cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fpwwvykfmh
title: Slope deformation velocities and controlling factors (Original data)

description: Research hypothesis
We hypothesize that slope instability in mining-affected landscapes is jointly controlled by geological, topographic, climatic, and anthropogenic factors, and that deformation patterns derived from InSAR can be systematically linked to these variables. In particular, intermittently unstable slopes are expected to show periodic responses to dynamic environmental forcing (e.g., rainfall, temperature, snow cover, vegetation).

Data description
This dataset contains slope-unit based deformation velocities and associated variables in the Datong Coalfield, China, derived from multi-temporal SBAS-InSAR analysis between May 2019 and April 2025. The data are organized into three groups:

Mean deformation and variables (TS1, TS2, TWs)
Slopes_DeformationVelocity_Variables_TS1.xlsx: Mean LOS deformation velocities (mm/year) and controlling variables for each slope unit during TS1 (2019–2022).
Slopes_DeformationVelocity_Variables_TS2.xlsx: Same as above for TS2 (2022–2025).
Slopes_DeformationVelocity_Variables_TWs.xlsx: Slope-unit deformation and variables across 12 short-term time windows (TWs).

Time series of deformation vs. dynamic variables
Deformation_vs_Variables_StableSlopes.xlsx: 2019–2025 deformation and environmental variables for slope units classified as stable.
Deformation_vs_Variables_IntermittentlyUnstableSlopes.xlsx: Same for intermittently unstable slopes.
Deformation_vs_Variables_UnstableSlopes.xlsx: Same for unstable slopes.

Each file is provided in Excel .xlsx format, with rows representing slope units and columns representing deformation velocities and variables (slope angle, aspect, lithology, distance to faults, distance to mines, mine density, rainfall, land surface temperature, snow cover, NDVI).

Notable findings
Mean deformation velocities allowed classification of slope units into stable, intermittently unstable, and unstable categories using a 10 mm/year threshold.
Long-term modeling showed that geological and topographic factors exert persistent influences, while climatic and anthropogenic variables displayed greater temporal variability.
Intermittently unstable slopes exhibited periodic lagged associations with rainfall, temperature, snow cover, and vegetation activity.

Interpretation and use
The dataset provides slope-unit scale information suitable for:
Investigating the controls of slope instability in mining-affected landscapes.
Developing new methods for spatiotemporal analysis of slope deformation.
Cross-referencing with independent datasets (e.g., precipitation, geology, mining activity).

Data gathering
Surface deformation was derived from Sentinel-1 SAR images processed with the SBAS-InSAR technique.
Static variables were extracted from DEMs, geological maps, and mining activity datasets.
Dynamic variables (rainfall, LST, snow cover, NDVI) were obtained from remote sensing and meteorological products.
All variables were aggregated to slope units delineated using GRASS GIS.
DOI: 10.17632/fpwwvykfmh.1
title: Slope deformation velocities and controlling factors (Original data)

description: Research hypothesis
We hypothesize that slope instability in mining-affected landscapes is jointly controlled by geological, topographic, climatic, and anthropogenic factors, and that deformation patterns derived from InSAR can be systematically linked to these variables. In particular, intermittently unstable slopes are expected to show periodic responses to dynamic environmental forcing (e.g., rainfall, temperature, snow cover, vegetation).

Data description
This dataset contains slope-unit based deformation velocities and associated variables in the Datong Coalfield, China, derived from multi-temporal SBAS-InSAR analysis between May 2019 and April 2025. The data are organized into three groups:

Mean deformation and variables (TS1, TS2, TWs)
Slopes_DeformationVelocity_Variables_TS1.xlsx: Mean LOS deformation velocities (mm/year) and controlling variables for each slope unit during TS1 (2019–2022).
Slopes_DeformationVelocity_Variables_TS2.xlsx: Same as above for TS2 (2022–2025).
Slopes_DeformationVelocity_Variables_TWs.xlsx: Slope-unit deformation and variables across 12 short-term time windows (TWs).

Time series of deformation vs. dynamic variables
Deformation_vs_Variables_StableSlopes.xlsx: 2019–2025 deformation and environmental variables for slope units classified as stable.
Deformation_vs_Variables_IntermittentlyUnstableSlopes.xlsx: Same for intermittently unstable slopes.
Deformation_vs_Variables_UnstableSlopes.xlsx: Same for unstable slopes.

Each file is provided in Excel .xlsx format, with rows representing slope units and columns representing deformation velocities and variables (slope angle, aspect, lithology, distance to faults, distance to mines, mine density, rainfall, land surface temperature, snow cover, NDVI).

