cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/d39v4bx59p
title: Datasets and Codes for "FDI Spillovers, Innovation and the Role of Industrial Clusters: Evidence from Innovative Indian Manufacturing Firms"

description: In this study, to analyse the role of horizontal and vertical FDI spillover upon innovation output, measured through patent application and patent granted across firms in a prominent industrial cluster and firms located outside the industrial cluster, we have taken data of Indian innovative manufacturing firms during the period 2007 to 2020. India’s full-fledged implementation of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) started in 2005. However, one of the key variables of the study (the Input-Output Table for India, required to calculate the vertical FDI spillover variable) is available only from 2007. Our initial dataset comprises 10,316 firms across 23 broader two-digit manufacturing industries. These data are further complemented with PatSeer (Patent Search and Analysis Software) data for patent information. The firm-level data are collected from the CMIE Prowess database. Following Hall et al. (2013) and Igna and Venturini (2023), we dropped the firms without a single patent to their credit during our entire study period from the sample. This decision is consistent with the literature as it ensures that all firms face a decision on how to protect their innovation. This process left us with 347 innovative firms, constituting the final sample for the empirical analysis.
DOI: 10.17632/d39v4bx59p.1
title: Datasets and Codes for "FDI Spillovers, Innovation and the Role of Industrial Clusters: Evidence from Innovative Indian Manufacturing Firms"

description: In this study, to analyse the role of horizontal and vertical FDI spillover upon innovation output, measured through patent application and patent granted across firms in a prominent industrial cluster and firms located outside the industrial cluster, we have taken data of Indian innovative manufacturing firms during the period 2007 to 2020. India’s full-fledged implementation of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) started in 2005. However, one of the key variables of the study (the Input-Output Table for India, required to calculate the vertical FDI spillover variable) is available only from 2007. Our initial dataset comprises 10,316 firms across 23 broader two-digit manufacturing industries. These data are further complemented with PatSeer (Patent Search and Analysis Software) data for patent information. The firm-level data are collected from the CMIE Prowess database. Following Hall et al. (2013) and Igna and Venturini (2023), we dropped the firms without a single patent to their credit during our entire study period from the sample. This decision is consistent with the literature as it ensures that all firms face a decision on how to protect their innovation. This process left us with 347 innovative firms, constituting the final sample for the empirical analysis.
DOI: 10.17632/fhwxx4s5kp
title: Dataset for Ranking Social Science Institutions in India through Responsible Research Assessment (RRA)

description: This dataset supports the study entitled: “A Responsible Research Assessment Framework for Ranking Social Science Institutions in India”. It comprises diverse research outputs, academic activities, and institutional resources of twenty-four ICSSR-funded institutions across the country. The dataset is primarily extracted from Scopus (a citation database), subsequently validated through Annual Reports of the ICSSR, covering the period from 2013 to 2022. Moreover, high-resolution images of all figures and tables used in the paper have also been shared to support transparency and facilitate detailed examination. The dataset is curated to provide a holistic view of institutional performance in order to showcase the publication productivity, academic activities, resource allocations, prestige values, stakeholder engagement, and societal impact through various quantitative indicators. The dataset enables replication of the study, cross-institutional benchmarking, and policy-driven analysis of research ecosystems. The dataset adheres to FAIR principles and is structured to enable reuse, thereby enhancing reproducibility as an ethical practice for generating better insights. Eventually, this dataset aims to accelerate scientific progress by building upon existing work, which is crucial for advancing open science practices.
DOI: 10.17632/fhwxx4s5kp.1
title: Dataset for Ranking Social Science Institutions in India through Responsible Research Assessment (RRA)

description: This dataset supports the study entitled: “A Responsible Research Assessment Framework for Ranking Social Science Institutions in India”. It comprises diverse research outputs, academic activities, and institutional resources of twenty-four ICSSR-funded institutions across the country. The dataset is primarily extracted from Scopus (a citation database), subsequently validated through Annual Reports of the ICSSR, covering the period from 2013 to 2022. Moreover, high-resolution images of all figures and tables used in the paper have also been shared to support transparency and facilitate detailed examination. The dataset is curated to provide a holistic view of institutional performance in order to showcase the publication productivity, academic activities, resource allocations, prestige values, stakeholder engagement, and societal impact through various quantitative indicators. The dataset enables replication of the study, cross-institutional benchmarking, and policy-driven analysis of research ecosystems. The dataset adheres to FAIR principles and is structured to enable reuse, thereby enhancing reproducibility as an ethical practice for generating better insights. Eventually, this dataset aims to accelerate scientific progress by building upon existing work, which is crucial for advancing open science practices.
DOI: 10.21227/f90x-e095
title: "AWS, Azure, Google Cloud Platform Vulnerability datasets gethered on 2025 May"

