cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/pk57v3p26b
title: Ampliación del rango altitudinal del Gavilán Gris Buteo plagiatus, PNVI

description: Registro bioacústico de la presencia del gavilán gris (Buteo plagiatus) en el Parque Nacional Volcán Irazú (PNVI; 9° 57’ 26’’ N, -83° 53’ 15’’ O; 2742 msnm), Cartago, Costa Rica. Este registro amplia el rango de distribución de esta especie y es el primer registro para el PNVI.
DOI: 10.17632/pk57v3p26b.1
title: Ampliación del rango altitudinal del Gavilán Gris Buteo plagiatus, PNVI

description: Registro bioacústico de la presencia del gavilán gris (Buteo plagiatus) en el Parque Nacional Volcán Irazú (PNVI; 9° 57’ 26’’ N, -83° 53’ 15’’ O; 2742 msnm), Cartago, Costa Rica. Este registro amplia el rango de distribución de esta especie y es el primer registro para el PNVI.
DOI: 10.15468/dl.ekj3am
title: Occurrence Download

description: A dataset containing 444 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Antherospora",
 "HasCoordinate is true"
 ]
}

 The dataset includes 444 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039426-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25829/505w-7z44
title: Species-level hydro-functional traits (mean and sd) for the ten MyDiv tree species

description: Climate change is predicted to increase the frequency and duration of drought events, threatening the functioning of Central European forests. To study how individual tree growth during the drought event was dependend on the hydro-functional strategy of the tree species and if the tree growth reductions during drought could be buffered by the hydro-functional dissimilarity of the focal tree to its neighbouring trees, we created this dataset.
This dataset is a compiled data set of the tree species mean trait values of the 14 hydrofunctional traits, including water potential at 50 % loss of conductivity (P50), minimum water potential (Ψmin), difference between midday and predawn water potential (ΔΨ), wood density (WD), specific leaf area (SLA), leaf dry matter content (LDMC), stomata density (SD), guard cell length (GCL), mean stomatal conductance during drought (gs_drought), residual conductance at leaf VPD > 3 (g_res), leaf carbon isotope composition (leaf δ13C), hydraulically weighted vessel diameter (Dh), and vessel density (VD).
DOI: 10.17632/hbr34wzz65.4
title: Liberalisation, concentration and diversification: Business Groups in India, 2000-2020- Data Resources

description: We analyse the evolution of market structure in India between 2000 and 2020 using a rich dataset at high levels of disaggregation. We examine the extent to which business groups – notably family-owned groups – have sustained their dominant market positions in the Indian economy. We focus on two key dimensions. The first is the extent of concentration in markets and market shares by industry. The second concerns the dynamics and the extent to which business groups have focussed on consolidating their position in specific, narrow sectors or diversified by entering new sectors. We find that while market concentration has been falling, a bloc of high concentration sectors remains. Further, diversification has been actively pursued across sectors by most business groups. While this points to greater competition among business groups, the ratio of revenues to variable costs – a measure of the markup – has shifted upwards, particularly after 2013. The weight and persistence of these large business groups in the economy, as measured by the ratio of their revenues to GDP, has also increased. Finally, we discuss possible policy options. 

This dataset provides data for Figures and the STATA .do file for the replication of the replication of figures and tables. Due to restrictions on sharing the data by the data service provider "CMIE Prowessdx", the authors are not at liberty to share the dataset for the analysis.
DOI: 10.17632/s96xzc5hpb
title: Real-World Efficacy of Upadacitinib in a Diverse Alopecia Areata Cohort: A Retrospective Study

description: Supplementary TableⅠFactors Associated with Achieving SALT30, SALT50, and SALT80 Response Following Upadacitinib Treatment: A Univariate Logistic Regression Analysis
Supplementary Table II Treatment Response in Subgroups Based on Family History, Disease Duration, Age, and Prior JAK Inhibitor Exposure
DOI: 10.17632/s96xzc5hpb.1
title: Real-World Efficacy of Upadacitinib in a Diverse Alopecia Areata Cohort: A Retrospective Study

description: Supplementary TableⅠFactors Associated with Achieving SALT30, SALT50, and SALT80 Response Following Upadacitinib Treatment: A Univariate Logistic Regression Analysis
Supplementary Table II Treatment Response in Subgroups Based on Family History, Disease Duration, Age, and Prior JAK Inhibitor Exposure
DOI: 10.5281/zenodo.17426626
title: Supplemental Materials- Chronic kidney disease and cardiac remodeling potentiate cognitive impairment progression: disentangling the sex-specific crosstalk of kidney-heart-brain axis

description: Supplemental Materials for the manuscript "Chronic kidney disease and cardiac remodeling potentiate cognitive impairment progression: disentangling the sex-specific crosstalk of kidney-heart-brain axis".
DOI: 10.5281/zenodo.17426627
title: Supplemental Materials- Chronic kidney disease and cardiac remodeling potentiate cognitive impairment progression: disentangling the sex-specific crosstalk of kidney-heart-brain axis

description: Supplemental Materials for the manuscript "Chronic kidney disease and cardiac remodeling potentiate cognitive impairment progression: disentangling the sex-specific crosstalk of kidney-heart-brain axis".
DOI: 10.5281/zenodo.17632123
title: Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"

description: The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. 

