cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5679578
title: Can my Package Survive? Aligning contributions anddependencies for Ecosystem Congruence

description: Abstract: Adoption of third-party libraries for contemporary software development has led to the creation of large inter-dependency networks, where sustainability issues of a single library can have widespread network effects. Maintainers of these libraries are often overworked, relying on the contributions of volunteers to keep libraries alive. Where do these contributions come from?<br> In this work, we hypothesize that the developers who rely on a library because their code depends on it might also be the ones who contribute to the library to ensure its sustainability, i.e., their contributions and dependencies are aligned in an ecosystem. We leverage socio-technical techniques to measure the degree to which contributions align with dependency changes at the ecosystem level. We conduct a large-scale empirical study to compute our congruence metrics using 1.7 million issues, 970 thousand pull requests (PR), and over 5.3 million commits belonging to 107,242 npm packages. We find that congruence reflects global ecosystem events and has an impact on package survival. By providing quantitative evidence, our work lays the groundwork for future research on how the collective ecosystem can ensure the survival of its packages. Data Description: The source code and datasets for (a) ecosystem-level analysis (\Ecosystem_congruence_analysis(RQ1)), (b) package survival analysis (\Survival_analysis(RQ2)), and (c) file path and source code similarities of developer contributions analysis (Filepath_code_similarity_analysis(RQ3)). For each folder, it includes a text file to describe the file details.
DOI: 10.5281/zenodo.5680642
title: Giving Back: Contributions Congruent to Library Dependency Changes in a Software Ecosystem

description: Abstract: Widespread adoption of third-party libraries for contemporary software development has led to the creation of large inter-dependency networks, where sustainability issues of a single library can have widespread network effects. Maintainers of these libraries are often overworked, relying on the contributions of volunteers to sustain these libraries. To understand these contributions, in this work, we leverage socio-technical techniques to introduce and formalise dependency-contribution congruence (DC congruence) at both ecosystem and library level, i.e., to understand the degree and origins of contributions congruent to dependency changes, analyze whether they contribute to library dormancy (i.e., a lack of activity), and investigate similarities between these congruent contributions compared to typical contributions. We conduct a large-scale empirical study to measure the DC congruence for the NPM ecosystem using 1.7 million issues, 970 thousand pull requests (PR), and over 5.3 million commits belonging to 107,242 NPM libraries. We find that the most congruent contributions originate from contributors who can only submit (not commit) to both a client and library.<br> At the project level, we find that DC congruence shares an inverse relationship with the likelihood that a library becomes dormant, i.e., the lower the DC congruence, the more likely the project becomes dormant. Finally, by comparing source code of contributions, we find statistical differences in file path and added lines in source code of congruent contributions when compared to typical contributions. Our work has implications to encourage and sustain dependency change congruent contributions, especially to support library maintainers in sustaining their projects. Data Description: Datasets and source code related to (a) the ecosystem-level and package-level DC congruence results, (b) the metric data for our survival model analysis, and (c) the file path and source code similarities between contribution types. For each folder, it includes a text file to describe file details.
DOI: 10.5281/zenodo.7388963
title: Giving Back: Contributions Congruent to Library Dependency Changes in a Software Ecosystem

description: Abstract: Widespread adoption of third-party libraries for contemporary software development has led to the creation of large inter-dependency networks, where sustainability issues of a single library can have widespread network effects. Maintainers of these libraries are often overworked, relying on the contributions of volunteers to sustain these libraries. To understand these contributions, in this work, we leverage socio-technical techniques to introduce and formalise dependency-contribution congruence (DC congruence) at both ecosystem and library level, i.e., to understand the degree and origins of contributions congruent to dependency changes, analyze whether they contribute to library dormancy (i.e., a lack of activity), and investigate similarities between these congruent contributions compared to typical contributions. We conduct a large-scale empirical study to measure the DC congruence for the NPM ecosystem using 1.7 million issues, 970 thousand pull requests (PR), and over 5.3 million commits belonging to 107,242 NPM libraries. We find that the most congruent contributions originate from contributors who can only submit (not commit) to both a client and library.<br> At the project level, we find that DC congruence shares an inverse relationship with the likelihood that a library becomes dormant, i.e., the lower the DC congruence, the more likely the project becomes dormant. Finally, by comparing source code of contributions, we find statistical differences in file path and added lines in source code of congruent contributions when compared to typical contributions. Our work has implications to encourage and sustain dependency change congruent contributions, especially to support library maintainers in sustaining their projects. Data Description: Datasets and source code related to (a) the ecosystem-level and package-level DC congruence results, (b) the metric data for our survival model analysis, and (c) the file path and source code similarities between contribution types. For each folder, it includes a text file to describe file details.
DOI: 10.5281/zenodo.5677371
title: Giving Back: Contributions Congruent to Library Dependency Changes in a Software Ecosystem

