cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3700770
title: Round or rectangular tables for collaborative problem solving? A multimodal learning analytics study

description: This dataset contains the necessary details to reproduce the experiments of the paper : Vujovic M., Hernández-Leo, D., Tassani S., Spikol D. (2020). Round or rectangular tables for collaborative problem solving? A multimodal learning analytics study, (in the process of revision) <em>British Journal of Education Technology</em> This data represents measurements collected from two experiments explained in the article: Qualitative data - observations Experiment_university students - measurements (distance between students, range of movement, level of participation) Experiment_elementary school students - measurements (distance between students, range of movement, level of participation) We would appreciate it if you cite the paper after using the dataset.
DOI: 10.5281/zenodo.3700775
title: Verifying OpenJDK's LinkedList using KeY: Additional artefacts belonging to master thesis

description: The file testcases.tar.gz contains source code files w.r.t. 64 test cases carried out for this thesis. It also contains log files that contain output of these test cases. The latter are also contained in the file Appendix.pdf, which serves as an on-line appendix for the thesis. Other sources which are of importance for the thesis can be found in https://doi.org/10.5281/zenodo.3517081. Three proof files have been re-established for the purpose of describing these proofs in the thesis. Thus: they deviate from the ones that can be found in https://doi.org/10.5281/zenodo.3517081. These three can be found in the file "Three renewed proof files.zip". It concerns proof files for lastIndexOf(Object), linkFirst(Object), and addFirst(Object). https://doi.org/10.5281/zenodo.3517081 is a link that has been created to store artefacts w.r.t. a paper for the TACAS conference in April 2020 in Dublin, Ireland. The paper carries the same title as this thesis.
DOI: 10.5281/zenodo.3700776
title: Verifying OpenJDK's LinkedList using KeY: Additional artefacts belonging to master thesis

description: The file testcases.tar.gz contains source code files w.r.t. 64 test cases carried out for this thesis. It also contains log files that contain output of these test cases. The latter are also contained in the file Appendix.pdf, which serves as an on-line appendix for the thesis. Other sources which are of importance for the thesis can be found in https://doi.org/10.5281/zenodo.3517081. Three proof files have been re-established for the purpose of describing these proofs in the thesis. Thus: they deviate from the ones that can be found in https://doi.org/10.5281/zenodo.3517081. These three can be found in the file "Three renewed proof files.zip". It concerns proof files for lastIndexOf(Object), linkFirst(Object), and addFirst(Object). https://doi.org/10.5281/zenodo.3517081 is a link that has been created to store artefacts w.r.t. a paper for the TACAS conference in April 2020 in Dublin, Ireland. The paper carries the same title as this thesis.
DOI: 10.5281/zenodo.3700833
title: Twitter Crawling for "Viral" or "Heboh" News

description: The data we take is about tweet form 10 official account of news portal on twitter that mentioned word "Viral" or "Heboh". The dataset contain 5 columns and sorted by time.
DOI: 10.5281/zenodo.3700834
title: Twitter Crawling for "Viral" or "Heboh" News

description: The data we take is about tweet form 10 official account of news portal on twitter that mentioned word "Viral" or "Heboh". The dataset contain 5 columns and sorted by time.
DOI: 10.5281/zenodo.3700842
title: alfalifr/twitterCrawlViral: Twitter Crawling for "Viral" or "Heboh" News

description: The data we take is about tweet form 10 official account of news portal on twitter that mentioned word "Viral" or "Heboh". The dataset contain 5 columns and sorted by time.
DOI: 10.5281/zenodo.3700843
title: alfalifr/twitterCrawlViral: Twitter Crawling for "Viral" or "Heboh" News

description: The data we take is about tweet form 10 official account of news portal on twitter that mentioned word "Viral" or "Heboh". The dataset contain 5 columns and sorted by time.
DOI: 10.5281/zenodo.3667632
title: Druggability Assessment in TRAPP using Machine Learning Approaches

description: This archive contains data sets and software codes used for building ML models reported in the paper "Druggability Assessment in TRAPP using Machine Learning Approaches"; <em>J. Chem. Inf. Model.</em> 2020, 60, 3, 1685–1699; https://doi.org/10.1021/acs.jcim.9b01185
DOI: 10.17632/gyfpgdyms8.1
title: Data for: A novel method for fabricating bioinspired layered nanocomposites using aligned graphene oxide/PVDF and their micromechanical modeling

