cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21299253
title: NWM GWR code and data

description: Dataset and code used in a journal paper entitled <em><strong>Geographically weighted regression based on a network weight matrix: a case study using urbanization driving force data in China</strong></em><em> </em>, published in the <strong>International Journal of Geographical Information Science</strong>. <br> <strong>Abstract: </strong>Geographically weighted regression (GWR) is a classical modeling method for dealing with spatial non-stationarity. It incorporates the distance decay effect in space to fit local regression models, where distance is defined as Euclidean distance. Although this definition has been expanded, it remains focused on physical distance. However, in the era of globalization and informatization, where the phenomenon of remotely close association is common, physical distance may not reflect real spatial proximity, and GWR based on physical distance has clear limitations. This paper proposes a geographically weighted regression based on a network weight matrix (NWM GWR) model. This does not rely on geographical location modeling; instead, it uses network distance to measure the proximity between two regions and weights observations by improving the kernel function to achieve distance attenuation. We adopt the population mobility network to establish a network weight matrix, modeling China’s urbanization and its multidimensional driving factors using network autocorrelation and NWM GWR methods. Results show that the NWM GWR model has more accurate fit and better stability than ordinary least squares and GWR models, and better reveals relationships between variables, which makes it suitable for modeling economic and social systems more broadly.
DOI: 10.6084/m9.figshare.21299253.v3
title: NWM GWR code and data.rar

description: Dataset and code used in a journal paper entitled <em><strong>Geographically weighted regression based on a network weight matrix: a case study using urbanization driving force data in China</strong></em><em> </em>, published in the <strong>International Journal of Geographical Information Science</strong>. <br> <strong>Abstract: </strong>Geographically weighted regression (GWR) is a classical modeling method for dealing with spatial non-stationarity. It incorporates the distance decay effect in space to fit local regression models, where distance is defined as Euclidean distance. Although this definition has been expanded, it remains focused on physical distance. However, in the era of globalization and informatization, where the phenomenon of remotely close association is common, physical distance may not reflect real spatial proximity, and GWR based on physical distance has clear limitations. This paper proposes a geographically weighted regression based on a network weight matrix (NWM GWR) model. This does not rely on geographical location modeling; instead, it uses network distance to measure the proximity between two regions and weights observations by improving the kernel function to achieve distance attenuation. We adopt the population mobility network to establish a network weight matrix, modeling China’s urbanization and its multidimensional driving factors using network autocorrelation and NWM GWR methods. Results show that the NWM GWR model has more accurate fit and better stability than ordinary least squares and GWR models, and better reveals relationships between variables, which makes it suitable for modeling economic and social systems more broadly.
DOI: 10.6084/m9.figshare.22256803
title: Subjects_PLV

description: share the data
DOI: 10.6084/m9.figshare.22256803.v1
title: Subjects_PLV

description: share the data
DOI: 10.5281/zenodo.7390790
title: BE-KONFORM data set (Group A) (Bedarfsermittlung im Rahmen der Erstellung des Konzepts für das Forschungsdatenmanagement an der Medizinischen Fakultät der Universität Freiburg

description: This dataset was collected within the BE-KONFORM study investigating employee's needs regarding research data management at the Medical Faculty of the University of Freiburg. The full dataset captures 236 complete cases. The study included a randomized module allocating subjects to one of two groups. Group A received the information that data will be published (n=113) and group B did not. Due to data protection law, only data of group A, where written informed consent was given (n=112) could be published here. This dataset had to be prepared for publication in order to avoid de-anonymisation of subjects. Therefore, variable [anzahl_m] was recoded into a categorical variable. Open text answers, where combination of variables might lead to an identification of the subjects were changed, where necessary. Changes are marked with brackets: [changed text]. Information on survey mode and sampling is provided in the data note.
description: Dataset and variable labels are provided in English. Text answers to open questions might be in German.
DOI: 10.5281/zenodo.7414827
title: BE-KONFORM data set (Group A) (Bedarfsermittlung im Rahmen der Erstellung des Konzepts für das Forschungsdatenmanagement an der Medizinischen Fakultät der Universität Freiburg