description: Abstract: Widespread adoption of third-party libraries for contemporary software development has led to the creation of large inter-dependency networks, where sustainability issues of a single library can have widespread network effects. Maintainers of these libraries are often overworked, relying on the contributions of volunteers to sustain these libraries. To understand these contributions, in this work, we leverage socio-technical techniques to introduce and formalise dependency-contribution congruence (DC congruence) at both ecosystem and library level, i.e., to understand the degree and origins of contributions congruent to dependency changes, analyze whether they contribute to library dormancy (i.e., a lack of activity), and investigate similarities between these congruent contributions compared to typical contributions. We conduct a large-scale empirical study to measure the DC congruence for the NPM ecosystem using 1.7 million issues, 970 thousand pull requests (PR), and over 5.3 million commits belonging to 107,242 NPM libraries. We find that the most congruent contributions originate from contributors who can only submit (not commit) to both a client and library.<br> At the project level, we find that DC congruence shares an inverse relationship with the likelihood that a library becomes dormant, i.e., the lower the DC congruence, the more likely the project becomes dormant. Finally, by comparing source code of contributions, we find statistical differences in file path and added lines in source code of congruent contributions when compared to typical contributions. Our work has implications to encourage and sustain dependency change congruent contributions, especially to support library maintainers in sustaining their projects. Data Description: Datasets and source code related to (a) the ecosystem-level and package-level DC congruence results, (b) the metric data for our survival model analysis, and (c) the file path and source code similarities between contribution types. For each folder, it includes a text file to describe file details.
DOI: 10.5281/zenodo.7377039
title: Dental mesowear and microwear raw data for Cervus elaphus, Rupicapra pyrenaica and Sus scrofa from Balma del Gai; and the ANOVA - test for equal means

description: Quantitative data for the dental microwear and mesowear analyses on <em>Cervus elaphus</em>, <em>Rupicapra pyrenaica </em>and <em>Sus scrofa</em> from the Epipalaeolithic sequence of Balma del Gai (Moià, Spain). And the ANOVA - Test for equal means.
DOI: 10.5281/zenodo.7377038
title: Dental mesowear and microwear raw data for Cervus elaphus, Rupicapra pyrenaica and Sus scrofa from Balma del Gai; and the ANOVA - test for equal means

description: Quantitative data for the dental microwear and mesowear analyses on <em>Cervus elaphus</em>, <em>Rupicapra pyrenaica </em>and <em>Sus scrofa</em> from the Epipalaeolithic sequence of Balma del Gai (Moià, Spain). And the ANOVA - Test for equal means.
DOI: 10.6084/m9.figshare.21665639
title: Rph-assembly_predicted-proteins.fasta

description: The proteome sequence.
DOI: 10.5281/zenodo.7388521
title: doi.org/10.1016/j.rse.2020.112006

