cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7729565
title: Replication data in the form of simulation outputs for "The joint evolution of animal movement and competition strategies"

description: This version of the data is a re-upload of an identical dataset uploaded to DataverseNL. The data are being uploaded here for uniformity of archiving with the other supplementary materials accompanying the manuscript, "The joint evolution of animal movement and competition strategies", which is accepted for publication in <em>The American Naturalist</em>. The dataset consists of a single zipped folder, data/, which contains subfolders with the simulation specific data. These subfolders are named 'sim_SCENARIO_rep_NNN_gro_RMAX', where 'SCENARIO' refers to the three scenarios of the model described in our manuscript, 'NNN' is the replicate number, and 'RMAX' is the maximum cell productivity in that simulation. Each of the subfolders consists of the following: The directory ‘depends/’, which holds the ‘extract.exe’ program. This program is used to extract specific data from the stored simulation output. The ‘sourceMe.R’ file allows the ‘extract.exe’ program to be linked to R, delivering the required data as a list object that can be handled in R. The stored simulation data on agents, as a series of ‘.arc’ and ‘.bin’ files. The ecological snapshots of the landscape, with prey items, foragers, kleptoparasites, and handlers (depending on the scenario), which are stored as PNG files named ‘00NNN.png’, where NNN is a three digit representation of the generation number (e.g. 001 for generation 1). Cumulative sums of the numbers of prey items, foragers, kleptoparasites, the intake from foraging (searching for prey), and the intake from the kleptoparasitic strategy (searching for handlers), on each cell of the landscape, for each of the last 9 generations of the simulation, as ‘NNN.txt’, where LAYER may be ‘items’ (prey items), ‘foragers’ (foragers), ‘klepts’ (kleptoparasites), ‘foragers_intake’ (the intake from the foraging strategy), or ‘klepts_intake’ (the intake due the kleptoparasite strategy. NB: These files are not used in our analyses, and may be ignored. 'SCENARIO' may be one of “foragers” (scenario 1), “obligate” (scenario 2), or “facultative” (scenario 3). 'NNN' may be one of “001”, “002”, or “003”. 'RMAX' may be one of “0.001”, “0.005”, “0.01”, “0.02”, “0.03”, “0.04”, or “0.05”. The manuscript presents results for 'RMAX' = 0.01.
description: PRG was supported by an Adaptive Life Programme grant made possible by the Groningen Institute for Evolutionary Life Sciences (GELIFES). PRG, CFGN and FJW acknowledge funding from the European Research Council (ERC Advanced Grant No. 789240 to FJW).
DOI: 10.5517/cc8lln2
title: CCDC 256204: Experimental Crystal Structure Determination

description: Related Article: Su-rong Jin, Lian-meng Zhang, Shi-zhong Liu, Xiang-gao Meng|2008|Fenzi Kexue Xuebao|24|196|
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: 10.18112/openneuro.ds003340.v1.0.4
title: Tasting Pictures: Viewing Images of Foods Evokes Taste-Quality-Specific Activity in Gustatory Insular Cortex
DOI: 10.6084/m9.figshare.22250104
title: Data_Speed-specific optimal contractile conditions of the human soleus muscle from slow to maximum running speed

description: Data set for the manuscript: Speed-specific optimal contractile conditions of the human soleus muscle from slow to maximum running speed
DOI: 10.6084/m9.figshare.22250104.v1
title: Data_Speed-specific optimal contractile conditions of the human soleus muscle from slow to maximum running speed

description: Data set for the manuscript: Speed-specific optimal contractile conditions of the human soleus muscle from slow to maximum running speed
DOI: 10.6084/m9.figshare.22264363
title: Felidae TNFRSF4 Fasta

description: Data Set 1, Fasta file containing genetic sequences used for analysis throughout our project. Coding sequences were obtained through BLAST searches on Ensembl, and alignment of sequences was performed using AliView. Included are 8 sequences comprised of four TNFRSF4 genes from domestic cat, lion, leopard, and canadian lynx, as well as the human TNFRSF4 and domestic cat, lion, and leopard TNFRSF18 genes as outgroups. The resulting Fasta file was used for multiple modes of analyis for our project.
DOI: 10.6084/m9.figshare.22265824
title: Felis casus CD134 pdb