 

 
description: We gratefully acknowledge the funding by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation; project number 268020496; TRR 172) within the Transregional Collaborative Research Centre “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3”. The authors are grateful for computing time from the Deutsches Klimarechenzentrum (DKRZ). Computing resources at DKRZ were granted under project number bb1005.The authors express their gratitude for the computing time granted by the Resource Allocation Board and utilized on the supercomputers Lise and Emmy at NHR@ZIB and NHR@Göttingen as a component of the NHR infrastructure. The calculations for this research were conducted with computing resources under the project hbk00084.
DOI: 10.5281/zenodo.13235177
title: Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"

description: The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. 

 

 
description: We gratefully acknowledge the funding by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation; project number 268020496; TRR 172) within the Transregional Collaborative Research Centre “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3”. The authors are grateful for computing time from the Deutsches Klimarechenzentrum (DKRZ). Computing resources at DKRZ were granted under project number bb1005.The authors express their gratitude for the computing time granted by the Resource Allocation Board and utilized on the supercomputers Lise and Emmy at NHR@ZIB and NHR@Göttingen as a component of the NHR infrastructure. The calculations for this research were conducted with computing resources under the project hbk00084.
DOI: 10.17632/mnv8wyy8s9
title: Supplementary Table for Real-World Efficacy of Upadacitinib in a Diverse Alopecia Areata Cohort: A Retrospective Study

description: Supplementary Table
DOI: 10.17632/mnv8wyy8s9.1
title: Supplementary Table for Real-World Efficacy of Upadacitinib in a Diverse Alopecia Areata Cohort: A Retrospective Study

description: Supplementary Table
DOI: 10.17632/2b63rx67xt
title: Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2025

description: This compilation of historical daily stock market price data relates to the Kenyan Nairobi Securities Exchange (NSE) for year 2025 (upto Oct.). This data is valuable for any machine learning algorithm that needs data (training, validation, testing). This compilation builds on an earlier dataset (2008-2012) that was initially compiled as part of a research project to predict the next day's stock price, based on the previous five days, using Artificial Neural Networks (ANN). This initial research [1],[2] tested 6 stocks[3] using ANN of configuration 5:21:21:1. The data was then enhanced as a new compilation of all stocks for the period 2007-2012[4].

This new dataset augments the NSE dataset from 2007-2012[4], 2013-2020[5], 2021[6], 2022[7], and 2023-2024[8]. The data was scraped from a publicly accessible website[9] licensed by NSE by exporting raw web data to spreadsheets, then cleaning it up to final CSV.

Just like the previous compilations, each stock data row has 13 columns (1)Date (2)Stock Code (3)Stock Name (4)12month LowPrice (5)12month HighPrice (6)Day LowPrice (7)Day HighPrice (8)Day FinalPrice (9)Previous traded price (10)Change in price value (11)Change in price% (12)Volume traded (13)Adjusted price. One additional CSV file is also provided to show the stock market sector, with 3 columns (1)Market sector (2)Stock Code (3)Stock Name.

This additional dataset provides researchers with an even larger dataset (2007-2025) of stock market data, including market sector information, for machine learning research.

List of data files on this dataset:
NSE_data_all_stocks_2025_jan_to_oct.csv
NSE_data_stock_market_sectors_2025.csv