description: FTIR raw data used to generate Figure 5: Optical images and FTIR results for (a) electrospun fiber mats ES and (b) after folding and hot pressing HP
DOI: 10.3886/e116386v1-22874
title: Replication data for: Behavioral CEOs: The Role of Managerial Overconfidence

description: In this paper, we provide a theoretical and empirical framework that allows us to synthesize and assess the burgeoning literature on CEO overconfidence. We also provide novel empirical evidence that overconfidence matters for corporate investment decisions in a framework that explicitly addresses the endogeneity of firms' financing constraints.
DOI: 10.5281/zenodo.3700814
title: Elevation data to accompany "A curvature-based method for measuring valley width applied to glacial and fluvial landscapes"

description: This repository contains elevation data, derivatives, and manual measurements used in the manuscript "A curvature-based method for measuring valley width applied to glacial and fluvial landscapes" submitted to the Journal of Geophysical Research. This data is derived from the 1/3 arc-second (~10 m) resolution seamless digital elevation models from the 3D Elevation Program (3DEP) in the coterminous United States [0]. The Canadian Rockies study site uses 90 m resolution data derived from the Shuttle Radar Topography Mission (SRTM) dataset [1]. Each study site corresponds to a single directory (e.g. 'valley_width/olympics/'). Derivatives are stored in GeoTIFF format (with no file extensions) with the following naming conventions (shown for the Olympic Mountains study area). Manual width measurements are stored as Pickle files (e.g. 'olympics_width_fluvial.p') in the measurements/ subdirectory in each case. - olympics_area (Catchment area)<br> - olympics_elevation (Elevation)<br> - olympics_filled (Hydrologically corrected elevation)<br> - olympics_flow_direction (Flow direction)<br> - olympics_mask_fluvial (Binary mask of fluvial catchments)<br> - olympics_mask_glacial (Binary mask of glacial catchments)<br> - olympics_unnormalized_width (Valley width estimated without using scale normalization)<br> - olympics_width (Valley width estimated using scale normalization) Python scripts to reproduce the major figures and analysis and their EPS output are also included. References [0] https://www.usgs.gov/core-science-systems/ngp/3dep/about-3dep-products-services [1] http://srtm.csi.cgiar.org/srtmdata/
DOI: 10.5281/zenodo.3700815
title: Elevation data to accompany "A curvature-based method for measuring valley width applied to glacial and fluvial landscapes"

description: This repository contains elevation data, derivatives, and manual measurements used in the manuscript "A curvature-based method for measuring valley width applied to glacial and fluvial landscapes" submitted to the Journal of Geophysical Research. This data is derived from the 1/3 arc-second (~10 m) resolution seamless digital elevation models from the 3D Elevation Program (3DEP) in the coterminous United States [0]. The Canadian Rockies study site uses 90 m resolution data derived from the Shuttle Radar Topography Mission (SRTM) dataset [1]. Each study site corresponds to a single directory (e.g. 'valley_width/olympics/'). Derivatives are stored in GeoTIFF format (with no file extensions) with the following naming conventions (shown for the Olympic Mountains study area). Manual width measurements are stored as Pickle files (e.g. 'olympics_width_fluvial.p') in the measurements/ subdirectory in each case. - olympics_area (Catchment area)<br> - olympics_elevation (Elevation)<br> - olympics_filled (Hydrologically corrected elevation)<br> - olympics_flow_direction (Flow direction)<br> - olympics_mask_fluvial (Binary mask of fluvial catchments)<br> - olympics_mask_glacial (Binary mask of glacial catchments)<br> - olympics_unnormalized_width (Valley width estimated without using scale normalization)<br> - olympics_width (Valley width estimated using scale normalization) Python scripts to reproduce the major figures and analysis and their EPS output are also included. References [0] https://www.usgs.gov/core-science-systems/ngp/3dep/about-3dep-products-services [1] http://srtm.csi.cgiar.org/srtmdata/
DOI: 10.7910/dvn/rbn3xg/fx7vow
title: sub-001_ses-01_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/nrkaxr
title: sub-001_ses-01_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/9i9zjb
title: sub-002_ses-01_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/b78gti
title: sub-002_ses-05_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/cb7bmh
title: sub-002_ses-03_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/i8dxsb
title: sub-002_ses-02_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/nvw7gg
title: sub-002_ses-04_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/nwer8n
title: sub-002_ses-05_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/rzt9pe
title: sub-002_ses-03_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/xex4yl
title: sub-002_ses-01_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/dmwq8b
title: sub-002_ses-09_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/gsbvjz
title: sub-002_ses-04_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/quwfun
title: sub-002_ses-07_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/rwa0bb
title: sub-002_ses-06_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/ssu28w
title: sub-002_ses-10_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/xflcjd
title: sub-002_ses-02_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.5519/qd.wf9ceuaj
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (10631 records)
DOI: 10.7910/dvn/rbn3xg/0yig0v
title: sub-002_ses-10_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/7a5jz3
title: sub-002_ses-09_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/ayb9kb
title: sub-002_ses-14_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/bi9bkt
title: sub-002_ses-08_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/e3pnk7
title: sub-002_ses-07_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/l6ff66
title: sub-002_ses-08_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/zjhrwb
title: sub-002_ses-06_task-motorimagery_eeg.set