description: Data Set 2, pdb file of Felis catus CD134 receptor. This file was obtained through AlphaFold, and was later used in Chimera for structural analysis of Felis catus' CD134 versus the CD134 receptor of lions, leopard, and lynx.
DOI: 10.6084/m9.figshare.22264000
title: MSR 2023 Dataset

description: This is the artifact that accompanies the paper titled: "Wasmizer: Curating WebAssembly-driven Projects on GitHub". <br> It contains: - the scripts we used to produce a dataset of WebAssembly binaries - a dataset of WebAssembly binaries (8915 .wasm files and 1384 .wat files) - the WASMIZER tool that can be used to automatically collect a new dataset, with refined information # Scripts <br> The `Scripts/` folder contains the scripts that we used to collect our dataset in December 2022. Since then, we have refined our scripts and made a tool, WASMIZER, described at the end of this README and accessible in the `WASMIZER/` folder. <br> In the `Scripts/` folder, there are three subfolders : <br> - `Collector/` that contains the scripts to collect GitHub projects that may be targeting WebAssembly as a compilation target - `Compiler/` that tries to compile a project and extracts the `.wasm` and `.wat` found after compilation. - `SmellsChecker/` that contains the checkers for the smells used in the case study of the paper. Each of these directories contain a README providing more details. <br> # Dataset The `Dataset/` folder contains our dataset of WebAssembly binaries collected in December 2022. It is structured as follows: <br> - The `wasm/` folder contains WebAssembly binaries in their binary format (`.wasm`) - The `wat/` folder contains WebAssembly binaries in their textual format. The basename of the files are their SHA checksum. Each binary is accompanied by a `.meta` file of the same name containing the project and the path within the project in which it was found. For example, for the binary `00047ad76615715bb2b36fa2102135b8dc32ac3c17f3488451168f808e2039f0.wasm`, there is a `00047ad76615715bb2b36fa2102135b8dc32ac3c17f3488451168f808e2039f0.meta` file containing: <br> ``` ./JuiceFV-Emscripten_OpenGL/application/dependencies/lib_sources/GLM/test/gtx/test-gtx_easing.wasm ``` <br> This indicates that we have found a WebAssembly binary in the `JuiceFV/Emscripten_OpenGL` GitHub project, at location `application/dependencies/lib_sources/GLM/test/gtx/test-gtx_easing.wasm`. When collecting this dataset, a number of metadata have not been collected and are thus missing from this initial snapshot. However, we have bundled a tool described below, which scrapes repositories and extract .wasm files after compilation, which collects more metadata. <br> # WASMIZER The WASMIZER tool is provided in the `WASMIZER/` directory. This is a refined version of our scripts used to collect the dataset. More details can be found in `WASMIZER/README.md`. To obtain the latest version, one can run `git pull` from the `WASMIZER/` directory, or access the repository online at [https://github.com/arash-mazidi/WASMIZER](https://github.com/arash-mazidi/WASMIZER). <br> The tool is deployed and regularly pushes newly found projects, .wasm and .wat files to the following shared folder : https://tucloud.tu-clausthal.de/index.php/s/MMRQMEZm66GRGXI (password: wasmizer).
DOI: 10.6084/m9.figshare.22264000.v1
title: MSR 2023 Dataset

