cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sddjvb4c4r.1
title: Dataset Learning Environment

description: This dataset contains raw data collected for a study on the development and validation of a University Learning Environment Instrument specifically for the Indonesian higher education context. The data collection was conducted in two separate phases to ensure the psychometric properties of the scale.

The dataset consists of two subsets corresponding to two studies:

a. Study 1 (Exploratory Factor Analysis / EFA):
Purpose: To identify the underlying dimensions of the learning environment construct.
Sample: N = 200 undergraduate students in Indonesia.
Demographics: 23 Males, 177 Females.
Data Content: Responses to the initial pool of items.

b. Study 2 (Validation Phase):
Purpose: To confirm the structure obtained from Study 1 using the same criteria and collection methods.
Sample: N = 241 undergraduate students in Indonesia.
Demographics: 55 Males, 186 Females.
Data Content: Responses to the refined items.

Variables included:
Demographic data: Gender (Sex).
Item responses: Likert-scale responses for the learning environment instrument items.

All data has been anonymized to protect participant privacy. This dataset is provided to support the transparency and reproducibility of the associated research article.
DOI: 10.17632/8w3zjj8hzc
title: ChanEst Dataset for Deep Learning-Based 6G Channel Estimation

description: ChanEst dataset was developed to address the core research challenge that accurate channel estimation is increasingly difficult due to highly diverse, dynamic, and extreme propagation environments in 6G. Although deep learning (DL) techniques have shown strong potential as alternatives to conventional estimators, their progress is limited due to the lack of reproducible and reconfigurable datasets that adhere to 3GPP compliance and realistic receiver preprocessing. ChanEst directly responds to this gap by providing a fully controlled, standards aligned dataset generation framework designed for DL based channel estimation.

ChanEst is generated using the 6G Exploration Library in MATLAB and strictly follows 3GPP physical layer specifications. Each sample is constructed on an OFDM grid (612 subcarriers × 14 symbols) populated with DM RS Type 2 pilots (3GPP TS 38.211). The transmitted signals propagate through standardized 3GPP TDL channel models (TDL A to TDL E), while channel parameters, SNR (−10 to 30 dB), delay spread (10–2000 ns), and Doppler shift (5–5000 Hz), are stratified to ensure wide and balanced scenario diversity representative of 6G FR3 operation at 7 GHz. This design prevents overrepresentation of mild or unrealistic cases and ensures coverage of high mobility and high dispersion conditions.

Each dataset entry consists of two tensors:
• X_input: LS channel estimates at pilot positions followed by 2 D linear interpolation, representing the noisy and imperfect receiver perspective.
• Y_label: perfect OFDM grid channel responses, serving as the supervised learning target.
Both tensors are stored in real valued format, with channels represented as [K × L × C × N], where C = 2 × NTx × NRx to pack real and imaginary components. The framework supports SISO and MIMO, configurable numerologies, and user defined parameter ranges. Dataset size is scalable based on computational capacity, making ChanEst suitable for lightweight prototyping as well as large scale model training.

Extensive validation confirms that ChanEst exhibits strong physical consistency. Correlation between X_input and Y_label is high under mild channel conditions and degrades appropriately under severe scenarios, reflecting real world estimation difficulty. NMSE trends align with expected impacts of noise, delay spread, and mobility, demonstrating that the dataset avoids degenerate or trivial cases. Scenario distributions remain balanced across propagation regimes, ensuring fairness and robustness in DL model benchmarking.

ChanEst is delivered with detailed metadata, including SNR, Doppler, delay spread, and TDL profile, enabling stratified evaluation, targeted stress testing, and reproducible comparisons against classical estimators. Its flexibility allows researchers to reconfigure antenna dimensions, numerology, and parameter ranges to suit diverse DL-based research tasks in 6G and beyond. The dataset is available in MATLAB (.mat) and HDF5 (.h5) formats.
DOI: 10.17632/8w3zjj8hzc.1
title: ChanEst Dataset for Deep Learning-Based 6G Channel Estimation

description: ChanEst dataset was developed to address the core research challenge that accurate channel estimation is increasingly difficult due to highly diverse, dynamic, and extreme propagation environments in 6G. Although deep learning (DL) techniques have shown strong potential as alternatives to conventional estimators, their progress is limited due to the lack of reproducible and reconfigurable datasets that adhere to 3GPP compliance and realistic receiver preprocessing. ChanEst directly responds to this gap by providing a fully controlled, standards aligned dataset generation framework designed for DL based channel estimation.

