cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1307632
title: 1112

description: 1213
DOI: 10.5281/zenodo.1307633
title: 1112

description: 1213
DOI: 10.5281/zenodo.1307634
title: 11111

description: 11111
DOI: 10.5281/zenodo.1307635
title: 11111

description: 11111
DOI: 10.6084/m9.figshare.6124460.v1
title: GO annotation

description: Microsatellites obtained in the transcriptome of Portunus sanguinolentus.
DOI: 10.23645/epacomptox.5491516.v1
title: DSSTox SDF File 10-11-2017

description: DSSTox SDF File October 2017
DOI: 10.5281/zenodo.1009446
title: Countwords

description: Results from tool executed using PubRunner on MEDLINE corpus. Code for the tool can be found at https://github.com/NCBI-Hackathons/PubRunner/tree/master/server/tools/CountWords/0.1
DOI: 10.5281/zenodo.1009447
title: Countwords

description: Results from tool executed using PubRunner on MEDLINE corpus. Code for the tool can be found at https://github.com/NCBI-Hackathons/PubRunner/tree/master/server/tools/CountWords/0.1
DOI: 10.5281/zenodo.1009660
title: Countwordserror

description: Results from tool executed using PubRunner on MEDLINE corpus. Code for the tool can be found at https://github.com/NCBI-Hackathons/PubRunner/tree/master/server/tools/CountWordsError/0.1
DOI: 10.5281/zenodo.1307636
title: 3333

description: 3333
DOI: 10.5281/zenodo.1307637
title: 3333

description: 3333
DOI: 10.6084/m9.figshare.1057646
title: Data for 133885 GDB-9 molecules

description: Data for 133885 GDB-9 molecules<br>In an earlier version of this dataset the titles of the files "Data for 133885 GDB-9 molecules" and "Data for 6095 constitutional isomers of C7H10O2" were swapped. This has now been corrected.<br>
DOI: 10.6084/m9.figshare.1057646.v2
title: Data for 133885 GDB-9 molecules

description: Data for 133885 GDB-9 molecules<br>In an earlier version of this dataset the titles of the files "Data for 133885 GDB-9 molecules" and "Data for 6095 constitutional isomers of C7H10O2" were swapped. This has now been corrected.<br>
DOI: 10.5281/zenodo.1009659
title: Countwordserror

description: Results from tool executed using PubRunner on MEDLINE corpus. Code for the tool can be found at https://github.com/NCBI-Hackathons/PubRunner/tree/master/server/tools/CountWordsError/0.1
DOI: 10.22021/bbldntz201802
title: The Linked Open British National Bibliography – Books. 1950/2018-02 – N-Triples
DOI: 10.22021/bbldrdfz201802
title: The Linked Open British National Bibliography – Books. 1950/2018-02 – RDF/XML
DOI: 10.22021/bcldntz201802
title: The Linked Open British National Bibliography – Forthcoming Books. 2018-02 – N-Triples
DOI: 10.22021/bcldrdfz201802
title: The Linked Open British National Bibliography – Forthcoming Books. 2018-02 – RDF/XML
DOI: 10.22021/bsldntz201802
title: The Linked Open British National Bibliography – Serials. 1950/2018-02 – N-Triples
DOI: 10.22021/bsldrdfz201802
title: The Linked Open British National Bibliography – Serials. 1950/2018-02 – RDF/XML
DOI: 10.5281/zenodo.344629
title: Moma Collection - Automatic Monthly Update

description: Temporary Description
DOI: 10.12751/g-node.fb5bd5
title: Suppression of Itch in Mice and Dogs by Selective Modulation of Spinal α2/α3 GABAA Receptors

description: Excel files of the morphological, electrophysiological and behavioral data sets included in Ralvenius et al (2018) Suppression of Itch in Mice and Dogs by Selective Modulation of Spinal α2/α3 GABAA Receptors.
DOI: 10.5281/zenodo.1010481
title: Bio2Vec

description: Results from tool executed using PubRunner on MEDLINE corpus. Code for the tool can be found at https://github.com/NCBI-Hackathons/PubRunner/tree/master/server/tools/Bio2Vec/1.0
DOI: 10.5281/zenodo.1010482
title: Bio2Vec

description: Results from tool executed using PubRunner on MEDLINE corpus. Code for the tool can be found at https://github.com/NCBI-Hackathons/PubRunner/tree/master/server/tools/Bio2Vec/1.0
DOI: 10.5281/zenodo.1202174
title: Listing Of Data Repositories That Embed Schema.Org Metadata In Dataset Landing Pages

description: Machine-readable metadata available from landing pages for datasets facilitate data citation by enabling easy integration with reference managers and other tools used in a data citation workflow. Embedding these metadata using the schema.org standard with the JSON-LD is emerging as the community standard. This dataset is a listing of data repositories that have implemented this approach or are in the progress of doing so.