description: This is the artifact that accompanies the paper titled: "Wasmizer: Curating WebAssembly-driven Projects on GitHub". <br> It contains: - the scripts we used to produce a dataset of WebAssembly binaries - a dataset of WebAssembly binaries (8915 .wasm files and 1384 .wat files) - the WASMIZER tool that can be used to automatically collect a new dataset, with refined information # Scripts <br> The `Scripts/` folder contains the scripts that we used to collect our dataset in December 2022. Since then, we have refined our scripts and made a tool, WASMIZER, described at the end of this README and accessible in the `WASMIZER/` folder. <br> In the `Scripts/` folder, there are three subfolders : <br> - `Collector/` that contains the scripts to collect GitHub projects that may be targeting WebAssembly as a compilation target - `Compiler/` that tries to compile a project and extracts the `.wasm` and `.wat` found after compilation. - `SmellsChecker/` that contains the checkers for the smells used in the case study of the paper. Each of these directories contain a README providing more details. <br> # Dataset The `Dataset/` folder contains our dataset of WebAssembly binaries collected in December 2022. It is structured as follows: <br> - The `wasm/` folder contains WebAssembly binaries in their binary format (`.wasm`) - The `wat/` folder contains WebAssembly binaries in their textual format. The basename of the files are their SHA checksum. Each binary is accompanied by a `.meta` file of the same name containing the project and the path within the project in which it was found. For example, for the binary `00047ad76615715bb2b36fa2102135b8dc32ac3c17f3488451168f808e2039f0.wasm`, there is a `00047ad76615715bb2b36fa2102135b8dc32ac3c17f3488451168f808e2039f0.meta` file containing: <br> ``` ./JuiceFV-Emscripten_OpenGL/application/dependencies/lib_sources/GLM/test/gtx/test-gtx_easing.wasm ``` <br> This indicates that we have found a WebAssembly binary in the `JuiceFV/Emscripten_OpenGL` GitHub project, at location `application/dependencies/lib_sources/GLM/test/gtx/test-gtx_easing.wasm`. When collecting this dataset, a number of metadata have not been collected and are thus missing from this initial snapshot. However, we have bundled a tool described below, which scrapes repositories and extract .wasm files after compilation, which collects more metadata. <br> # WASMIZER The WASMIZER tool is provided in the `WASMIZER/` directory. This is a refined version of our scripts used to collect the dataset. More details can be found in `WASMIZER/README.md`. To obtain the latest version, one can run `git pull` from the `WASMIZER/` directory, or access the repository online at [https://github.com/arash-mazidi/WASMIZER](https://github.com/arash-mazidi/WASMIZER). <br> The tool is deployed and regularly pushes newly found projects, .wasm and .wat files to the following shared folder : https://tucloud.tu-clausthal.de/index.php/s/MMRQMEZm66GRGXI (password: wasmizer).
DOI: 10.5281/zenodo.7729278
title: Datasets for 176Lu+ clock comparison at the 10E-18 level via correlation spectroscopy

description: Datasets and figure data for "176Lu+ clock comparison at the 10E-18 level via correlation spectroscopy"
DOI: 10.5281/zenodo.7729277
title: Datasets for 176Lu+ clock comparison at the 10E-18 level via correlation spectroscopy

description: Datasets and figure data for "176Lu+ clock comparison at the 10E-18 level via correlation spectroscopy"
DOI: 10.6084/m9.figshare.22265845.v1
title: Annual maps of global urban extents between 1992 to 2020 from SRU-Net NPP-VIIRS-like nighttime light data

description: The dataset contains a consistent annual global urban extents between 1992 to 2020 from SRU-Net NPP-VIIRS-like nighttime light data. The pixel value is 0 or 1. 0 means non-urban, and 1 means urban. Spatial Resolution: 500m Spatial Refrence: North_Pole_Lambert_Azimuthal_Equal_Area Extent: spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Each zip file contains an urban extent image in geotiff format. The filename of each image is "UByyyy", where "yyyy" is the year of the image.
DOI: 10.6084/m9.figshare.22265845.v2
title: Annual maps of global urban extents between 1992 to 2020 from SRU-Net NPP-VIIRS-like nighttime light data

description: The dataset contains a consistent annual global urban extents between 1992 to 2020 from SRU-Net NPP-VIIRS-like nighttime light data. The pixel value is 0 or 1. 0 means non-urban, and 1 means urban. Spatial Resolution: 500m Spatial Refrence: North_Pole_Lambert_Azimuthal_Equal_Area Extent: spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Each zip file contains an urban extent image in geotiff format. The filename of each image is "UByyyy", where "yyyy" is the year of the image.
DOI: 10.25345/c5k06xb0q
title: MassIVE MSV000091478 - Metabolic landscape of the male mouse gut identifies different niches determined by microbial activities
title: Metabolic landscape of the male mouse gut identifies different niches determined by microbial activities
DOI: 10.5281/zenodo.6628275
title: BLMtwitter: The Black Lives Matter (BLM) Twitter Corpus

description: In May 2020, the murder of George Floyd led to protests throughout the United States and around the world. Along with these protests, social media platforms saw a dramatic increase in the volume of discussion regarding Black Lives Matter (BLM). Two years on, online discussion of BLM continues to be lively, not just in the United States but also internationally. This dataset represents one of the most extensive collections of Black Lives Matter tweets available, covering from 2010 through May 2022 and including nearly 50 million tweets overall. Parallel corpora for All Lives Matter and Blue Lives matter are also included. Monthly updates for 2022 are available on Github (https://github.com/amaurits/BLMtwitter), along with a datasheet and a preprint describing the dataset in more detail.
DOI: 10.5281/zenodo.7155210
title: BLMtwitter: The Black Lives Matter (BLM) Twitter Corpus