ChanEst is generated using the 6G Exploration Library in MATLAB and strictly follows 3GPP physical layer specifications. Each sample is constructed on an OFDM grid (612 subcarriers × 14 symbols) populated with DM RS Type 2 pilots (3GPP TS 38.211). The transmitted signals propagate through standardized 3GPP TDL channel models (TDL A to TDL E), while channel parameters, SNR (−10 to 30 dB), delay spread (10–2000 ns), and Doppler shift (5–5000 Hz), are stratified to ensure wide and balanced scenario diversity representative of 6G FR3 operation at 7 GHz. This design prevents overrepresentation of mild or unrealistic cases and ensures coverage of high mobility and high dispersion conditions.

Each dataset entry consists of two tensors:
• X_input: LS channel estimates at pilot positions followed by 2 D linear interpolation, representing the noisy and imperfect receiver perspective.
• Y_label: perfect OFDM grid channel responses, serving as the supervised learning target.
Both tensors are stored in real valued format, with channels represented as [K × L × C × N], where C = 2 × NTx × NRx to pack real and imaginary components. The framework supports SISO and MIMO, configurable numerologies, and user defined parameter ranges. Dataset size is scalable based on computational capacity, making ChanEst suitable for lightweight prototyping as well as large scale model training.

Extensive validation confirms that ChanEst exhibits strong physical consistency. Correlation between X_input and Y_label is high under mild channel conditions and degrades appropriately under severe scenarios, reflecting real world estimation difficulty. NMSE trends align with expected impacts of noise, delay spread, and mobility, demonstrating that the dataset avoids degenerate or trivial cases. Scenario distributions remain balanced across propagation regimes, ensuring fairness and robustness in DL model benchmarking.

ChanEst is delivered with detailed metadata, including SNR, Doppler, delay spread, and TDL profile, enabling stratified evaluation, targeted stress testing, and reproducible comparisons against classical estimators. Its flexibility allows researchers to reconfigure antenna dimensions, numerology, and parameter ranges to suit diverse DL-based research tasks in 6G and beyond. The dataset is available in MATLAB (.mat) and HDF5 (.h5) formats.
DOI: 10.17632/87z4v4gkzm
title: tripeptide_cln025_data

description: "tripeptide_nowater.dcd" : Exercise 1 (trajectory files of alanine tripeptide removing water molecules and ions)
 "tripeptide_nowater.psf": Exercise 1 (topology files of alanine tripeptide removing water molecules and ions)
"tri_sin_phi_data.npz": Exercise 1, 2 (Extract the sine values of six backbone dihedral angles of tripeptide trajectory, together with the corresponding dihedral angle values (φ) and save as a npz file)
 "cln025_aligned.npz": Exercise 3 (Align a CLN025 dcd trajectory to a reference structure and output a npz file of aligned Cartesian coordinates)
 "chig_sin_rmsd_rg_data.npz": Exercise 3 (Extract and store sine-transformed dihedral angles, RMSD, and Rg data from CLN025 trajectory and output a npz file )
DOI: 10.17632/87z4v4gkzm.1
title: tripeptide_cln025_data