This is the first version of this dataset and was generated via community consultation. We expect to update this dataset, as an increasing number of data repositories adopt this approach, and we hope to see this information added to registries of data repositories such as re3data and FAIRsharing.

In addition to the listing of data repositories we provide information of the schema.org properties supported by these data repositories, focussing on the required and recommended properties from the "Data Citation Roadmap for Scholarly Data Repositories".
DOI: 10.5281/zenodo.1202178
title: Listing Of Data Repositories That Embed Schema.Org Metadata In Dataset Landing Pages

description: Machine-readable metadata available from landing pages for datasets facilitate data citation by enabling easy integration with reference managers and other tools used in a data citation workflow. Embedding these metadata using the schema.org standard with the JSON-LD is emerging as the community standard. This dataset is a listing of data repositories that have implemented this approach or are in the progress of doing so.

This is the first version of this dataset and was generated via community consultation. We expect to update this dataset, as an increasing number of data repositories adopt this approach, and we hope to see this information added to registries of data repositories such as re3data and FAIRsharing.

In addition to the listing of data repositories we provide information of the schema.org properties supported by these data repositories, focussing on the required and recommended properties from the "Data Citation Roadmap for Scholarly Data Repositories".
DOI: 10.22021/bbldntz201806
title: The Linked Open British National Bibliography – Books. 1950/2018-06 – N-Triples
DOI: 10.22021/bbldrdfz201806
title: The Linked Open British National Bibliography – Books. 1950/2018-06 – RDF/XML
DOI: 10.22021/bcldntz201806
title: The Linked Open British National Bibliography – Forthcoming Books. 2018-06 – N-Triples
DOI: 10.22021/bcldrdfz201806
title: The Linked Open British National Bibliography – Forthcoming Books. 2018-06 – RDF/XML
DOI: 10.22021/bsldntz201806
title: The Linked Open British National Bibliography – Serials. 1950/2018-06 – N-Triples
DOI: 10.22021/bsldrdfz201806
title: The Linked Open British National Bibliography – Serials. 1950/2018-06 – RDF/XML
DOI: 10.25678/000022
title: Data for: Pesticide Risks in Small Streams - How to Get as Close as Possible to the Stress Imposed on Aquatic Organisms

description: The dataset contains concentrations profiles of 213 agricultural pesticides in surface water samples. Half-day composite samples were taken from five small streams in Switzerland from the beginning of March to the end of August 2015. Half-day samples from discharge events were measured individually, whereas half-day samples taken during dry weather periods between discharge events were pooled to samples of variable lengths (five days on average) and then measured resulting in 34 to 60 measured samples per site.
DOI: 10.5281/zenodo.819538
title: Geo-Tagged Tweets In Paris During Nov 2015

description: <strong>Abstract</strong>

The data sets released here has been used in our study on quantitatively evaluating the impact of disasters in the city. The study of disaster events and their impact in the urban space has been traditionally conducted through manual collections and analysis of surveys, questionnaires and authority documents. While there have been increasingly rich troves of human behavioral data related to the events of interest, the ability to obtain hindsight following a disaster event has not been scaled up. In this study, we propose a novel approach for analyzing events called PairFac. PairFac utilizes discriminant tensor analysis to automatically discover the impact of a major event from rich human behavioral data. Our method aims to (i) uncover the persistent patterns across multiple interrelated aspects of urban behavior (e.g., when, where and what citizens do in a city) and at the same time (ii) identify the salient changes following a potentially impactful event. We show the effectiveness of PairFac in comparison with previous methods through extensive experiments. We also demonstrate the advantages of our approach through case studies with real-world traffic sensor data and social media streams surrounding the 2015 terrorist attacks in Paris. Our work has both methodological contributions in studying the impact of an external stimulus on a system as well as practical implications in the area of disaster event analysis and assessment.

<strong>Dataset</strong>

There are two datasets used in this study, traffic sensor dataset and social media dataset.   

Traffic Sensor dataset was collected from open data Paris. This dataset includes all the hourly data for the flow and the occupancy rate assembled by the permanent traffic sensors installed on the Paris City network (urban network and peripheral boulevard). For interested readers, please direct to the link as: https://opendata.paris.fr/explore/dataset/comptages-routiers-permanents/

Social media dataset was collected from Twitter API. The dataset contains geo-tagged tweets from Paris collected through Twitter API between the period of Oct 16th, 2015 and Nov 20, 2015. 75,982 geo-located tweets were extracted during the period covered.