description: "The vulnerability datasets used in this study were collected from official and publicly available sources as of May 2025. For AWS and GCP, vulnerability information was retrieved from their respective security bulletin portals (AWS: https:\/\/aws.amazon.com\/security\/security-bulletins, GCP: https:\/\/cloud.google.com\/support\/bulletins).In the case of MSA, direct and systematic extraction of vulnerability data was not feasible due to the lack of a publicly accessible, structured bulletin format. Therefore, we utilized the third-party vulnerability database (https:\/\/www.cvedetails. com), employing its advanced search interface to isolate Azure-specific vulnerabilities. Specifically, we set the vendor and product fields to Microsoft and Azure respectively. This search yielded 122 Azure-related product entries with a total of 378 vulnerability records, including duplicates. The dataset was then refined by filtering out non-unique CVE entries.The final dataset comprises unique CVE identifiers for each cloud service provider as follows: AWS (122 entries), MSA (319 entries), and GCP (264 entries)."
DOI: 10.17632/2t4rdpr285.1
title: Supplementary information: Study database

description: This database contains the supplementary material for the study.
DOI: 10.17632/3zpzyfd487
title: BTB Analysis

description: This dataset maps sources concerned with the decentralization of digital platform power and links them to the frequency of specific decentralization strategies coded in those sources. The first column lists academic and professional fields (e.g., law, management, technology) and counts how many sources from each domain engage with the topic. The second column lists individual decentralization strategies (e.g., divestiture, interoperability, data trusts) and the frequency with which each strategy appears across all sources. Some entries also specify national contexts (e.g., C: India, C: US) or legislative proposals (e.g., DMA, DSA, AICOA). The dataset reflects both the disciplinary spread of interest and the strategic diversity in approaches to decentralizing platform power.
DOI: 10.17632/3zpzyfd487.1
title: BTB Analysis

description: This dataset maps sources concerned with the decentralization of digital platform power and links them to the frequency of specific decentralization strategies coded in those sources. The first column lists academic and professional fields (e.g., law, management, technology) and counts how many sources from each domain engage with the topic. The second column lists individual decentralization strategies (e.g., divestiture, interoperability, data trusts) and the frequency with which each strategy appears across all sources. Some entries also specify national contexts (e.g., C: India, C: US) or legislative proposals (e.g., DMA, DSA, AICOA). The dataset reflects both the disciplinary spread of interest and the strategic diversity in approaches to decentralizing platform power.
DOI: 10.17632/gyrxb8j3g8
title: Data of the Transportation territorial spatial suitability

description: In this paper, most of the source data are already in the folder, other data please refer to the paper ' Data Sources ', this part, there are detailed data sources, you can go to the corresponding link, collect data
DOI: 10.17632/gyrxb8j3g8.1
title: Data of the Transportation territorial spatial suitability

description: In this paper, most of the source data are already in the folder, other data please refer to the paper ' Data Sources ', this part, there are detailed data sources, you can go to the corresponding link, collect data
DOI: 10.15468/dl.h7rzpf
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Draba sikkimensis"
 ]
}

 The dataset includes 24 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018903-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jxby8pkww5
title: Data and Codes for "Analyzing Price Efficiency Using Machine Learning Generated Price Indices: the Case of the Chilean Used Car Market "

description: This dataset contains all the necessary data and codes to replicate the main findings of the article, which examines how new car import prices affect the valuation of used vehicles in Chile's secondary car market. The study uses event study and difference-in-differences (DiD) methodologies to evaluate price efficiency.

The replication package includes:

- Used car price indexes dataset: Filtered and pre-processed to include key vehicle attributes (model, year, mileage, transmission, fuel type, seller type, region, etc.).