description: In this work, the development of an artificial Neural Network for AEROsol retrieval (NNAero) is presented. NNAero uses data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) flying on the NASA Terra and Aqua satellites. The MODIS-derived spectral reflectances of solar radiation at the top of the atmosphere (TOA) and at the surface were used together with ground-based Aerosol Robotic Network (AERONET) measurements of Aerosol Optical Depth (AOD) and FMF to train a Convolutional Neural Network (CNN) for the joint retrieval of FMF and AOD. The NNAero results over northern and eastern China were validated against an independent reference AERONET dataset (i.e. not used in training the CNN). The results show that 68% of the NNAero AOD values are within the MODIS expected error (EE) envelope over land of ± (0.05 + 15%), which is similar to the results from the MODIS Deep Blue (DB) algorithm (63% within EE), and both are better than the Dark Target (DT) algorithm (31% within EE). The validation of the NNAero FMF vs AERONET data shows a significant improvement with respect to the DT FMF, with Root Mean Squared Prediction Errors (RMSE) of 0.1567 (NNAero) and 0.34 (DT). The NNAero method shows the potential of improved retrieval of the FMF.
description: Chen, X., de Leeuw, G., Arola, A., Liu, S., Liu, Y., Li, Z., & Zhang, K. (2020). Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method. Remote Sensing of Environment, 249, 112006.
DOI: 10.5281/zenodo.7388522
title: doi.org/10.1016/j.rse.2020.112006

description: In this work, the development of an artificial Neural Network for AEROsol retrieval (NNAero) is presented. NNAero uses data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) flying on the NASA Terra and Aqua satellites. The MODIS-derived spectral reflectances of solar radiation at the top of the atmosphere (TOA) and at the surface were used together with ground-based Aerosol Robotic Network (AERONET) measurements of Aerosol Optical Depth (AOD) and FMF to train a Convolutional Neural Network (CNN) for the joint retrieval of FMF and AOD. The NNAero results over northern and eastern China were validated against an independent reference AERONET dataset (i.e. not used in training the CNN). The results show that 68% of the NNAero AOD values are within the MODIS expected error (EE) envelope over land of ± (0.05 + 15%), which is similar to the results from the MODIS Deep Blue (DB) algorithm (63% within EE), and both are better than the Dark Target (DT) algorithm (31% within EE). The validation of the NNAero FMF vs AERONET data shows a significant improvement with respect to the DT FMF, with Root Mean Squared Prediction Errors (RMSE) of 0.1567 (NNAero) and 0.34 (DT). The NNAero method shows the potential of improved retrieval of the FMF.
description: Chen, X., de Leeuw, G., Arola, A., Liu, S., Liu, Y., Li, Z., & Zhang, K. (2020). Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method. Remote Sensing of Environment, 249, 112006.
DOI: 10.5281/zenodo.7062281
title: pastclim_Beyer2020

description: Dataset for the R package pastclim, covering the data from Beyer et al. 2020. This is a version of the dataset with ice sheets and internal seas removed. website of the package: https://evolecolgroup.github.io/pastclim/index.html
description: Leverhulme Research Grant RPG-2020-317
description: {"references": ["Beyer, R.M., Krapp, M. & Manica, A. High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years. Sci Data 7, 236 (2020). ", "M. Leonardi, E. Y. Hallett, R. Beyer, M. Krapp, A. Manica (2022) pastclim: an R package to easily access and use paleoclimatic reconstructions, bioRxiv 2022.05.18.492456"]}
DOI: 10.5281/zenodo.7062280
title: pastclim_Beyer2020

description: Dataset for the R package pastclim, covering the data from Beyer et al. 2020. This is a version of the dataset with ice sheets and internal seas removed. website of the package: https://evolecolgroup.github.io/pastclim/index.html
description: Leverhulme Research Grant RPG-2020-317
description: {"references": ["Beyer, R.M., Krapp, M. & Manica, A. High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years. Sci Data 7, 236 (2020). ", "M. Leonardi, E. Y. Hallett, R. Beyer, M. Krapp, A. Manica (2022) pastclim: an R package to easily access and use paleoclimatic reconstructions, bioRxiv 2022.05.18.492456"]}
DOI: 10.5281/zenodo.7388091
title: pastclim_Beyer2020

description: Dataset for the R package pastclim, covering the data from Beyer et al. 2020. This is a version of the dataset with ice sheets and internal seas removed. website of the package: https://evolecolgroup.github.io/pastclim/index.html
description: Leverhulme Research Grant RPG-2020-317
description: {"references": ["Beyer, R.M., Krapp, M. & Manica, A. High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years. Sci Data 7, 236 (2020). ", "M. Leonardi, E. Y. Hallett, R. Beyer, M. Krapp, A. Manica (2022) pastclim: an R package to easily access and use paleoclimatic reconstructions, bioRxiv 2022.05.18.492456"]}
DOI: 10.25442/hku.21482400
title: Supporting data for "The role and mechanisms of interleukin-9 in facilitating anticancer effect of immune checkpoint blockade in lung cancer animal model".