Duration: 2015-10-16 to 2015-11-20.

Total number of tweets: 75,982

<strong>Publication</strong>

If you make use of this data set, please kindly cite:

Xidao Wen, Yu-Ru Lin, and Konstantinos Pelechrinis. 2016. PairFac: Event Analytics through Discriminant Tensor Factorization. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM '16). ACM, New York, NY, USA, 519-528. DOI: https://doi.org/10.1145/2983323.2983837
DOI: 10.12751/g-node.984192
title: Usage learning in bats

description: The usage_learning zip file contains the raw recording data presented in this manuscript and the analysis file to detect calls in these recordings and extract their acoustic call parameters. The output of the analysis file are the statistical results and plot of the data for the selected individual. The private folder and the yin.m file are supplementary files necessary for the analysis script to work.
DOI: 10.5281/zenodo.819905
title: Geo-Tagged Tweets In Paris During Nov 2015

description: <strong>Abstract</strong>

The data sets released here have been used in our study on quantitatively evaluating the impact of disasters in the city. The study of disaster events and their impact in the urban space has been traditionally conducted through manual collections and analysis of surveys, questionnaires and authority documents. While there have been increasingly rich troves of human behavioral data related to the events of interest, the ability to obtain hindsight following a disaster event has not been scaled up. In this study, we propose a novel approach for analyzing events called PairFac. PairFac utilizes discriminant tensor analysis to automatically discover the impact of a major event from rich human behavioral data. Our method aims to (i) uncover the persistent patterns across multiple interrelated aspects of urban behavior (e.g., when, where and what citizens do in a city) and at the same time (ii) identify the salient changes following a potentially impactful event. We show the effectiveness of PairFac in comparison with previous methods through extensive experiments. We also demonstrate the advantages of our approach through case studies with real-world traffic sensor data and social media streams surrounding the 2015 terrorist attacks in Paris. Our work has both methodological contributions in studying the impact of an external stimulus on a system as well as practical implications in the area of disaster event analysis and assessment.

<strong>Dataset</strong>

There are two datasets used in this study, traffic sensor dataset and social media dataset.   

Traffic Sensor dataset was collected from open data Paris. This dataset includes all the hourly data for the flow and the occupancy rate assembled by the permanent traffic sensors installed on the Paris City network (urban network and peripheral boulevard). For interested readers, please direct to the link as: https://opendata.paris.fr/explore/dataset/comptages-routiers-permanents/

Social media dataset was collected from Twitter API. The dataset contains geo-tagged tweets from Paris collected through Twitter API between the period of Oct 16th, 2015 and Nov 20, 2015. 75,982 geo-located tweets were extracted during the period covered.

Duration: 2015-10-16 to 2015-11-20.

Total number of tweets: 75,982

<strong>Publication</strong>

If you make use of this data set, please kindly cite:

Xidao Wen, Yu-Ru Lin, and Konstantinos Pelechrinis. 2016. PairFac: Event Analytics through Discriminant Tensor Factorization. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM '16). ACM, New York, NY, USA, 519-528. DOI: https://doi.org/10.1145/2983323.2983837
DOI: 10.22021/bbldntz201609
title: The Linked Open British National Bibliography – Books. 1950/2016-09 – N-Triples
DOI: 10.22021/lodbnb1
title: The Linked Open British National Bibliography – Books
DOI: 10.22021/lodbnb2
title: The Linked Open British National Bibliography – Serials
DOI: 10.5281/zenodo.1013932
title: Annex Dataset

description: Results of Chameleon on Idgraf using (dmda,dmdas,dmdar,lws,ws) schedulers

 Chameleon @ idgraf. Compilation and first execution.
DOI: 10.5281/zenodo.1013933
title: Annex Dataset

description: Results of Chameleon on Idgraf using (dmda,dmdas,dmdar,lws,ws) schedulers

 Chameleon @ idgraf. Compilation and first execution.
DOI: 10.5281/zenodo.1013934
title: Annex Dataset

description: Results of Chameleon on Orion3 using (dmda,dmdas,dmdar,lws,ws) schedulers

 Chameleon @ orion3. Compilation and first execution.
DOI: 10.5281/zenodo.1013935
title: Annex Dataset

description: Results of Chameleon on Orion3 using (dmda,dmdas,dmdar,lws,ws) schedulers

