who answered yes (aggregated all-time).

no_count - the number of respondents who answered no (aggregated all-time).

prevalence_per_10k - an estimate of the prevalence per 10,000.  If we were to survey 10,000 American adults, how many would we expect to respond yes to this signifier?  The number is calculated as yes_count / (yes_count + no_count) * 10,000.

earliest_obs_date - this date represents the first time this particular signifier was ever displayed to a respondent.  Signifiers are chosen with stochasticity from a long list; new signifiers are sometimes added, old signifiers are sometimes deleted, and a signifier may be moved higher or lower in priority for sampling.

latest_obs_date - the date closest to the present date on which signifier was most recently presented to a respondent.


Version created on 2025-11-23 — cumulative data.
DOI: 10.6084/m9.figshare.28200872
title: Historical forest aboveground biomass carbon storage (HFCS) dataset

description: The primary challenge in reconstructing historical forest aboveground biomass carbon storage lies in the difficulty of acquiring forest growth status indicators spanning over a century, irrespective of canopy height, forest age, NDVI, LAI, or SIF. In this study, we utilized historical forest cover percentage datasets from 1900 to 2020 as a surrogate indicator for modelling century-long forest carbon stock estimation. Subsequently, we selected 15 categories of predictive factors, including vegetation, meteorologic, geophysical, soil, and spatial distance factors . To do this, we used the forest aboveground biomass (AGB) dataset from 2019 as the base map. We divided the dataset into a 70% training set and a 30% test set for model training and evaluation. By continuously adjusting the velocity and position of particles using the Particle Swarm Optimization algorithm, we obtained the optimal RF model with optimal model parameters as follows: a number of decision trees of 195, a maximum depth of 10 for each decision tree, a number of features of 12 to consider when looking for the best split, minimum number of samples of 4 required to split an internal node, and a minimum number of samples of 3 required to be at a leaf node.
DOI: 10.6084/m9.figshare.28200872.v1
title: Historical forest aboveground biomass carbon storage (HFCS) dataset

description: The primary challenge in reconstructing historical forest aboveground biomass carbon storage lies in the difficulty of acquiring forest growth status indicators spanning over a century, irrespective of canopy height, forest age, NDVI, LAI, or SIF. In this study, we utilized historical forest cover percentage datasets from 1900 to 2020 as a surrogate indicator for modelling century-long forest carbon stock estimation. Subsequently, we selected 15 categories of predictive factors, including vegetation, meteorologic, geophysical, soil, and spatial distance factors . To do this, we used the forest aboveground biomass (AGB) dataset from 2019 as the base map. We divided the dataset into a 70% training set and a 30% test set for model training and evaluation. By continuously adjusting the velocity and position of particles using the Particle Swarm Optimization algorithm, we obtained the optimal RF model with optimal model parameters as follows: a number of decision trees of 195, a maximum depth of 10 for each decision tree, a number of features of 12 to consider when looking for the best split, minimum number of samples of 4 required to split an internal node, and a minimum number of samples of 3 required to be at a leaf node.
DOI: 10.34740/kaggle/dsv/13835393
title: Contrastive Language Image Pretraining by openai
DOI: 10.15468/dl.va5kz9
title: Occurrence Download

description: A dataset containing 2139 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phoracantha semipunctata)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2139 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003862-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k5ecfg
title: Occurrence Download

description: A dataset listing the 10 species recorded in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Trichechus"
 ]
}

 The dataset's 10 records were derived from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003861-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.seda84
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-107.43860 -23.90474,59.43860 -23.90474,59.43860 42.90474,-107.43860 42.90474,-107.43860 -23.90474))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Trichechus senegalensis"
 ]
}

 The dataset's 1 records were derived from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003860-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sfef32
title: Occurrence Download

description: A dataset containing 186 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Caryopteris mongholica"
 ]
}

 The dataset includes 186 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003863-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5f4hqz
title: Occurrence Download

description: A dataset containing 420 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gonipterus scutellatus)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 420 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003864-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mys6hj
title: Occurrence Download

description: A dataset containing 31 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 GBIF_WITHIN('POLYGON((-123.01892 -16.62742,-9.98108 -16.62742,-9.98108 28.62742,-123.01892 28.62742,-123.01892 -16.62742))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 7059452 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 7059452 OR CAST(occurrence.kingdomkey AS INTEGER) = 7059452 OR CAST(occurrence.phylumkey AS INTEGER) = 7059452 OR CAST(occurrence.classkey AS INTEGER) = 7059452 OR CAST(occurrence.orderkey AS INTEGER) = 7059452 OR CAST(occurrence.familykey AS INTEGER) = 7059452 OR CAST(occurrence.genuskey AS INTEGER) = 7059452 OR CAST(occurrence.specieskey AS INTEGER) = 7059452)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 31 records were derived from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003858-251120083545085/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.30687545
title: <b>T</b><b>he Impact of Female TMT on Supply Chain Risk Management through Information Transparency and Corporate Innovation</b>

description: This study empirically examines the impact of female top management teams (TMT) on supply chain risk management performance using data from Chinese A-share listed companies from 2001 to 2021. Through methodologies such as PSM-DID, OLS, instrumental variables, and quantile regression, the research demonstrates that female TMT significantly reduce supply chain concentration, thereby enhancing risk management outcomes. Mechanism tests reveal that female TMT improve supply chain performance by enhancing information transparency and fostering corporate innovation. Additionally, the level of human capital positively moderates this relationship, and heterogeneity analysis shows that the effect is more pronounced in state-owned enterprises. The study provides theoretical and practical insights into gender diversity in TMT and its role in building resilient supply chains.
DOI: 10.6084/m9.figshare.30687545.v1
title: <b>T</b><b>he Impact of Female TMT on Supply Chain Risk Management through Information Transparency and Corporate Innovation</b>

description: This study empirically examines the impact of female top management teams (TMT) on supply chain risk management performance using data from Chinese A-share listed companies from 2001 to 2021. Through methodologies such as PSM-DID, OLS, instrumental variables, and quantile regression, the research demonstrates that female TMT significantly reduce supply chain concentration, thereby enhancing risk management outcomes. Mechanism tests reveal that female TMT improve supply chain performance by enhancing information transparency and fostering corporate innovation. Additionally, the level of human capital positively moderates this relationship, and heterogeneity analysis shows that the effect is more pronounced in state-owned enterprises. The study provides theoretical and practical insights into gender diversity in TMT and its role in building resilient supply chains.
DOI: 10.15468/dl.z9z6y8
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Distribución del Manatí (Trichechus manatus) en la cuenca baja del río Sinú en el departamento de Córdoba"
 ]
}

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003865-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17670560
title: HF-EOLUS. Task 3. MeteoGalicia Wind Interpolation Outputs. Data Supplement 1 of 2

description: This record contains the interpolated datasets from 2018 to 2020 for:



Herrera Cortijo, J. L., Fernández-Baladrón, A., Rosón, G., Gil Coto, M., Dubert, J., & Varela Benvenuto, R. (2025). HF-EOLUS Wind Interpolation Toolkit for MeteoGalicia models output (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.17598353.