description: "tripeptide_nowater.dcd" : Exercise 1 (trajectory files of alanine tripeptide removing water molecules and ions)
 "tripeptide_nowater.psf": Exercise 1 (topology files of alanine tripeptide removing water molecules and ions)
"tri_sin_phi_data.npz": Exercise 1, 2 (Extract the sine values of six backbone dihedral angles of tripeptide trajectory, together with the corresponding dihedral angle values (φ) and save as a npz file)
 "cln025_aligned.npz": Exercise 3 (Align a CLN025 dcd trajectory to a reference structure and output a npz file of aligned Cartesian coordinates)
 "chig_sin_rmsd_rg_data.npz": Exercise 3 (Extract and store sine-transformed dihedral angles, RMSD, and Rg data from CLN025 trajectory and output a npz file )
DOI: 10.17632/n97sjjc2nm
title: Data associated with "Co-opting the bacterial lipoprotein pathway in the biosynthesis of a lipidated macrocyclic peptide" PNAS 2026

description: all data associated with figures in main text and Supplementary Information of "Co-opting the bacterial lipoprotein pathway for the biosynthesis of lipidated macrocyclic peptides", PNAS 2026
DOI: 10.17632/n97sjjc2nm.1
title: Data associated with "Co-opting the bacterial lipoprotein pathway in the biosynthesis of a lipidated macrocyclic peptide" PNAS 2026

description: all data associated with figures in main text and Supplementary Information of "Co-opting the bacterial lipoprotein pathway for the biosynthesis of lipidated macrocyclic peptides", PNAS 2026
DOI: 10.17632/ndzwhs9gyf
title: Dataset for: Estimation and sensitivity analysis of modal dispersions of borehole acoustic wireline logging for anisotropic formation parameters by the perturbation method

description: This dataset accompanies the manuscript and is accessible using Octave.
DOI: 10.17632/ndzwhs9gyf.1
title: Dataset for: Estimation and sensitivity analysis of modal dispersions of borehole acoustic wireline logging for anisotropic formation parameters by the perturbation method

description: This dataset accompanies the manuscript and is accessible using Octave.
DOI: 10.15468/dl.xd95zf
title: Occurrence Download

description: A dataset containing 1617 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Piptadenia gonoacantha",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 1617 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010992-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wrv45r462b
title: Dataset with values of parameters of Short assessment of renewable energy sources regarding population collected for 2022

description: The dataset includes values of parameters of Short assessment of renewable energy sources regarding population collected for selected 29 European countries for 2022. The dataset was used in the research paper for the multi-criteria problem of evaluating European countries in relation to the sustainable share of renewable energy sources in particular domains of the economy. Data was grasped from the Eurostat website in the SHARES (Short assessment of renewable energy sources) collection. The criteria of the multi-criteria assessment model of European countries with the share of RES in different areas of the economy are included in the dataset.

The dataset is related to the research paper:
Bączkiewicz, A., Dowejko, J. (2025). Using SSP-VIKOR in Sustainable Share of Renewable Energy Sources Assessment. In: Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds) Computational Science – ICCS 2025 Workshops. ICCS 2025. Lecture Notes in Computer Science, vol 15910. Springer, Cham. https://doi.org/10.1007/978-3-031-97567-7_16
DOI: 10.17632/wrv45r462b.1
title: Dataset with values of parameters of Short assessment of renewable energy sources regarding population collected for 2022

description: The dataset includes values of parameters of Short assessment of renewable energy sources regarding population collected for selected 29 European countries for 2022. The dataset was used in the research paper for the multi-criteria problem of evaluating European countries in relation to the sustainable share of renewable energy sources in particular domains of the economy. Data was grasped from the Eurostat website in the SHARES (Short assessment of renewable energy sources) collection. The criteria of the multi-criteria assessment model of European countries with the share of RES in different areas of the economy are included in the dataset.