description: This dataset includes the data used in thesis entitled "The role and mechanisms of interleukin-9 in facilitating anticancer effect of immune checkpoint blockade in lung cancer animal model".
DOI: 10.25442/hku.21482400.v1
title: Supporting data for "The role and mechanisms of interleukin-9 in facilitating anticancer effect of immune checkpoint blockade in lung cancer animal model".

description: This dataset includes the data used in thesis entitled "The role and mechanisms of interleukin-9 in facilitating anticancer effect of immune checkpoint blockade in lung cancer animal model". <br> Laboratory research data includes: 1) Western blot: Images will be scanned and saved as .jpg or .tif files. Measurements and quantification of images can be recorded into excel spreadsheets. 2) MTT: MTT datasets are exported as .xlsx excel files, showing the OD values at 570nm and the test date. 3) Tumor volume will be recorded every other day as three diameters in a .xlsl file, documenting the treatment time and groups. 4) Flow cytometry: Raw flow cytometry files saved from Cotyflex will be in the format of .fcs. Fowjo is used to analyze the .fcs files. Final datasets will be saved as a .wsp file and images will be around 500MB. 5) RT-qPCR: Real-time exponential graphs and data generated by the StepOne program will be exported as .csv excel files and .eds raw data files. Files generated throughout the entire course will be approximately 1-1.5 GB. 6) Statistical analysis software includes GraphPad Prism and ImageJ, files will be saved in their respective intrinsic/proprietary formats.
DOI: 10.25442/hku.21540042.v1
title: Supporting data for “A study of summertime wind and air temperature in Kowloon”

description: The study investigated the summertime wind and temperature behaviours in Kowloon by analysing the meteorological data and conducting numerical simulations. And the dataset contains daily weather data at the weather stations such as HKO, KP and SKG from the Hong Kong Observatory for the past 20 years. The simulated results of the two-dimensional WRF simulation for the Kowloon area are also included.
DOI: 10.25442/hku.21540042
title: Supporting data for “A study of summertime wind and air temperature in Kowloon”

description: The study investigated the summertime wind and temperature behaviours in Kowloon by analysing the meteorological data and conducting numerical simulations. And the dataset contains daily weather data at the weather stations such as HKO, KP and SKG from the Hong Kong Observatory for the past 20 years. The simulated results of the two-dimensional WRF simulation for the Kowloon area are also included.
DOI: 10.5519/qd.f4wg4wkp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (29667 records)
DOI: 10.5281/zenodo.7389339
title: Data related to Hong Kong compulsory testing premises

description: Data related to Hong Kong compulsory testing premises for the article "Intra-City Transmission of SARS-CoV-2 Omicron Variant: Decentralization and Activity Segregation".
DOI: 10.5281/zenodo.7389338
title: Data related to Hong Kong compulsory testing premises

description: Data related to Hong Kong compulsory testing premises for the article "Intra-City Transmission of SARS-CoV-2 Omicron Variant: Decentralization and Activity Segregation".
DOI: 10.17608/k6.auckland.21524121.v1
title: VoyRisk_input-data.zip