A new car import dataset built from official customs records covering shipment dates, CIF prices, and units imported per model and version.

R and Python scripts for estimation and analysis:

"Unit Root Test.R": Tests for stationarity using Im, Pesaran, and Shin (IPS) panel tests; and "Event Study CARS.py":

"Event Study CARS.py": Performs event study estimation using cumulative abnormal returns (CAARs), including subsample analyses by vintage and vehicle segment.

"DiD Event Studies.R": Estimates difference-in-difference (DiD) regressions using staggered treatment timing and fixed effects and calculates cumulative abnormal returns (CAARs) from fitted values.

Each code script includes comments and references to the corresponding tables and figures in the paper.

Key findings that can be replicated using these files include:

- Evidence of prompt and statistically significant price responses in the used car market following increases in new import prices.

- Stronger responses among newer and high-end used cars.

- These responses occur before the public release of import data, suggesting high informational efficiency.

- The results are robust across different methodological approaches and sample partitions.

This replication package allows for the independent verification of results. All datasets are anonymized and formatted for reproducibility. The codes are compatible with R 4.2+ and Python 3.8+ environments.
DOI: 10.6084/m9.figshare.29575223
title: figure_11N_plotData.tsv

description: The file contains a combined dataset of DNA methylation and hydroxymethylation levels measured across multiple genomic features in mouse autosomes. Genomic coordinates and per-sample counts of 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), and unmodified cytosines (C) are provided for three biological replicates of each condition: control (CRE) and TET Triple knockout (TKO). For each region, the average 5hmC level across replicates is calculated for CRE and TKO, and the difference between these averages is reported. Each region is annotated with its genomic feature type, including genic regions, promoters, introns, CpG islands, all enhancers (excluding promoters), enhancers overlapping introns, and enhancers not overlapping introns. Regions without valid average 5hmC values in either condition are excluded, and duplicate entries are removed.
DOI: 10.6084/m9.figshare.29575223.v1
title: figure_11N_plotData.tsv

description: The file contains a combined dataset of DNA methylation and hydroxymethylation levels measured across multiple genomic features in mouse autosomes. Genomic coordinates and per-sample counts of 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), and unmodified cytosines (C) are provided for three biological replicates of each condition: control (CRE) and TET Triple knockout (TKO). For each region, the average 5hmC level across replicates is calculated for CRE and TKO, and the difference between these averages is reported. Each region is annotated with its genomic feature type, including genic regions, promoters, introns, CpG islands, all enhancers (excluding promoters), enhancers overlapping introns, and enhancers not overlapping introns. Regions without valid average 5hmC values in either condition are excluded, and duplicate entries are removed.
DOI: 10.17632/jxby8pkww5.1
title: Data and Codes for "Analyzing Price Efficiency Using Machine Learning Generated Price Indices: the Case of the Chilean Used Car Market "

description: This dataset contains all the necessary data and codes to replicate the main findings of the article, which examines how new car import prices affect the valuation of used vehicles in Chile's secondary car market. The study uses event study and difference-in-differences (DiD) methodologies to evaluate price efficiency.

The replication package includes:

- Used car price indexes dataset: Filtered and pre-processed to include key vehicle attributes (model, year, mileage, transmission, fuel type, seller type, region, etc.).

A new car import dataset built from official customs records covering shipment dates, CIF prices, and units imported per model and version.

R and Python scripts for estimation and analysis:

"Unit Root Test.R": Tests for stationarity using Im, Pesaran, and Shin (IPS) panel tests; and "Event Study CARS.py":

"Event Study CARS.py": Performs event study estimation using cumulative abnormal returns (CAARs), including subsample analyses by vintage and vehicle segment.

"DiD Event Studies.R": Estimates difference-in-difference (DiD) regressions using staggered treatment timing and fixed effects and calculates cumulative abnormal returns (CAARs) from fitted values.

Each code script includes comments and references to the corresponding tables and figures in the paper.

Key findings that can be replicated using these files include:

- Evidence of prompt and statistically significant price responses in the used car market following increases in new import prices.

- Stronger responses among newer and high-end used cars.

- These responses occur before the public release of import data, suggesting high informational efficiency.

- The results are robust across different methodological approaches and sample partitions.