description: In May 2020, the murder of George Floyd led to protests throughout the United States and around the world. Along with these protests, social media platforms saw a dramatic increase in the volume of discussion regarding Black Lives Matter (BLM). Two years on, online discussion of BLM continues to be lively, not just in the United States but also internationally. This dataset represents one of the most extensive collections of Black Lives Matter tweets available, covering from 2010 through May 2022 and including nearly 50 million tweets overall. Parallel corpora for All Lives Matter and Blue Lives matter are also included. Monthly updates for 2022 are available on Github (https://github.com/amaurits/BLMtwitter), along with a datasheet and a preprint describing the dataset in more detail.
description: Version 1.0 accidentally omitted black lives matter data for 2016. That is fixed in the current version (1.1).
DOI: 10.5281/zenodo.6628274
title: BLMtwitter: The Black Lives Matter (BLM) Twitter Corpus

description: In May 2020, the murder of George Floyd led to protests throughout the United States and around the world. Along with these protests, social media platforms saw a dramatic increase in the volume of discussion regarding Black Lives Matter (BLM). Two years on, online discussion of BLM continues to be lively, not just in the United States but also internationally. This dataset represents one of the most extensive collections of Black Lives Matter tweets available, covering from 2010 through 2022 and including more than 50 million tweets overall. Parallel corpora for All Lives Matter and Blue Lives matter are also included. Monthly updates for 2023 are available on Github (https://github.com/amaurits/BLMtwitter), along with a datasheet and a preprint describing the dataset in more detail.
description: Version 1.0 accidentally omitted black lives matter data for 2016. That was fixed in version 1.1. Version 1.2 updates the data through the end of 2022.
DOI: 10.5281/zenodo.7730083
title: BLMtwitter: The Black Lives Matter (BLM) Twitter Corpus

description: In May 2020, the murder of George Floyd led to protests throughout the United States and around the world. Along with these protests, social media platforms saw a dramatic increase in the volume of discussion regarding Black Lives Matter (BLM). Two years on, online discussion of BLM continues to be lively, not just in the United States but also internationally. This dataset represents one of the most extensive collections of Black Lives Matter tweets available, covering from 2010 through 2022 and including more than 50 million tweets overall. Parallel corpora for All Lives Matter and Blue Lives matter are also included. Monthly updates for 2023 are available on Github (https://github.com/amaurits/BLMtwitter), along with a datasheet and a preprint describing the dataset in more detail.
description: Version 1.0 accidentally omitted black lives matter data for 2016. That was fixed in version 1.1. Version 1.2 updates the data through the end of 2022.
DOI: 10.5281/zenodo.7628889
title: Mapping of Use Cases to ISO 12207 Processes

description: Mapping of software analytics use cases to software life cycle processes from ISO/IEC/IEEE 12207:2017 (IEEE Access paper: On Adopting Software Analytics for Managerial Decision-Making: A Practitioner's Perspective).
DOI: 10.5281/zenodo.7628890
title: Mapping of Use Cases to ISO 12207 Processes

description: Mapping of software analytics use cases to software life cycle processes from ISO/IEC/IEEE 12207:2017 (IEEE Access paper: On Adopting Software Analytics for Managerial Decision-Making: A Practitioner's Perspective).
DOI: 10.18112/openneuro.ds004529.v1.0.0
title: Modeling Face Recognition in the Predictive Coding Framework: A Combined Computational Modeling and Functional Imaging Study - Stage 2
DOI: 10.34740/kaggle/dsv/5159892
title: Nigerian Presidential Election 2023 Tweets
DOI: 10.5281/zenodo.7628974
title: Software Analytics Use Cases

description: Coded segments - software analytics use cases (IEEE Access paper: On Adopting Software Analytics for Managerial Decision-Making: A Practitioner's Perspective).
DOI: 10.5281/zenodo.7628975
title: Software Analytics Use Cases

description: Coded segments - software analytics use cases (IEEE Access paper: On Adopting Software Analytics for Managerial Decision-Making: A Practitioner's Perspective).
DOI: 10.5066/p9vjuctp
title: Comparisons from an Aqualog Fluorometer Standardized to Quinine Sulfate Equivalents (QSE) with Excitation (ex) and Emissions (em) Equivalent to Fluorescence of Dissolved Organic Matter (fDOM) Sensors from Multiple Manufacturers

