cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3841721
title: Duhumbi Grammar - Sound Files, Toolbox and Transcriber File, PDFs of files

description: This data set contains the .wav sound files, .trs Transcriber files, .txt Toolbox-compatible Notepad files and .pdf files with the completely transcribed, glossed, parsed and translated examples of the recordings that belong to the following publication: Bodt, Timotheus Adrianus. 2020. Grammar of Duhumbi. Leiden: Brill. ISBN 978-90-04-40947-7. https://brill.com/view/title/55767 The explanation of all the grammatical features that occur in these sound files can be found in the Grammar of Duhumbi. The main Toolbox files can be found in the zip file “Settings”, this includes the IPA keys for Duhumbi, the entire setup of the Toolbox database, and the Duhumbi dictionary and Parsing dictionary. The .wav, .txt and .trs files combined in the same folder will enable to open Toolbox and work with the recordings, e.g. play them sentence for sentence and see the transcriptions and translations. Transcriber version 1.5.1: http://trans.sourceforge.net/en/presentation.php or https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/ Toolbox version 1.6.1: https://software.sil.org/toolbox/download/ This data set contains the files belonging to the sound files as mentioned in the pdf file “Duhumbi Grammar All Files Upload 1”. The S/N code corresponds to the code used in the Grammar to identify the text from which an example was taken. The name of the file refers to the name of the .wav, .trs, .txt and .pdf files in this upload. The subject is a short description of the topic of the text. The duration is the duration of the recording. For the metadata of the sound files in this data set, I refer to Chapter 13 Texts in the Grammar of Duhumbi. This Chapter has a complete listing of the texts, their topics, the speakers and their background etc. This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collector of the material. By downloading this material, you agree to these restrictions. This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on https://creativecommons.org/licenses/by-nc-sa/4.0/. Legal Code on https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode. Tim Bodt: monpasang (at) gmail (dot) com
DOI: 10.15161/oar.it/23685
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.3842342
title: Data for 2020GL087949R

description: Data used in the paper 2020GL087949R, which has been submitted to Geophysical Research Letters. This is the new version of 'Data for 2020GL087949' (https://doi.org/10.5281/zenodo.3715555).
DOI: 10.5281/zenodo.3843228
title: Weakly Supervised Airplane Detection Dataset: WSADD

description: The images in this dataset includes airport and nearby areas of different countries (mainly from China, the United States, the United Kingdom, France, Japan, and Singapore) taken from the Google Earth satellite. The dataset comprises 700 remote sensing images in total, of which 400 images contain an aircraft target (the ‘’positive sample set’’) and the other 300 do not (the ‘’negative sample set’’). In the process of dataset construction, the spatial resolution of the image was controlled between 0.3 m and 2 m, and the size was fixed to 768×768. We sought to collect images from different sensors during different daytimes, different seasons, and different light intensities to ensure that the dataset has a high diversity.
DOI: 10.5281/zenodo.3843229
title: Weakly Supervised Airplane Detection Dataset: WSADD

description: The images in this dataset includes airport and nearby areas of different countries (mainly from China, the United States, the United Kingdom, France, Japan, and Singapore) taken from the Google Earth satellite. The dataset comprises 700 remote sensing images in total, of which 400 images contain an aircraft target (the ‘’positive sample set’’) and the other 300 do not (the ‘’negative sample set’’). In the process of dataset construction, the spatial resolution of the image was controlled between 0.3 m and 2 m, and the size was fixed to 768×768. We sought to collect images from different sensors during different daytimes, different seasons, and different light intensities to ensure that the dataset has a high diversity.
DOI: 10.3886/e114988v1
title: Regulation by Shaming: Deterrence Effects of Publicizing Violations of Workplace Safety Laws

description: Publicizing firms' socially undesirable actions may enhance firms' incentives to avoid such actions. In 2009, the Occupational Safety and Health Administration (OSHA) began issuing press releases about facilities that violated safety and health regulations. Using quasi-random variation arising from a cutoff rule OSHA followed, I find that publicizing a facility's violations led other facilities to substantially improve their compliance and experience fewer occupational injuries. OSHA would need to conduct 210 additional inspections to achieve the same improvement in compliance as achieved with a single press release. Evidence suggests that employers improve compliance to avoid costly responses from workers.
DOI: 10.5281/zenodo.1239731
title: Individual versus computer-supported collaborative self-explanations: how do their writing analytics differ? Coh-Metrix results and students' excerpts.

description: Coh-Metrix results and students' excerpts of the publication: Albó, L., Beardsley, M., Amarasinghe, I. & Hernández-Leo, D. (2020). Individual versus computer-supported collaborative self-explanations: how do their writing analytics differ? In International conference on Advanced Learning Technologies and Technology-enhanced Learning (ICALT).
DOI: 10.5281/zenodo.1239732
title: Individual versus computer-supported collaborative self-explanations: how do their writing analytics differ? Coh-Metrix results and students' excerpts.