description: :unav
DOI: 10.5281/zenodo.3700858
title: Dataset of Grouped Commit Author IDs after Identity Resolution

description: This Dataset contains the SHA1 values of IDs for 5,427,024 commit authors who have created commits in git version control system, and have more than 1 ID in git. It is a compressed CSV file (separated by ; ) with 14,861,538 author IDs, where the first column is the group ID, which is same as the first (randomly selected) author ID of the group, and the second column is the author ID that is part of the group. If an author was found to have 2 different IDs: <em>I1, I2</em>, then it is recorded in the file in 2 separate lines, with the lines being <strong>I1;I1</strong> and <strong>I1;I2</strong>, i.e. the first column is the group identifier, which is one of the IDs in a group, and the second column contains the different author IDs in separate lines. Author IDs consist of the Author's name and email address in the format: Name &lt;Email&gt;.
DOI: 10.5281/zenodo.3700859
title: Dataset of Grouped Commit Author IDs after Identity Resolution

description: This Dataset contains the SHA1 values of IDs for 5,427,024 commit authors who have created commits in git version control system, and have more than 1 ID in git. It is a compressed CSV file (separated by ; ) with 14,861,538 author IDs, where the first column is the group ID, which is same as the first (randomly selected) author ID of the group, and the second column is the author ID that is part of the group. If an author was found to have 2 different IDs: <em>I1, I2</em>, then it is recorded in the file in 2 separate lines, with the lines being <strong>I1;I1</strong> and <strong>I1;I2</strong>, i.e. the first column is the group identifier, which is one of the IDs in a group, and the second column contains the different author IDs in separate lines. Author IDs consist of the Author's name and email address in the format: Name &lt;Email&gt;.
DOI: 10.7910/dvn/rbn3xg/5kdnux
title: sub-002_ses-13_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/fdn16y
title: sub-002_ses-13_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/goit7l
title: sub-002_ses-15_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/haibxt
title: sub-002_ses-12_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/idciqn
title: sub-002_ses-12_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/iiwx4m
title: sub-002_ses-14_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/jsdcus
title: sub-002_ses-15_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/ny15wc
title: sub-002_ses-11_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/xy7fr6
title: sub-002_ses-11_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/3nrl7g
title: sub-002_ses-18_task-rest_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/cebpz2
title: sub-002_ses-17_task-rest_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/f0vdtv
title: sub-002_ses-16_task-rest_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/mfplzi
title: sub-002_ses-19_task-rest_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/npmfin
title: sub-002_ses-18_task-rest_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/vee2bs
title: sub-002_ses-16_task-rest_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/wcehgq
title: sub-002_ses-17_task-rest_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/y5g6mi
title: sub-002_ses-19_task-rest_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/6gakgz
title: sub-001_ses-02_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/btsicx
title: sub-001_ses-09_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/ffsrtr
title: sub-001_ses-07_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/hj1rsz
title: sub-001_ses-04_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/ido9bm
title: sub-001_ses-04_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/jsdcgc
title: sub-001_ses-05_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/olv779
title: sub-001_ses-02_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/2mk0rv
title: sub-001_ses-09_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/3gnzth
title: sub-001_ses-03_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/b0fzle
title: sub-001_ses-10_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/csbjfe
title: sub-001_ses-10_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/ehereo
title: sub-001_ses-08_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/ftvruu
title: sub-001_ses-07_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/kfygft
title: sub-001_ses-03_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/sdx7xi
title: sub-001_ses-06_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/vvpw2v
title: sub-001_ses-08_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/0pny58
title: sub-001_ses-15_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/7ypv2b
title: sub-001_ses-06_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/fvsoon
title: sub-001_ses-19_task-rest_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/j8q6to
title: sub-001_ses-17_task-rest_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/kwtm1b
title: sub-001_ses-05_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/vu3xav
title: sub-001_ses-15_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/z9ebli
title: sub-001_ses-14_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.5281/zenodo.3699664
title: A mapping between Bot Commit, Projects, Files, and Blobs