description: Here, we present the results supporting Table 2 in Field Techniques for Fluorescence Measurements Targeting Dissolved Organic Matter, Hydrocarbons, and Wastewater in Environmental Waters: Principles and Guidelines for Instrument Selection, Operation and Maintenance, Quality Assurance, and Data Reporting. Table 2 shows comparisons from an Aqualog 800 benchtop fluorometer standardized to quinine sulfate equivalents (QSE) with excitation (ex) and emissions (em) equivalent to fluorescence of dissolved organic matter (fDOM) sensors from multiple manufacturers. Data are reported from two standard reference materials (SRM) and the mean, minimum, and maximum from 76 environmental samples. No replicates were collected for environmental samples; therefore, the relative standard deviation is not available.
DOI: 10.5281/zenodo.7130246
title: Urban cloud patterns and environmental factors dataset supporting for paper: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023)

description: The dataset supporting for the publication: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023), PNAS. The data format is in tabular form (.csv, comma delimited). Each row is the <strong>monthly </strong>aggregated \(\Delta_{CloudCover}\) for <strong>each </strong>city for daytime and nighttime, associated with the environmental factors used to study the relationship in the research. More specifically, the description of each factor associated with its unit formatted in the table are listed as follows: the spatial differences in cloud coverage (in percent) between urban and adjacent background domain (\(\Delta_{CloudCover}\)): <strong>cldmeand </strong>(in percent) the city size (\(log_{10}A\), A in km<sup>2</sup>) as an indicator of surface roughness and anthropogenic emission: <strong>CitySize </strong>(in \(log_{10}(km^{2})\)) the differential urban-background surface heating measured by land surface temperature (\(\Delta LST\)): <strong>Tempdiff </strong>(in K) the moisture availability measured by annual precipitation (P): <strong>precipannualbackground </strong>(in mm) the energy availability measured by mean annual temperature (\(\overline{T}\)): <strong>tempannualbackground </strong>(in \(^{o}C\)) the longitudes and latitudes for the defined city: <strong>xcoords </strong>(longitudes, in meters) and <strong>ycoords </strong>(latitudes, in meters) Other columns: <strong>NAME10</strong>: the name of urban domain or city defined in the study (CENSUS 2010) <strong>time</strong>: daytime (around 13 LT) or nighttime (around 1 LT). <strong>monthtext</strong>: specifies the month (January to December) <strong>urban_type</strong>: the geographical location of the defined urban area (either inland, coastal or mountainous city)
DOI: 10.5281/zenodo.7130245
title: Urban cloud patterns and environmental factors dataset supporting for paper: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023)

description: The dataset supporting for the publication: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023), PNAS. The data format is in tabular form (.csv, comma delimited). Each row is the <strong>monthly </strong>aggregated \(\Delta_{CloudCover}\) for <strong>each </strong>city for daytime and nighttime, associated with the environmental factors used to study the relationship in the research. More specifically, the description of each factor associated with its unit formatted in the table are listed as follows: the spatial differences in cloud coverage (in percent) between urban and adjacent background domain (\(\Delta_{CloudCover}\)): <strong>cldmeand </strong>(in percent) the city size (\(log_{10}A\), A in km<sup>2</sup>) as an indicator of surface roughness and anthropogenic emission: <strong>CitySize </strong>(in \(log_{10}(km^{2})\)) the differential urban-background surface heating measured by land surface temperature (\(\Delta LST\)): <strong>Tempdiff </strong>(in K) the moisture availability measured by annual precipitation (P): <strong>precipannualbackground </strong>(in mm) the energy availability measured by mean annual temperature (\(\overline{T}\)): <strong>tempannualbackground </strong>(in \(^{o}C\)) the longitudes and latitudes for the defined city: <strong>xcoords </strong>(longitudes, in meters) and <strong>ycoords </strong>(latitudes, in meters) Other columns: <strong>NAME10</strong>: the name of urban domain or city defined in the study (CENSUS 2010) <strong>time</strong>: daytime (around 13 LT) or nighttime (around 1 LT). <strong>monthtext</strong>: specifies the month (January to December) <strong>urban_type</strong>: the geographical location of the defined urban area (either inland, coastal or mountainous city)