References:
[1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi), https://erepository.uonbi.ac.ke/handle/11295/71466
[2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434.
[3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3
[4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1
[5] Wanjawa, Barack (2021), “Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020”, Mendeley Data, V2, doi: 10.17632/73rb78pmzw.2
[6] Wanjawa, Barack (2022), “Nairobi Securities Exchange (NSE) Kenya - All Stocks Prices 2021”, Mendeley Data, V5, doi: 10.17632/97hkwn5y3x.5
[7] Wanjawa, Barack (2024), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2022”, Mendeley Data, V2, doi: 10.17632/jmcdmnyh2s.2
[8] Wanjawa, Barack (2025), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2023-2024”, Mendeley Data, V2, doi: 10.17632/ss5pfw8xnk.2
[9] Synergy Systems Ltd. (2023). MyStocks. Retrieved Nov. 15, 2025, from http://live.mystocks.co.ke/
DOI: 10.17632/2b63rx67xt.1
title: Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2025

description: This compilation of historical daily stock market price data relates to the Kenyan Nairobi Securities Exchange (NSE) for year 2025 (upto Oct.). This data is valuable for any machine learning algorithm that needs data (training, validation, testing). This compilation builds on an earlier dataset (2008-2012) that was initially compiled as part of a research project to predict the next day's stock price, based on the previous five days, using Artificial Neural Networks (ANN). This initial research [1],[2] tested 6 stocks[3] using ANN of configuration 5:21:21:1. The data was then enhanced as a new compilation of all stocks for the period 2007-2012[4].

This new dataset augments the NSE dataset from 2007-2012[4], 2013-2020[5], 2021[6], 2022[7], and 2023-2024[8]. The data was scraped from a publicly accessible website[9] licensed by NSE by exporting raw web data to spreadsheets, then cleaning it up to final CSV.

Just like the previous compilations, each stock data row has 13 columns (1)Date (2)Stock Code (3)Stock Name (4)12month LowPrice (5)12month HighPrice (6)Day LowPrice (7)Day HighPrice (8)Day FinalPrice (9)Previous traded price (10)Change in price value (11)Change in price% (12)Volume traded (13)Adjusted price. One additional CSV file is also provided to show the stock market sector, with 3 columns (1)Market sector (2)Stock Code (3)Stock Name.

This additional dataset provides researchers with an even larger dataset (2007-2025) of stock market data, including market sector information, for machine learning research.

List of data files on this dataset:
NSE_data_all_stocks_2025_jan_to_oct.csv
NSE_data_stock_market_sectors_2025.csv

References:
[1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi), https://erepository.uonbi.ac.ke/handle/11295/71466
[2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434.
[3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3
[4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1
[5] Wanjawa, Barack (2021), “Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020”, Mendeley Data, V2, doi: 10.17632/73rb78pmzw.2
[6] Wanjawa, Barack (2022), “Nairobi Securities Exchange (NSE) Kenya - All Stocks Prices 2021”, Mendeley Data, V5, doi: 10.17632/97hkwn5y3x.5
[7] Wanjawa, Barack (2024), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2022”, Mendeley Data, V2, doi: 10.17632/jmcdmnyh2s.2
[8] Wanjawa, Barack (2025), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2023-2024”, Mendeley Data, V2, doi: 10.17632/ss5pfw8xnk.2
[9] Synergy Systems Ltd. (2023). MyStocks. Retrieved Nov. 15, 2025, from http://live.mystocks.co.ke/
DOI: 10.25829/4hbe-ne61
title: Branch hydro-functional traits of all MyDiv tree species grown in monocultures

description: Climate change is projected to increase the frequency and duration of drought events, threatening the functioning and stability of Central European forests. To investigate how individual tree growth during the 2018–2020 drought depended on species-specific hydro-functional strategies, and whether growth reductions could be buffered by hydro-functional dissimilarity between a focal tree and its neighbors, we established this dataset.

The dataset contains measurements of three groups of hydraulic and structural functional traits: embolism resistance (P50), wood density, and xylem anatomy. Tree branches were sampled from monoculture plots of the MyDiv Experiment in autumn 2021, and in summer 2024 for two ring-porous species. These traits characterize species-specific differences in water transport efficiency, safety, and drought tolerance, providing key information on tree hydraulic functioning.

The data can be used to analyze interspecific variation in drought response, explore trade-offs among hydraulic traits, and assess how functional diversity contributes to the resilience of temperate forest ecosystems under climate stress.
DOI: 10.17632/ss5pfw8xnk
title: Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2023-2024

description: This compilation of historical daily stock market price data relates to the Kenyan Nairobi Securities Exchange (NSE) for 2023 and 2024. This data is valuable for any machine learning algorithm that needs data (training, validation, testing). This compilation develops on an earlier dataset (2008-2012) that was initially compiled as part of a research project to predict next day stock price, based on the previous five days, using Artificial Neural Networks (ANN). This initial research [1],[2] tested 6 stocks [3] using ANN of configuration 5:21:21:1. The data was then enhanced as a new compilation of all stocks for the period 2007-2012 [4].

This new dataset augments the NSE dataset for 2007-2012 [4], 2013 to 2020 [5], 2021 [6] and 2022 [7]. The data was scrapping from a publicly accessible website [8] licensed by NSE by exporting raw web data to spreadsheets, then cleaning it up to final CSV.