description: Coh-Metrix results and students' excerpts of the publication: Albó, L., Beardsley, M., Amarasinghe, I. & Hernández-Leo, D. (2020). Individual versus computer-supported collaborative self-explanations: how do their writing analytics differ? In International conference on Advanced Learning Technologies and Technology-enhanced Learning (ICALT).
DOI: 10.5281/zenodo.3843577
title: From smart apes to human brain boxes. A uniquely derived brain shape in late hominins clade: supplementary material

description: DATA.rda: an RData file containing the phylogenetic tree, the landmark and semilandmark sets used in the analyses of the endocast shape (123 specimens), the semilandmark sets used in the asymmetry analyses (120 specimens), the vector of the corresponding ECV values and a mesh containing the triangulated semilandmark configuration. script.R: the annotated script used to replicate the analyses reported in this study.
DOI: 10.5281/zenodo.3843255
title: GAW URLs and time stamps 2015-12

description: The dataset comprises the URLs and corresponding time stamps of the GAW snapshot harvested in 2015-12. The compressed directory <em>2015-12.zip</em> contains 100 files. Each file contains two columns delimited by a tab. The time stamps are given in the format of the java equivalent yyyyMMddHHmmss and are parsed as: <pre>DateTime.parse(timeStamp, DateTimeFormat.forPattern("yyyyMMddHHmmss")).getMillis</pre>
DOI: 10.5281/zenodo.3843254
title: GAW URLs and time stamps 2015-12

description: The dataset comprises the URLs and corresponding time stamps of the GAW snapshot harvested in 2015-12. The compressed directory <em>2015-12.zip</em> contains 100 files. Each file contains two columns delimited by a tab. The time stamps are given in the format of the java equivalent yyyyMMddHHmmss and are parsed as: <pre>DateTime.parse(timeStamp, DateTimeFormat.forPattern("yyyyMMddHHmmss")).getMillis</pre>
DOI: 10.5281/zenodo.3843581
title: Global consensus map and dynamics of human transcription factor footprints

description: Data associated with publication "Global reference mapping and dynamics of human transcription factor footprints". https://doi.org/10.1101/2020.01.31.927798 These data describe digital genomic footprints derived from 243 human biosamples. <strong>Please see http://vierstra.org/resources/dgf for additional information and a complete set of raw DNase I data.</strong> Metadata with biosample annotation and ENCODE project accessions in Excel format (Extended Data Table 1): Consensus_footprints_metadata.xlsx Motif clustering metdata in Excel format (Extended Data Table 2): Motif_clustering_metadata.xlsx Raw footprint call within individual datasets at a FDR cutoff of 0.01 (additional levels of FDR thresholding can be made available by request). The following tarball contains 243 BED-formatted files, each corresponding to an individual dataset. (Extended Data File 1). Footprints_per_sample.0q01.tar.gz Tab-delimited files with consensus footprint coordinates and overlaps with matches to motif model clusters in human genome build GRCh38/hg38. The legend file contains column definitions in detail (Extended Data File 2). Consensus_footprints_and_motifs_hg38.bed.gz Consensus_footprints_and_motifs_legend.txt BED-formatted file containing the coordinates for clustered motif model matches that overlap consensus footprints (Extended Data File 3) Collapsed_motifs_overlaping_consensus_footprints.bed.gz Collapsed_motifs_overlaping_consensus_footprints_legend.txt Footprint occupancy matrix of index footprints (rows) vs. biosamples (columns). Rows are same order as the consensus footprint file and columns are same order as in the metadata files (Extended Data Files 4 and 5). Consensus_footprints_and_motifs_matrix_full_hg38.txt.gz (Values are -log(1-posterior)) Consensus_footprints_and_motifs_matrix_binary_hg38.txt.gz (binary occupancy matrix, where footprints with posterior footprint probability &gt;0.99 are considered occupied) Single nucleotide variants tested for allelic imbalance. The legend file contains column definitions in detail (Extended Data File 6). De_novo_SNVs_tested_for_imbalance.bed.gz De_novo_SNVs_tested_for_imbalance.legend.txt Contact: Jeff Vierstra (jvierstra@altius.org)
description: This work was supported by NHGRI grant U54HG007010.
description: {"references": ["Vierstra et al. Global reference mapping and dynamics of human transcription factor footprints. (2020). bioRxiv"]}
DOI: 10.5281/zenodo.3843215
title: Anatomically Detailed Human Atrial FE Meshes