description: This dataset includes six files. Two of them (EcoProvinceCentroids_WGS84.* and WorldOceans_Canals_fixed.*) are datasets produced by external organizations and available online in public portals (https://data.unep-wcmc.org/datasets/38 and https://maps.princeton.edu/catalog/stanford-ds959rc3867, respectively). These two shapefiles are the input files used in the R script VoyRisk_trade_connections_script.R, which uses functions located in VoyRisk_localFunctions.R, to create simulated maritime paths between each pair of the 62 Marine Ecoregions Of the World (MEOW) ecoprovinces (Spalding et al. 2007). The other four files (ecoprovDistanceTable.csv, tr1_.RData, tr1C.RData, WorldOceans_fixed_raster_005res.tif) are intermediate files produced by the R script. <br> <br> <strong>References:</strong> Schattschneider, Jessica; Floerl, Lisa; Casanovas, Paula (2022): VoyRisk distance analysis code. The University of Auckland. Software. DOI: https://doi.org/10.17608/k6.auckland.21368874 <br> Spalding, M.D., Fox, H.E., Allen, G.R., Davidson, N., Ferdaña, Z.A., Finlayson, M., Halpern, B.S., Jorge, M.A., Lombana, A., Lourie, S.A., Martin, K.D., McManus, E., Molnar, J., Recchia, C.A., and Robertson, J. (2007). Marine Ecoregions of the World: a bioregionalization of coast and shelf areas. BioScience 57: 573-583. DOI: http://dx.doi.org/10.1641/B570707 <br>
DOI: 10.17608/k6.auckland.21524121
title: VoyRisk_input-data.zip
DOI: 10.5281/zenodo.5514951
title: ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for dryland wheat and maize across China during 1993–2018

description: ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type <sub>soil-depth</sub>). This dataset is maize<sub>10-20.</sub> *** The data file is in “.tif" format *** Spatial extent: maize drylands across China *** Soil depth: 10-20cm *** Temporal Resolution: Daily *** Pixel size: 1000 m *** Projection information: GCS_WGS_1984 *** Filename convention: Year_DOY.tif *** Year: Values from 1993 to 2018
DOI: 10.5281/zenodo.5515359
title: ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for dryland wheat and maize across China during 1993–2018

description: ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type <sub>soil-depth</sub>). This dataset is maize<sub>0-10.</sub> *** The data file is in “.tif" format *** Spatial extent: maize drylands across China *** Soil depth: 0-10cm *** Temporal Resolution: Daily *** Pixel size: 1000 m *** Projection information: GCS_WGS_1984 *** Filename convention: Year_DOY.tif *** Year: Values from 1993 to 2018
DOI: 10.25442/hku.21586602
title: Supporting data for “Application of Statistical Learning Methods to Predict Psychopathological Symptoms and Well-being among Young People”

description: The current data is originally collected from the naturalistic study of the LevelMind@JC project. The LevelMind@JC is a four-year youth mental wellness hub project funded by the Hong Kong Jockey Club Charities Trust. The primary purpose of the naturalistic study is to monitor the different aspects of youths' mental health, including psychological distress, functioning, and quality of life. All hub users are expected to complete a brief assessment every time they visit a mental wellness hub. Each assessment takes approximately 2 minutes and includes questions regarding their distress level, functioning, and quality of life. Participants have the option to report their demographics. The current dataset is a subset of the original data from the naturalistic study, which included only variables that were used in the thesis project "Application of Statistical Learning Methods to Predict Psychopathological Symptoms and Well-being among Young People". This subset of variables was used to develop a classification and regression tree model that predicts youths' mental health outcomes at follow-up based on their baseline indicators. <br>
DOI: 10.25442/hku.21586602.v1
title: Supporting data for “Application of Statistical Learning Methods to Predict Psychopathological Symptoms and Well-being in Young People”

description: The current data is originally collected from the naturalistic study of the LevelMind@JC project. The LevelMind@JC is a four-year youth mental wellness hub project funded by the Hong Kong Jockey Club Charities Trust. The primary purpose of the naturalistic study is to monitor the different aspects of youths' mental health, including psychological distress, functioning, and quality of life. All hub users are expected to complete a brief assessment every time they visit a mental wellness hub. Each assessment takes approximately 2 minutes and includes questions regarding their distress level, functioning, and quality of life. Participants have the option to report their demographics. The current dataset is a subset of the original data from the naturalistic study, which included only variables that were used in the thesis project "Application of Statistical Learning Methods to Predict Psychopathological Symptoms and Well-being among Young People". This subset of variables was used to develop a classification and regression tree model that predicts youths' mental health outcomes at follow-up based on their baseline indicators. <br>