The dataset is related to the research paper:
Bączkiewicz, A., Dowejko, J. (2025). Using SSP-VIKOR in Sustainable Share of Renewable Energy Sources Assessment. In: Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds) Computational Science – ICCS 2025 Workshops. ICCS 2025. Lecture Notes in Computer Science, vol 15910. Springer, Cham. https://doi.org/10.1007/978-3-031-97567-7_16
DOI: 10.15468/dl.39bzmg
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corumbataia veadeiros",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 11 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010993-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/t87wryn4fb
title: Dataset for sustainability of health systems assessment

description: This dataset is collected with the aim of analyzing the sustainability of European Union countries, the USA, and Switzerland in terms of 15 evaluation criteria (C1-C15) belonging to four main dimensions (G1-G4) listed in Table 1 in attached PDF file with data description, where the UK refers to the United Kingdom. The conducted investigation uses the most recent and complete data obtained from the United Nations, SDG Indicators Database (https://unstats.un.org/sdgs/dataportal/database).

The dataset was collected from the following data sources:

The conducted investigation uses the most recent and complete data obtained from United Nations, SDG Indicators Database: https://unstats.un.org/sdgs/dataportal/database

The missing data was completed from the following sources:

Criterion C5:

For Europe:
Austria, Finland, Hungary, Ireland, Norway, Slovenia, Spain, Sweden, Switzerland, United Kingdom:
https://www.ecdc.europa.eu/sites/default/files/documents/HIV-AIDS_surveillance_in_Europe_2023_%28_2022_data_%29_0.pdf

Tables, page. 33

For United States of America:
https://www.hiv.gov/hiv-basics/overview/data-and-trends/statistics

Key Points: HIV Incidence:
In 2022, there were 31,800 estimated new HIV infections in the U.S. and the rate was 11.3 (per 100,000 people).

Criteria C14 and C15 for Luxembourg:
https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/12/luxembourg-country-health-profile-2023_24906497/3cf98074-en.pdf

In the last 10 years, the ratio of doctors in Luxembourg has increased from 2.8 to 3.0 doctors per 1 000 population (in 2017)

In contrast, the number of nurses in Luxembourg is one of the highest in the EU (at approximately 11.7 nurses per 1 000 population in 2017

The decision matrix with performance values collected for considered countries is provided in Table 2 in attached PDF file with data description. This dataset is also provided in the XLSX file named dataset.xlsx.

The dataset is related to the research paper:
Bączkiewicz, A., Wątróbski, J., Rudawska, I. (2025). Is Health Systems Sustainability Measurable? - Operationalizing SDG Targets Using SSP-TOPSIS Approach. In: Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds) Computational Science – ICCS 2025 Workshops. ICCS 2025. Lecture Notes in Computer Science, vol 15908. Springer, Cham. https://doi.org/10.1007/978-3-031-97557-8_14
DOI: 10.15468/dl.ve3quh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corsia pyramidata",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010994-260208012135463/datasets/export for details.
DOI: 10.15468/dl.pg3kys
title: Occurrence Download

description: A dataset containing 181036 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "OccurrenceStatus is Present",
 "TaxonKey is Sciurus vulgaris"
 ]
}

 The dataset includes 181036 records from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010938-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/t87wryn4fb.1
title: Dataset for sustainability of health systems assessment

description: This dataset is collected with the aim of analyzing the sustainability of European Union countries, the USA, and Switzerland in terms of 15 evaluation criteria (C1-C15) belonging to four main dimensions (G1-G4) listed in Table 1 in attached PDF file with data description, where the UK refers to the United Kingdom. The conducted investigation uses the most recent and complete data obtained from the United Nations, SDG Indicators Database (https://unstats.un.org/sdgs/dataportal/database).

The dataset was collected from the following data sources:

The conducted investigation uses the most recent and complete data obtained from United Nations, SDG Indicators Database: https://unstats.un.org/sdgs/dataportal/database

The missing data was completed from the following sources:

Criterion C5:

For Europe:
Austria, Finland, Hungary, Ireland, Norway, Slovenia, Spain, Sweden, Switzerland, United Kingdom:
https://www.ecdc.europa.eu/sites/default/files/documents/HIV-AIDS_surveillance_in_Europe_2023_%28_2022_data_%29_0.pdf

Tables, page. 33

For United States of America:
https://www.hiv.gov/hiv-basics/overview/data-and-trends/statistics

Key Points: HIV Incidence:
In 2022, there were 31,800 estimated new HIV infections in the U.S. and the rate was 11.3 (per 100,000 people).