description: The left atrium (LA) has a complex anatomy with heterogeneous wall thickness and curvature. We include 3 patient-specific anatomical FE meshes with rule-based myofiber directions of each of the anatomies included in our study ("The impact of wall thickness and curvature on wall stress in patient-specific electromechanical models of the left atrium", BMMB, 2020, https://pubmed.ncbi.nlm.nih.gov/31802292/).<br> Additionally we include<br> - a model with Gaussian noise added (mean 0 um , standard deviation 100 um) to the initial geometry of patient case 3 and subsequently smoothed using ParaView; and <br> - a mesh with a constant wall thickness of 0.5 mm generated based on the endocardial surface of patient case 3. The meshes are given in VTK file format (.vtu) and in the binary format used for the Cardiac Arrhythmia Research Package simulator, see https://carpentry.medunigraz.at/carputils/index.html and https://opencarp.org. Here, for each of the geometries, we include a list of nodal coordinates (.bpts file), a list of triangular elements (.belem file), fiber fields (.blon file), surface files (*.surf files), and surface points (*.surf.vtx files).<br> Surface files include the endocardium (laendo.surf), the epicardium (laepi.surf), the mitral valve ring (mitralvv.surf), the pulmonary outlet rings (pulvring.surf) and lids (lid*.vtx) to close the five in- and outlets of the LA. Using the open source mesh utiliy "MeshTool" (https://bitbucket.org/aneic/meshtool/src/master/README.md)<br> meshes can be manipulated or converted to VTK or EnSight file formats.
DOI: 10.5281/zenodo.3843216
title: Anatomically Detailed Human Atrial FE Meshes

description: The left atrium (LA) has a complex anatomy with heterogeneous wall thickness and curvature. We include 3 patient-specific anatomical FE meshes with rule-based myofiber directions of each of the anatomies included in our study (The impact of wall thickness and curvature on wall stress in patient-specific electromechanical models of the left atrium, BMMB, 2020, https://pubmed.ncbi.nlm.nih.gov/31802292/).<br> Additionally we include<br> - a noised model with Gaussian noise added (mean 0 um , standard deviation 100 um ) to the initial geometry of patient case 3 and subsequently smoothed using ParaView; and <br> - a mesh with a constant wall thickness of 0.5 mm generated based on the endocardial surface of patient case 3. The meshes are in the binary format for the Cardiac Arrhythmia Research Package simulator, see https://carpentry.medunigraz.at/carputils/index.html and https://opencarp.org. For each of the geometries, we include a list of nodal coordinates (.bpts file), a list of triangular elements (.belem file), fiber fields (.blon file), surface files (*.surf files), and surface points (*.surf.vtx files). Using the open source mesh manipulation utiliy "MeshTool" (https://bitbucket.org/aneic/meshtool/src/master/README.md)<br> meshes can be converted to VTK or EnSight file formats.
DOI: 10.5281/zenodo.3847859
title: GDSC1

description: GDSC1 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3847860
DOI: 10.5281/zenodo.3847860
title: GDSC1

description: GDSC1 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3847860
DOI: 10.5281/zenodo.3843266
title: GAW URLs and time stamps 2015-05

description: The dataset comprises the URLs and corresponding time stamps of the GAW snapshot harvested in 2015-05. The compressed directory <em>2015-05.zip</em> contains 100 files. Each file contains two columns delimited by a tab. The time stamps are given in the format of the java equivalent yyyyMMddHHmmss and are parsed as: <pre>DateTime.parse(timeStamp, DateTimeFormat.forPattern("yyyyMMddHHmmss")).getMillis</pre>
DOI: 10.5281/zenodo.3843267
title: GAW URLs and time stamps 2015-05

description: The dataset comprises the URLs and corresponding time stamps of the GAW snapshot harvested in 2015-05. The compressed directory <em>2015-05.zip</em> contains 100 files. Each file contains two columns delimited by a tab. The time stamps are given in the format of the java equivalent yyyyMMddHHmmss and are parsed as: <pre>DateTime.parse(timeStamp, DateTimeFormat.forPattern("yyyyMMddHHmmss")).getMillis</pre>
DOI: 10.5281/zenodo.3842853
title: telota/lebenswelten-lehndorff: Publication of XML/TEI-data of "Lebenswelten Lehndorff"

description: XML/TEI-files (documents and indexes) from the digital edition "Lebenswelten, Erfahrungsräume und politische Horizonte der ostpreußischen Adelsfamilie Lehndorff vom 18. bis in das 20. Jahrhundert".
DOI: 10.5281/zenodo.3842854
title: telota/lebenswelten-lehndorff: Publication of XML/TEI-data of "Lebenswelten Lehndorff"