description: This dataset was collected within the BE-KONFORM study investigating employee's needs regarding research data management at the Medical Faculty of the University of Freiburg. The full dataset captures 236 complete cases. The study included a randomized module allocating subjects to one of two groups. Group A received the information that data will be published (n=113) and group B did not. Due to data protection law, only data of group A, where written informed consent was given (n=112) could be published here. This dataset had to be prepared for publication in order to avoid de-anonymisation of subjects. Therefore, variable [anzahl_m] was recoded into a categorical variable. Open text answers, where combination of variables might lead to an identification of the subjects were changed, where necessary. Changes are marked with brackets: [changed text]. Information on survey mode and sampling is provided in the data note.
description: Dataset and variable labels are provided in English. Text answers to open questions might be in German.
DOI: 10.5281/zenodo.7723111
title: BE-KONFORM data set (Group A) (Bedarfsermittlung im Rahmen der Erstellung des Konzepts für das Forschungsdatenmanagement an der Medizinischen Fakultät der Universität Freiburg

description: This dataset was collected within the BE-KONFORM study investigating employee's needs regarding research data management at the Medical Faculty of the University of Freiburg. The full dataset captures 236 complete cases. The study included a randomized module allocating subjects to one of two groups. Group A received the information that data will be published (n=113) and group B did not. Due to data protection law, only data of group A, where written informed consent was given (n=112) could be published here. This dataset had to be prepared for publication in order to avoid de-anonymisation of subjects. Therefore, variable [anzahl_m] was recoded into a categorical variable. Open text answers, where combination of variables might lead to an identification of the subjects were changed, where necessary. Changes are marked with brackets: [changed text]. Information on survey mode and sampling is provided in the data note.
description: Dataset and variable labels are provided in English. Text answers to open questions might be in German.
DOI: 10.6084/m9.figshare.22256929.v1
title: Search strategies-Characteristics, treatment, and outcomes of Folie a Deux. A systematic review of case report and case series (1).pdf

description: Search strategies and list of included studies (Folie a Deux)
DOI: 10.6084/m9.figshare.20473656
title: Simulation of urban perforation after random vacant plot generation: Application of the thinning point process

description: The codes and sample data for (1) Delineating morphological urban areas, (2) Specifying vacant plots, (3) Simulation for vacant plots generation and NND test, (4) Simulation for vacant plots accumulation_q[k,t]_variable, (5) NND test.
DOI: 10.6084/m9.figshare.20473656.v2
title: Simulation of urban perforation after random vacant plot generation: Application of the thinning point process

description: The codes and sample data for (1) Delineating morphological urban areas, (2) Specifying vacant plots, (3) Simulation for vacant plots generation and NND test, (4) Simulation for vacant plots accumulation_q[k,t]_variable, (5) NND test.
DOI: 10.6084/m9.figshare.20473656.v1
title: Simulation of urban perforation after random vacant plot generation: Application of the thinning point process

description: The codes and sample data for (1) Delineating morphological urban areas, (2) Specifying vacant plots, (3) Simulation for vacant plots generation and NND test, (4) Simulation for vacant plots accumulation_q[k,t]_variable, (5) NND test.
DOI: 10.6084/m9.figshare.20103140
title: Automatic measurement of building setbacks and streetscape widths and their spatial variability along streets and in plots: integration of streetscape skeletons and plot geometry

description: The codes and data for generating {AV(B<em>i</em>)}, {L(B<em>i</em>)}, {M(Q<em>i</em>,<em>k</em>)}, {RS(Q<em>i</em>,<em>k</em>)}, and {SB(Q<em>i</em>,<em>k</em>)} as well as computing <em>wi</em>,<em>k</em> and <em>ui</em>,<em>k</em> on QGIS.
DOI: 10.6084/m9.figshare.20103140.v2
title: Automatic measurement of building setbacks and streetscape widths and their spatial variability along streets and in plots: integration of streetscape skeletons and plot geometry