Notable findings
Mean deformation velocities allowed classification of slope units into stable, intermittently unstable, and unstable categories using a 10 mm/year threshold.
Long-term modeling showed that geological and topographic factors exert persistent influences, while climatic and anthropogenic variables displayed greater temporal variability.
Intermittently unstable slopes exhibited periodic lagged associations with rainfall, temperature, snow cover, and vegetation activity.

Interpretation and use
The dataset provides slope-unit scale information suitable for:
Investigating the controls of slope instability in mining-affected landscapes.
Developing new methods for spatiotemporal analysis of slope deformation.
Cross-referencing with independent datasets (e.g., precipitation, geology, mining activity).

Data gathering
Surface deformation was derived from Sentinel-1 SAR images processed with the SBAS-InSAR technique.
Static variables were extracted from DEMs, geological maps, and mining activity datasets.
Dynamic variables (rainfall, LST, snow cover, NDVI) were obtained from remote sensing and meteorological products.
All variables were aggregated to slope units delineated using GRASS GIS.
DOI: 10.17632/xnc6syy642
title: Supplementary Table 1. Three SNPs co-segregated with the Xwgc2315 STS locus on distal 7B; SNP map positions from Wang et al. (2014), STS marker details from Zhang et al. (2022).

description: Three SNP polymorphisms co-segregated with the Xwgc2315 STS locus located at the distal end of chromosome 7B. The consensus map positions of the three SNP loci were provided by ​(S. Wang et al., 2014)​ and the STS marker detail were obtained from ​W. Zhang et al. (2022)​
DOI: 10.17632/xnc6syy642.1
title: Supplementary Table 1. Three SNPs co-segregated with the Xwgc2315 STS locus on distal 7B; SNP map positions from Wang et al. (2014), STS marker details from Zhang et al. (2022).

description: Three SNP polymorphisms co-segregated with the Xwgc2315 STS locus located at the distal end of chromosome 7B. The consensus map positions of the three SNP loci were provided by ​(S. Wang et al., 2014)​ and the STS marker detail were obtained from ​W. Zhang et al. (2022)​
DOI: 10.17632/wj9gnpgcv8
title: Meteorological Dataset – Southern Dominican Republic

description: This dataset includes both raw meteorological measurements and cleaned, hourly aggregated data collected from four provinces in the southern Dominican Republic: San Cristóbal, Azua, Barahona, and San Juan. The data were gathered as part of the research project “Characterization of Urban Wind Energy Potential for Energy Purposes in the Southern Region of the Dominican Republic”, Code: 2022-3C1-141, funded by the Fondo Nacional de Innovación y Desarrollo Científico y Tecnológico (FONDOCYT).
The raw dataset includes high-frequency measurements (10 and 30-minute intervals), while the hourly dataset presents cleansed and aggregated data. Both datasets span from early 2024 to mid-2025 and were recorded using Davis Instruments Vantage PRO2/6312 weather stations. Each station was geolocated and calibrated to ensure consistency and reliability across sites.
Key variables measured include:
- Temperature (°C)
- Relative Humidity (%)
- Wind Speed (m/s)
- Wind Direction (cardinal and degrees)
- Barometric Pressure (hPa)
- Rainfall (mm)
- Air Density (kg/m³) [hourly dataset only]
Each dataset is accompanied by detailed metadata sheets specifying station codes, geographic coordinates, elevation, measurement periods, data frequency, and record counts per variable. These metadata ensure transparency and facilitate reuse in climate analysis, renewable energy modeling, and environmental research.
The database was curated and structured for public access following open data standards. It is intended to support academic, governmental, and industrial stakeholders interested in meteorological trends and wind energy potential in urban and peri-urban areas of the Dominican Republic.
DOI: 10.17632/wj9gnpgcv8.1
title: Meteorological Dataset – Southern Dominican Republic