Criteria C14 and C15 for Luxembourg:
https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/12/luxembourg-country-health-profile-2023_24906497/3cf98074-en.pdf

In the last 10 years, the ratio of doctors in Luxembourg has increased from 2.8 to 3.0 doctors per 1 000 population (in 2017)

In contrast, the number of nurses in Luxembourg is one of the highest in the EU (at approximately 11.7 nurses per 1 000 population in 2017

The decision matrix with performance values collected for considered countries is provided in Table 2 in attached PDF file with data description. This dataset is also provided in the XLSX file named dataset.xlsx.

The dataset is related to the research paper:
Bączkiewicz, A., Wątróbski, J., Rudawska, I. (2025). Is Health Systems Sustainability Measurable? - Operationalizing SDG Targets Using SSP-TOPSIS Approach. In: Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds) Computational Science – ICCS 2025 Workshops. ICCS 2025. Lecture Notes in Computer Science, vol 15908. Springer, Cham. https://doi.org/10.1007/978-3-031-97557-8_14
DOI: 10.17632/m8r4zt246z
title: Dataset with parameters of selected smart mobile devices considered to be utilized in mobile crowd computing

description: This dataset includes a decision matrix with values of parameters of selected smart mobile devices considered to be utilized in mobile crowd computing. The dataset was collected from the research paper:
Pramanik, P. K. D., Biswas, S., Pal, S., Marinković, D., & Choudhury, P. (2021). A comparative analysis of multi-criteria decision-making methods for resource selection in mobile crowd computing. Symmetry, 13(9), 1713.
Dataset with two-dimensional decision matrix is provided in XLSX file.
The dataset is related to the research paper:
Wątróbski, J., Bączkiewicz, A. & Karczmarczyk, A. (2025). Using SSP-TOPSIS in Sustainable Resource Selection for Mobile Crowd ComputingIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.82
DOI: 10.17632/m8r4zt246z.1
title: Dataset with parameters of selected smart mobile devices considered to be utilized in mobile crowd computing

description: This dataset includes a decision matrix with values of parameters of selected smart mobile devices considered to be utilized in mobile crowd computing. The dataset was collected from the research paper:
Pramanik, P. K. D., Biswas, S., Pal, S., Marinković, D., & Choudhury, P. (2021). A comparative analysis of multi-criteria decision-making methods for resource selection in mobile crowd computing. Symmetry, 13(9), 1713.
Dataset with two-dimensional decision matrix is provided in XLSX file.
The dataset is related to the research paper:
Wątróbski, J., Bączkiewicz, A. & Karczmarczyk, A. (2025). Using SSP-TOPSIS in Sustainable Resource Selection for Mobile Crowd ComputingIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.82
DOI: 10.17632/h523cjrgwx
title: Datasets including performances for multi-criteria temporal evaluation of SDG 7 implementation by European countries collected for 2017-2022

description: This dataset comprises data collected from 2017 to 2022, organized in separate XLSX and CSV files for each year. Files country_names.xlsx and country_names.csv include abbreviations and full names of countries considered in this research. The data were utilized for a time-based decision-making goal focused on evaluating how selected European nations are progressing toward the targets assigned in Sustainable Development Goal 7 (SDG 7), regarding accessible and environmentally friendly energy. Key dimensions of evaluation include enhancing energy efficiency, expanding the use of renewable resources, and diversifying energy portfolios, all while maintaining energy affordability for the population. The specific goals used as evaluation criteria within the assessment framework are listed in data.pdf file describing provided dataset.

The dataset is related to the research paper:
Wątróbski, J. (2025). New Multi-Criteria Approach to Sustainable Development Assessment. In: Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds) Computational Science – ICCS 2025 Workshops. ICCS 2025. Lecture Notes in Computer Science, vol 15910. Springer, Cham. https://doi.org/10.1007/978-3-031-97567-7_18