description: The codes and data for generating {AV(B<em>i</em>)}, {L(B<em>i</em>)}, {M(Q<em>i</em>,<em>k</em>)}, {RS(Q<em>i</em>,<em>k</em>)}, and {SB(Q<em>i</em>,<em>k</em>)} as well as computing <em>wi</em>,<em>k</em> and <em>ui</em>,<em>k</em> on QGIS.
DOI: 10.6084/m9.figshare.22257151
title: Katie Supplementary_24_10_2022.xlsx

description: Supplementary Tables
DOI: 10.6084/m9.figshare.22257151.v1
title: Katie Supplementary_24_10_2022.xlsx

description: Supplementary Tables
DOI: 10.6084/m9.figshare.21399960
title: Raw data from monitoring data and software simulations

description: The original displacement data provided by the Three Gorges Institute of Surveying and Mapping of the Yangtze River can reflect the deformation of the mountain.There are also simulated valley deformation data.
DOI: 10.6084/m9.figshare.21399960.v2
title: Raw data from monitoring data and software simulations

description: The original displacement data provided by the Three Gorges Institute of Surveying and Mapping of the Yangtze River can reflect the deformation of the mountain.There are also simulated valley deformation data.
DOI: 10.6084/m9.figshare.21399960.v1
title: Raw data from monitoring data and software simulations

description: The original displacement data provided by the Three Gorges Institute of Surveying and Mapping of the Yangtze River can reflect the deformation of the mountain.There are also simulated valley deformation data.
DOI: 10.5281/zenodo.7723203
title: Delete Project

description: Delete Project
DOI: 10.5281/zenodo.7723204
title: Delete Project

description: Delete Project
DOI: 10.34740/kaggle/dsv/5147682
title: Formula 1 Drivers Dataset
DOI: 10.7910/dvn/hkxo7q
title: HPACC dataset 2

description: :unav
DOI: 10.6084/m9.figshare.22257232
title: Statistical source data for Fig.2c and Fig.6f-6h

description: Source data for Fig.2c and Fig.6f-6h shared in excel sheet
DOI: 10.6084/m9.figshare.22257232.v1
title: Statistical source data for Fig.2c and Fig.6f-6h

description: Source data for Fig.2c and Fig.6f-6h shared in excel sheet
DOI: 10.5281/zenodo.7723224
title: Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"

description: There are 2 zip folders in this repository. "a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model". The zip file contains 3 main folders: base_setup_files a3d_jgr_alpha1 a3d_jgr_alpha3 The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file. a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects. To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here. _________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model". The zip file contains 2 main folders: SNOWPACK_JGR_ALPHA1 SNOWPACK_JGR_ALPHA3 In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file. To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.
DOI: 10.5281/zenodo.7723223
title: Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"

description: There are 2 zip folders in this repository. "a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model". The zip file contains 3 main folders: base_setup_files a3d_jgr_alpha1 a3d_jgr_alpha3 The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file. a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects. To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here. _________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model". The zip file contains 2 main folders: SNOWPACK_JGR_ALPHA1 SNOWPACK_JGR_ALPHA3 In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file. To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.
DOI: 10.23728/b2share.9814decadcfd4fb68a8963efbdc67d41
title: Supplementary Material for „Impact of Explainable AI On Cognitive Load: Insights From An Empirical Study“

description: Abstract: Supplementary material for the article: Herm, Lukas-Valentin: Impact of Explainable AI On Cognitive Load: Insights From An Empirical Study. In: 31st European Conference on Information Systems (ECIS). AIS Virtual Conference Series : AIS, 2023, bl conditionally accepted Abstract: "While the emerging research field of explainable artificial intelligence (XAI) claims to address the lack of explainability in high-performance machine learning models, in practice, XAI targets developers rather than actual end-users. Unsurprisingly, end-users are often unwilling to use XAI-based decision support systems. Similarly, there is limited interdisciplinary research on end-users’ behavior during XAI explanations usage, rendering it unknown how explanations may impact cognitive load and further affect end-user performance. Therefore, we conducted an empirical study with 271 prospective physicians, measuring their cognitive load, task performance, and task time for distinct implementation-independent XAI explanation types using a COVID-19 use case. We found that these explanation types strongly influence end-users’ cognitive load, task performance, and task time. Further, we contextualized a mental efficiency metric, ranking local XAI explanation types best, to provide recommendations for future applications and implications for sociotechnical XAI research." *** Using this data for academic publications is granted explicitly. ***
DOI: 10.34740/kaggle/dsv/5148076
title: Nigerian Presidential Election 2023 Tweets
DOI: 10.5281/zenodo.7723388
title: A method for creating a burn severity atlas: an example from Alberta, Canada