description: This dataset includes both raw meteorological measurements and cleaned, hourly aggregated data collected from four provinces in the southern Dominican Republic: San Cristóbal, Azua, Barahona, and San Juan. The data were gathered as part of the research project “Characterization of Urban Wind Energy Potential for Energy Purposes in the Southern Region of the Dominican Republic”, Code: 2022-3C1-141, funded by the Fondo Nacional de Innovación y Desarrollo Científico y Tecnológico (FONDOCYT).
The raw dataset includes high-frequency measurements (10 and 30-minute intervals), while the hourly dataset presents cleansed and aggregated data. Both datasets span from early 2024 to mid-2025 and were recorded using Davis Instruments Vantage PRO2/6312 weather stations. Each station was geolocated and calibrated to ensure consistency and reliability across sites.
Key variables measured include:
- Temperature (°C)
- Relative Humidity (%)
- Wind Speed (m/s)
- Wind Direction (cardinal and degrees)
- Barometric Pressure (hPa)
- Rainfall (mm)
- Air Density (kg/m³) [hourly dataset only]
Each dataset is accompanied by detailed metadata sheets specifying station codes, geographic coordinates, elevation, measurement periods, data frequency, and record counts per variable. These metadata ensure transparency and facilitate reuse in climate analysis, renewable energy modeling, and environmental research.
The database was curated and structured for public access following open data standards. It is intended to support academic, governmental, and industrial stakeholders interested in meteorological trends and wind energy potential in urban and peri-urban areas of the Dominican Republic.
DOI: 10.17632/wbyngt9hbn
title: Meta-analysis cases of nitrification inhibitors in agriculture

description: Document contains curated data from references included in the meta-analysis titled: "Nitrification inhibitors in agriculture: A global synthesis on their role in nitrogen accumulation and crop productivity"
DOI: 10.17632/wbyngt9hbn.1
title: Meta-analysis cases of nitrification inhibitors in agriculture

description: Document contains curated data from references included in the meta-analysis titled: "Nitrification inhibitors in agriculture: A global synthesis on their role in nitrogen accumulation and crop productivity"
DOI: 10.17632/g9vgmrgj5m
title: The effect of polysaccharides from Dictyophora indusiata volva on transcriptome of photoaging HFS cells

description: The effect of polysaccharides from Dictyophora indusiata volva on transcriptome of photoaging HFS cells.
DOI: 10.17632/g9vgmrgj5m.1
title: The effect of polysaccharides from Dictyophora indusiata volva on transcriptome of photoaging HFS cells

description: The effect of polysaccharides from Dictyophora indusiata volva on transcriptome of photoaging HFS cells.
DOI: 10.5517/ccdc.csd.cc27jhq0
title: CCDC 2071442: Experimental Crystal Structure Determination

description: Related Article: Pradip Chakraborty, Mouhamadou Sy, Houcem Fourati, Teresa Delgado, Mousumi Dutta, Chinmoy Das, Céline Besnard, Andreas Hauser, Cristian Enachescu, Kamel Boukheddaden|2022|Phys.Chem.Chem.Phys.(PCCP)|24|982|doi:10.1039/D1CP04321H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27ldlv
title: CCDC 2073267: Experimental Crystal Structure Determination

description: Related Article: Kunihisa Sugimoto, Takashi Okubo, Masahiko Maekawa, Takayoshi Kuroda-Sowa|2021|Cryst.Growth Des.|21|4178|doi:10.1021/acs.cgd.1c00426
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27m0rn
title: CCDC 2073861: Experimental Crystal Structure Determination

description: Related Article: Ruth D. Rittinghaus, Aylin Karabulut, Alexander Hoffmann, Sonja Herres‐Pawlis|2021|Angew.Chem.,Int.Ed.|60|21795|doi:10.1002/anie.202109053
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27nrxl
title: CCDC 2075540: Experimental Crystal Structure Determination

description: Related Article: K. Znovjyak, M. Seredyuk, S.O. Malinkin, I.A. Golenya, T.Y. Sliva, S. Shova, N.U. Mulloev|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|495|doi:10.1107/S2056989021003662
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27psvl
title: CCDC 2076530: Experimental Crystal Structure Determination

description: Related Article: Devika Pokhriyal, Spencer P. Heins, Renee J. Sifri, Dillon T. Gentekos, Rachael E. Coleman, Peter T. Wolczanski, Thomas R. Cundari, Brett P. Fors, Kyle M. Lancaster, Samantha N. MacMillan|2021|Inorg.Chem.|60|18662|doi:10.1021/acs.inorgchem.1c01815
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27pswm
title: CCDC 2076531: Experimental Crystal Structure Determination

description: Related Article: Devika Pokhriyal, Spencer P. Heins, Renee J. Sifri, Dillon T. Gentekos, Rachael E. Coleman, Peter T. Wolczanski, Thomas R. Cundari, Brett P. Fors, Kyle M. Lancaster, Samantha N. MacMillan|2021|Inorg.Chem.|60|18662|doi:10.1021/acs.inorgchem.1c01815
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27psxn
title: CCDC 2076532: Experimental Crystal Structure Determination