This replication package allows for the independent verification of results. All datasets are anonymized and formatted for reproducibility. The codes are compatible with R 4.2+ and Python 3.8+ environments.
DOI: 10.15468/dl.r8mwed
title: Occurrence Download

description: A dataset containing 1716 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Finland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1716 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018905-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/44std32rxb.1
title: Gujarat-HWD: A Dataset of Gujarati Handwritten Digits

description: The Gujarat-HWD dataset offers a comprehensive collection of 11,000 images of handwritten digits in the Gujarati language, representing numerals from 0 to 9. This dataset is primarily designed for applications in optical character recognition (OCR), machine learning, and deep learning, with a particular focus on regional language processing. Gujarati, one of the most widely spoken languages in India, uses a script distinct from Devanagari. However, despite its extensive use, there is a notable lack of publicly available datasets for Gujarati handwritten digits. The Gujarat-HWD dataset bridges this gap by providing a clean, labelled, and diverse set of images that can aid researchers and developers in building effective recognition models for regional scripts.

The dataset has been developed through a systematic process involving the collection, scanning, and preprocessing of handwritten digit samples provided by more than 350 individuals from various age groups and educational backgrounds. It is well-suited for training and evaluating classification models using standard convolutional neural network (CNN) architectures. Additionally, the dataset can be extended for use in cross-lingual digit recognition, handwriting analysis, and other regional OCR systems.
DOI: 10.15468/dl.bbbccg
title: Occurrence Download

description: A dataset containing 482 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Italy",
 "HasCoordinate is true"
 ]
}

 The dataset includes 482 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018907-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vteua6
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Japan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 23 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018909-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4fa4p8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ziziphus mauritiana",
 "Country is United Arab Emirates",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018908-250711103210423/datasets/export for details.
DOI: 10.17632/4tt68zcbx8
title: Research Data for Bridging the Intention-Behavior Gap in Organic Farming Adoption: A Modified Theory of Planned Behavior Approach

description: This dataset contains survey data from 297 Taiwanese farmers examining the intention-behavior gap in organic farming adoption using a modified Theory of Planned Behavior framework. The study investigates how system-level factors moderate the relationship between farmer intentions and actual organic farming behavior. 

The dataset includes:
- Bilingual questionnaire data (Traditional Chinese and English versions)
- Responses measuring attitudes, subjective norms, self-efficacy, intentions, and organic farming behavior
- System-level moderating factors (certification ease, profit expectations)
- Demographic and farm characteristics
- Comprehensive documentation with construct definitions, item sources, and descriptive statistics

Data were collected March-May 2020 through structured face-to-face interviews across Taiwan's diverse agricultural systems. The modified TPB framework disaggregates perceived behavioral control into individual-level self-efficacy and system-level external conditions, treating the latter as moderators of intention-behavior conversion.

All data are de-identified and provided in Excel format compatible with SPSS/AMOS for structural equation modeling. Complete bilingual documentation ensures international accessibility while preserving cultural context. This dataset supports replication of published analyses and enables cross-cultural agricultural adoption research.
DOI: 10.17632/4tt68zcbx8.1
title: Research Data for Bridging the Intention-Behavior Gap in Organic Farming Adoption: A Modified Theory of Planned Behavior Approach

description: This dataset contains survey data from 297 Taiwanese farmers examining the intention-behavior gap in organic farming adoption using a modified Theory of Planned Behavior framework. The study investigates how system-level factors moderate the relationship between farmer intentions and actual organic farming behavior. 

The dataset includes:
- Bilingual questionnaire data (Traditional Chinese and English versions)
- Responses measuring attitudes, subjective norms, self-efficacy, intentions, and organic farming behavior
- System-level moderating factors (certification ease, profit expectations)
- Demographic and farm characteristics
- Comprehensive documentation with construct definitions, item sources, and descriptive statistics

Data were collected March-May 2020 through structured face-to-face interviews across Taiwan's diverse agricultural systems. The modified TPB framework disaggregates perceived behavioral control into individual-level self-efficacy and system-level external conditions, treating the latter as moderators of intention-behavior conversion.

All data are de-identified and provided in Excel format compatible with SPSS/AMOS for structural equation modeling. Complete bilingual documentation ensures international accessibility while preserving cultural context. This dataset supports replication of published analyses and enables cross-cultural agricultural adoption research.