description: Relativized Burn Ratio (RBR) grids derived from Landsat imagery for large fires in Alberta and adjacent national parks (1985 - 2018).
DOI: 10.5281/zenodo.7723387
title: A method for creating a burn severity atlas: an example from Alberta, Canada

description: Relativized Burn Ratio (RBR) grids derived from Landsat imagery for large fires in Alberta and adjacent national parks (1985 - 2018).
DOI: 10.5281/zenodo.7723380
title: Data from "Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows"

description: This is data from the paper "Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows" published in PNAS, 2023 This is the raw imaging data from Mouse 1. The .raw file is the particle tracking video, and Geometry4.mat is a Matlab file containing the 3D z-stack and accompanying segmentation (pvs_smooth). Details regarding the subject and imaging can be found in the published paper. Additional codes and data used in the paper will be made available upon request.
DOI: 10.5281/zenodo.7723381
title: Data from "Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows"

description: This is data from the paper "Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows" published in PNAS, 2023 This is the raw imaging data from Mouse 1. The .raw file is the particle tracking video, and Geometry4.mat is a Matlab file containing the 3D z-stack and accompanying segmentation (pvs_smooth). Details regarding the subject and imaging can be found in the published paper. Additional codes and data used in the paper will be made available upon request.
DOI: 10.5281/zenodo.7723574
title: Um estudo sobre as práticas de testes funcionais de GUI na indústria brasileira de software

description: Teste de software é uma importante atividade no ciclo de desenvolvimento de software, a qual busca garantir a qualidade dos software criados. Quando um software não supre as necessidades implícitas e explicitas de seus usuários, pode-se dizer que ele carece de qualidade. A falta de qualidade de um software pode gerar vários problemas para o projeto de desenvolvimento de software, como atritos entre fornecedor e clientes/usuários, perdas financeiras, problemas legais por descumprimento de contratos e até risco à vida dos seus usuários em contextos críticos. Nesse sentido, distintos níveis de testes foram desenvolvidos para garantir a qualidade em diferentes aspectos do software. Uma abordagem popular é o teste funcional de interface, que avalia o software através da execução de eventos na tela do software, como o preenchimento de um campo de texto ou o clique de um botão. Os testes funcionais de interface ou GUI (\textit{Graphical User Interface}), podem ser executados tanto de forma manual, com a interação direta do testador no software, quanto automatizada, com a execução do software através de um \textit{script} programado. Nos últimos anos, é possível encontrar na literatura diversas soluções e técnicas propostas para lidar com os testes de GUI. No entanto, as pesquisas não se aprofundam em analisar como os profissionais da indústria vem executando esse tipo de teste, deixando assim uma lacuna na área. Por isso, este estudo propõe investigar como os testadores estão realizando testes de interface em seus projetos. Assim, primeiramente foi realizado um questionário online com 222 profissionais de teste com distintas experiências, cargos e funções. O resultado indicou que ainda há muitos profissionais que realizam testes exclusivamente de forma manual. Além disso, a maioria dos profissionais concordam que as ferramentas utilizadas na automação de testes atendem as necessidades de negócio, embora tenham limitações técnicas. Em seguida, realizamos uma entrevista semiestruturada com 20 profissionais de testes para complementar os resultados obtidos com o questionário. Os resultados indicaram que testadores que realizam apenas testes de GUI manuais não criam testes de GUI automatizados devido ao contexto do projeto em que trabalham, suas preferências profissionais e também por questões educacionais, como dificuldade com programação. Além disso, foram observadas distintas limitações para testes de GUI manuais e automatizados que impactam negativamente as atividades desempenhadas pelos profissionais, como dificuldades relacionadas à cultura da empresa e problemas técnicos relacionados ao software sob teste. Os resultados combinados fornecem uma visão geral dos testes de GUI na indústria de software brasileira.