Just like the previous compilations, each stock data row has 13 data columns (1)Date (2)Stock Code (3)Stock Name (4)12-month Low price (5)12-month High price (6)Day's Low price (7)Day's High price (8)Day's Final Price (9)Previous traded price (10)Change in price value (11)Change in price % (12)Volume traded (13)Adjusted price. One additional CSV file is also provided to show the stocks market sector, with 3 columns as: (1)Market sector (2)Stock Code (3)Stock Name.

This additional dataset provides researchers with an even larger dataset (2007-2024) of stocks market data including market sector information for bigger opportunities of data analysis and usage in machine learning research.

List of data files on this dataset: 
NSE_data_all_stocks_2023.csv 
NSE_data_all_stocks_2024.csv 
NSE_data_stock_market_sectors_2023_2024.csv

References: 
[1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi). 
[2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434. 
[3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3 
[4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1 
[5] Wanjawa, Barack (2021), “Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020”, Mendeley Data, V2, doi: 10.17632/73rb78pmzw.2 
[6] Wanjawa, Barack (2022), “Nairobi Securities Exchange (NSE) Kenya - All Stocks Prices 2021”, Mendeley Data, V5, doi: 10.17632/97hkwn5y3x.5
[7] Wanjawa, Barack (2024), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2022”, Mendeley Data, V2, doi: 10.17632/jmcdmnyh2s.2
[8] Synergy Systems Ltd. (2023). MyStocks. Retrieved Jan 31, 2023, from http://live.mystocks.co.ke/
DOI: 10.17632/ss5pfw8xnk.3
title: Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2023-2024

description: This compilation of historical daily stock market price data relates to the Kenyan Nairobi Securities Exchange (NSE) for 2023 and 2024. This data is valuable for any machine learning algorithm that needs data (training, validation, testing). This compilation develops on an earlier dataset (2008-2012) that was initially compiled as part of a research project to predict next day stock price, based on the previous five days, using Artificial Neural Networks (ANN). This initial research [1],[2] tested 6 stocks [3] using ANN of configuration 5:21:21:1. The data was then enhanced as a new compilation of all stocks for the period 2007-2012 [4].

This new dataset augments the NSE dataset for 2007-2012 [4], 2013 to 2020 [5], 2021 [6] and 2022 [7]. The data was scrapping from a publicly accessible website [8] licensed by NSE by exporting raw web data to spreadsheets, then cleaning it up to final CSV.

Just like the previous compilations, each stock data row has 13 data columns (1)Date (2)Stock Code (3)Stock Name (4)12-month Low price (5)12-month High price (6)Day's Low price (7)Day's High price (8)Day's Final Price (9)Previous traded price (10)Change in price value (11)Change in price % (12)Volume traded (13)Adjusted price. One additional CSV file is also provided to show the stocks market sector, with 3 columns as: (1)Market sector (2)Stock Code (3)Stock Name.

This additional dataset provides researchers with an even larger dataset (2007-2024) of stocks market data including market sector information for bigger opportunities of data analysis and usage in machine learning research.

List of data files on this dataset: 
NSE_data_all_stocks_2023.csv 
NSE_data_all_stocks_2024.csv 
NSE_data_stock_market_sectors_2023_2024.csv

References: 
[1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi). 
[2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434. 
[3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3 
[4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1 
[5] Wanjawa, Barack (2021), “Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020”, Mendeley Data, V2, doi: 10.17632/73rb78pmzw.2 
[6] Wanjawa, Barack (2022), “Nairobi Securities Exchange (NSE) Kenya - All Stocks Prices 2021”, Mendeley Data, V5, doi: 10.17632/97hkwn5y3x.5
[7] Wanjawa, Barack (2024), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2022”, Mendeley Data, V2, doi: 10.17632/jmcdmnyh2s.2
[8] Synergy Systems Ltd. (2023). MyStocks. Retrieved Jan 31, 2023, from http://live.mystocks.co.ke/
DOI: 10.5281/zenodo.17632957
title: Material Suplementar - O apoio da Inteligência Artificial no Desenvolvimento Ágil de Software: uma revisão da literatura
DOI: 10.5281/zenodo.16934252
title: Material Suplementar - O apoio da Inteligência Artificial no Desenvolvimento Ágil de Software: uma revisão da literatura
DOI: 10.7910/dvn/vxr3xb
title: Replication Data for: The Impact of Unions on Non-union Wage Setting: Threats and Bargaining

description: This is the replication package for " The Impact of Unions on Non-union Wage Setting: Threats and Bargaining", accepted in 2025 by the Journal of Political Economy