description: Related Article: Devika Pokhriyal, Spencer P. Heins, Renee J. Sifri, Dillon T. Gentekos, Rachael E. Coleman, Peter T. Wolczanski, Thomas R. Cundari, Brett P. Fors, Kyle M. Lancaster, Samantha N. MacMillan|2021|Inorg.Chem.|60|18662|doi:10.1021/acs.inorgchem.1c01815
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27psyp
title: CCDC 2076533: Experimental Crystal Structure Determination

description: Related Article: Devika Pokhriyal, Spencer P. Heins, Renee J. Sifri, Dillon T. Gentekos, Rachael E. Coleman, Peter T. Wolczanski, Thomas R. Cundari, Brett P. Fors, Kyle M. Lancaster, Samantha N. MacMillan|2021|Inorg.Chem.|60|18662|doi:10.1021/acs.inorgchem.1c01815
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27qrwm
title: CCDC 2077461: Experimental Crystal Structure Determination

description: Related Article: Bernhard M. Krause, Britta Bauer, Jörg-Martin Neudörfl, Thomas Wieder, Hans-Günther Schmalz|2021|RSC Med. Chem.|12|2053|doi:10.1039/D1MD00163A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27qrxn
title: CCDC 2077462: Experimental Crystal Structure Determination

description: Related Article: Bernhard M. Krause, Britta Bauer, Jörg-Martin Neudörfl, Thomas Wieder, Hans-Günther Schmalz|2021|RSC Med. Chem.|12|2053|doi:10.1039/D1MD00163A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/ty6pc7yndh
title: The Role of Social Capital and Digital Literacy on Entrepreneurial Performance

description: This study examines social capital and digital literacy on entrepreneurial performance, and entrepreneurial orientation plays a mediating role. Methode: To test the hypothesis, use SEM analysis. Data was collected by online survey to 400 SME owners in South Sulawesi. Results: The study showed that the performance of SMEs could be explained through social capital and digital literacy as well as the role of entrepreneurial orientation as mediation. Conclusions: The role of social capital in terms of internal and external relationships, social networks, and values and beliefs can improve the business performance of SMEs. In addition, the role of digital literacy in understanding information technology, communication, digital content creation, and social media use greatly influences SMEs' performance. The role of entrepreneurial orientation also indirectly mediates the relationship between social capital and digital literacy to improve the performance of SMEs
DOI: 10.17632/ty6pc7yndh.1
title: The Role of Social Capital and Digital Literacy on Entrepreneurial Performance

description: This study examines social capital and digital literacy on entrepreneurial performance, and entrepreneurial orientation plays a mediating role. Methode: To test the hypothesis, use SEM analysis. Data was collected by online survey to 400 SME owners in South Sulawesi. Results: The study showed that the performance of SMEs could be explained through social capital and digital literacy as well as the role of entrepreneurial orientation as mediation. Conclusions: The role of social capital in terms of internal and external relationships, social networks, and values and beliefs can improve the business performance of SMEs. In addition, the role of digital literacy in understanding information technology, communication, digital content creation, and social media use greatly influences SMEs' performance. The role of entrepreneurial orientation also indirectly mediates the relationship between social capital and digital literacy to improve the performance of SMEs
DOI: 10.5517/ccdc.csd.cc27s0tw
title: CCDC 2078668: Experimental Crystal Structure Determination

description: Related Article: Christopher B. Larsen, Jason D. Braun, Issiah B. Lozada, Kristjan Kunnus, Elisa Biasin, Charles Kolodziej, Clemens Burda, Amy A. Cordones, Kelly J. Gaffney, David E. Herbert|2021|J.Am.Chem.Soc.|143|20645|doi:10.1021/jacs.1c06429
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27sd7p
title: CCDC 2079022: Experimental Crystal Structure Determination

description: Related Article: Liwei Guo, Wenjuan Zhang, Furong Cao, Youshu Jiang, Randi Zhang, Yanping Ma, Gregory A. Solan, Yang Sun, Wen-Hua Sun|2021|Polym.Chem.|12|4214|doi:10.1039/D1PY00660F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27sd8q
title: CCDC 2079023: Experimental Crystal Structure Determination

description: Related Article: Liwei Guo, Wenjuan Zhang, Furong Cao, Youshu Jiang, Randi Zhang, Yanping Ma, Gregory A. Solan, Yang Sun, Wen-Hua Sun|2021|Polym.Chem.|12|4214|doi:10.1039/D1PY00660F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27t76j
title: CCDC 2079827: Experimental Crystal Structure Determination

description: Related Article: K. Znovjyak, M. Seredyuk, S.O. Malinkin, I.O. Golenya, V.M. Amirkhanov, S. Shova, N.U. Mulloev|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|573|doi:10.1107/S2056989021004412
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.