description: XML/TEI-files (documents and indexes) from the digital edition "Lebenswelten, Erfahrungsräume und politische Horizonte der ostpreußischen Adelsfamilie Lehndorff vom 18. bis in das 20. Jahrhundert".
DOI: 10.15468/hvshrh
title: Two new species of Afroarabiella Yakovlev, 2008 (Lepidoptera, Cossidae) from Sudan and Ethiopia

description: This dataset contains the digitized treatments in Plazi based on the original journal article Yakovlev, Roman V., Witt, Thomas J. (2016): Two new species of Afroarabiella Yakovlev, 2008 (Lepidoptera, Cossidae) from Sudan and Ethiopia. European Journal of Taxonomy 240 (240): 1-8, DOI: http://dx.doi.org/10.5852/ejt.2016.240
DOI: 10.5281/zenodo.3843287
title: GAW URLs and time stamps 2014-12

description: The dataset comprises the URLs and corresponding time stamps of the GAW snapshot harvested in 2014-12. The compressed directory <em>2014-12.zip</em> contains 100 files. Each file contains two columns delimited by a tab. The time stamps are given in the format of the java equivalent yyyyMMddHHmmss and are parsed as: <pre>DateTime.parse(timeStamp, DateTimeFormat.forPattern("yyyyMMddHHmmss")).getMillis</pre>
DOI: 10.5281/zenodo.3843288
title: GAW URLs and time stamps 2014-12

description: The dataset comprises the URLs and corresponding time stamps of the GAW snapshot harvested in 2014-12. The compressed directory <em>2014-12.zip</em> contains 100 files. Each file contains two columns delimited by a tab. The time stamps are given in the format of the java equivalent yyyyMMddHHmmss and are parsed as: <pre>DateTime.parse(timeStamp, DateTimeFormat.forPattern("yyyyMMddHHmmss")).getMillis</pre>
DOI: 10.5281/zenodo.3847898
title: GDSC2

description: GDSC2 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3847898
DOI: 10.5281/zenodo.3847897
title: GDSC2

description: GDSC2 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3847898
DOI: 10.5281/zenodo.3847937
title: FIMM

description: FIMM PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3847938
DOI: 10.5281/zenodo.3847938
title: FIMM

description: FIMM PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3847938
DOI: 10.5281/zenodo.3834845
title: Supplementary Material for the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting

description: This is the dataset used in the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting. It is composed of: - Pre-trained models using active learning for document screening on HealthCLEF and Epistemonikos datasets. - Epistemonikos and HealthCLEF datasets containing medical questions and relevant/non relevant articles. - Embeddings and Document Representations used for experiments on both datasets. Scripts to run experiments can be found at: https://github.com/afcarvallo/active_learning_document_screening <strong>Paper abstract:</strong> Document screening is a fundamental task within Evidence-based Medicine (EBM), a practice that provides scientific evidence to support medical decisions. Several approaches have tried to reduce physicians' workload of screening and labeling vast amounts of documents to answer clinical questions. Previous works tried to semi-automate document screening, reporting promising results, but their evaluation was conducted on small datasets, which hinders generalization. Moreover, recent works in natural language processing have introduced neural language models, but none have compared their performance in EBM. In this paper, we evaluate the impact of several document representations such as TF-IDF along with neural language models (BioBERT, BERT, Word2vec, and GloVe) on an active learning-based setting for document screening in EBM. Our goal is to reduce the number of documents that physicians need to label to answer clinical questions. We evaluate these methods using both a small challenging dataset (HealthCLEF 2017) as well as a larger one but easier to rank (Epistemonikos). Our results indicate that word as well as textual neural embeddings always outperform the traditional TF-IDF representation. When comparing among neural and textual embeddings, in the HealthCLEF dataset the models BERT and BioBERT yielded the best results. On the larger dataset, Epistemonikos, Word2Vec and BERT were the most competitive, showing that BERT was the most consistent model across different corpuses. In term of active learning, an uncertainty sampling strategy combined with logistic regression achieved the best performance overall, above other methods under evaluation, and in fewer iterations.
DOI: 10.5281/zenodo.3843297
title: Replication Package for the Paper: "Code Smells Detection via Code Review: An Empirical Study"

description: This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020. <strong>1. data folder</strong> The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL. <strong>2. scripts floder</strong> The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms. <em>smell-term/general_smell_terms.txt</em> contains general code smell terms, such as "code smell". <em>smell-term/specific_smell_terms.txt</em> contains specific code smell terms, such as "dead code". <em>smell-term/misspelling_terms_of_smell.txt</em> contains the misspelling terms of 'smell', such as "ssell". <em>get_changes.py</em> is used for getting code changes from OpenStack. <em>get_comments.py</em> is used for getting review comments for each code change. <em>smell_search.py</em> is used for searching review comments that contain code smell terms. <strong>3. project folder</strong> The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download. <em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool. <em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.