description: This dataset contains information about 150,633,947 file updates by 12,326,137 bot commits, in terms of blob updates. Each line in the file is of the following form: commit_sha; projects (projectOwner_projectName) associated with the commit separated by comma; filename; new blob associated with the file; old blob associated with the file (before the commit) Example:<br> 00000a0cee231959811e6c5d808730240b325417;guyellis_plant;package.json;fadda016e338f970adc62395e77aab80b09fa11b;2e7b221f2bfdeda4ef78d3a56e039e456b6901bb <br> Contents of each blob can be accessed using the GitHub API: https://api.github.com/repos/&lt;projectOwner&gt;/&lt;projectName&gt;/git/blobs/&lt;blob id&gt; Example: https://api.github.com/repos/guyellis/plant/git/blobs/fadda016e338f970adc62395e77aab80b09fa11b See https://developer.github.com/v3/git/blobs/ for more deatils. If you're using our data, please do not forget it cite it !!!
DOI: 10.7910/dvn/rbn3xg/hdqbaf
title: sub-001_ses-13_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/hoi35f
title: sub-001_ses-12_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/tcfrfa
title: sub-001_ses-19_task-rest_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/tcnkyu
title: sub-001_ses-18_task-rest_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/u6elcv
title: sub-001_ses-12_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/xjpjdq
title: sub-001_ses-16_task-rest_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/zarmid
title: sub-001_ses-16_task-rest_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/8rsn8z
title: sub-001_ses-11_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/aibq0b
title: sub-001_ses-17_task-rest_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/ajggha
title: sub-001_ses-13_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/bseflb
title: sub-001_ses-11_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/cuxv0i
title: sub-003_ses-02_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/ec0gev
title: sub-001_ses-14_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/g5x4iu
title: sub-003_ses-08_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/kyyilh
title: sub-001_ses-18_task-rest_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/0c6pxy
title: sub-003_ses-06_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/1bnjcp
title: sub-003_ses-08_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/c1b2dd
title: sub-003_ses-01_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/ckz2lt
title: sub-003_ses-04_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/hrr2uu
title: sub-003_ses-04_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/ketpfl
title: sub-003_ses-05_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/w9yhwn
title: sub-003_ses-02_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/xf8ol1
title: sub-003_ses-09_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/y9ipxs
title: sub-003_ses-07_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/2o5qhi
title: sub-003_ses-03_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/50eg7e
title: sub-003_ses-09_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/clzdaz
title: sub-003_ses-06_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/dnmghy
title: sub-003_ses-05_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/jtq2hc
title: sub-003_ses-10_task-motorimagery_eeg.fdt

description: :unav
DOI: 10.7910/dvn/rbn3xg/kzxv9f
title: sub-003_ses-10_task-motorimagery_eeg.set

description: :unav
DOI: 10.7910/dvn/rbn3xg/mksp73
title: sub-003_ses-03_task-motorimagery_eeg.fdt

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DOI: 10.7910/dvn/rbn3xg/pichnp
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DOI: 10.5519/qd.yzgclcje
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DOI: 10.7910/dvn/rbn3xg/lqkkd4
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DOI: 10.3886/e118142
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DOI: 10.7910/dvn/rbn3xg/cjhvrc
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