 Chameleon @ orion3. Compilation and first execution.
DOI: 10.5281/zenodo.1013937
title: Annex Dataset

description: expHype5dmdasRev17324: results
DOI: 10.5281/zenodo.1013938
title: Annex Dataset

description: expHype5dmdasRev17324: results
DOI: 10.5281/zenodo.1314269
title: 123456789.Txt

description: 123456789.txt
DOI: 10.5281/zenodo.1314270
title: 123456789.Txt

description: 123456789.txt
DOI: 10.13012/b2idb-2237685_v1
title: Bibliotelemetry data

description: Bibliotelemetry data are provided in support of the evaluation of Internet of Things (IoT) middleware within library collections. IoT infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location-based recommendations. A modular evaluation and analysis herein clarified the nature of users’ requests for recommendations based on their location, and describes subject areas of the library for which users request recommendations. The modular mobile design allowed for deep exploration of bibliographic identifiers as they appeared throughout the global module system, serving to provide context to the searching and browsing data that are the focus of this study.
DOI: 10.22021/bbldrdfz201609
title: The Linked Open British National Bibliography – Books. 1950/2016-09 – RDF/XML
DOI: 10.22021/bsldntz201609
title: The Linked Open British National Bibliography – Serials. 1950/2016-09 – N-Triples
DOI: 10.22021/bsldrdfz201609
title: The Linked Open British National Bibliography – Serials. 1950/2016-09 – RDF/XML
DOI: 10.5281/zenodo.1013950
title: Annex Dataset

description: expHype5dmdasRev17324: results
DOI: 10.5281/zenodo.1013951
title: Annex Dataset

description: expHype5dmdasRev17324: results
DOI: 10.5281/zenodo.1014027
title: Floodx Preprocessed Data (All Experiments, Mixed Quality)

description: This package contains a preprocessed subset of data for the floodX flooding experiments, for which the data quality is <strong>not guaranteed</strong>.
DOI: 10.5281/zenodo.1014028
title: Floodx Preprocessed Data (All Experiments, Mixed Quality)

description: This package contains a preprocessed subset of data for the floodX flooding experiments, for which the data quality is <strong>not guaranteed</strong>.
DOI: 10.5281/zenodo.1314387
title: Test33

description: Test33
DOI: 10.5281/zenodo.1314388
title: Test33

description: Test33
DOI: 10.5281/zenodo.1314393
title: Test222

description: 1212
DOI: 10.5281/zenodo.1314394
title: Test222

description: 1212
DOI: 10.5281/zenodo.1314428
title: Testmax2

description: TestMax2
DOI: 10.5281/zenodo.1314429
title: Testmax2

description: TestMax2
DOI: 10.23645/epacomptox.6062536.v1
title: Standard Laboratory Protocol for Tox21 Assays

description: Data describing the standard laboratory protocol for Tox21 assays including descriptions of Tox21 assays; protocol for all assays (reference; quality control; procedures and performance) and assay data. �
DOI: 10.23645/epacomptox.6062539
title: OECD GD 211 ToxCast Endocrine-Related Assay Documentation

description: Descriptions and guidelines for ToxCast endocrine-related assays in format outlined by the OECD Guidance Document 211 for �describing non-guideline in vitro test methods. The intent of GD 211 is to harmonize non-guideline; in vitro method descriptions to allow assessment of the relevance of the test method for biological responses of interest and the quality of the data produced.�
DOI: 10.23645/epacomptox.6062539.v1
title: OECD GD 211 ToxCast Endocrine-Related Assay Documentation

description: Descriptions and guidelines for ToxCast endocrine-related assays in format outlined by the OECD Guidance Document 211 for �describing non-guideline in vitro test methods. The intent of GD 211 is to harmonize non-guideline; in vitro method descriptions to allow assessment of the relevance of the test method for biological responses of interest and the quality of the data produced.�
DOI: 10.5281/zenodo.1015016
title: Annex Dataset

description: expIdcin2trsmsyrkGPU results with 60*60 matrices

expIdcin2trsmsyrkGPU results

expIdcin2trsmsyrkGPU: compilation and first execution
DOI: 10.5281/zenodo.1015017
title: Annex Dataset

description: expIdcin2trsmsyrkGPU results with 60*60 matrices

expIdcin2trsmsyrkGPU results

expIdcin2trsmsyrkGPU: compilation and first execution
DOI: 10.5281/zenodo.1015267
title: Annex Dataset

description: expOrion3CpuRep results
DOI: 10.5281/zenodo.1015268
title: Annex Dataset

description: expOrion3CpuRep results
DOI: 10.5281/zenodo.1316940
title: 111111

description: 11111111
DOI: 10.23645/epacomptox.6062542
title: High-throughput screening data for estrogen receptor model

description: Estrogen receptor model data from the manuscript titled�Integrated Model of Chemical Perturbations of Biological Pathways Using 18 In Vitro High-throughput Screening Assays for the Estrogen Receptor�(Judson et al) published in Toxicological Science.�
DOI: 10.23645/epacomptox.6062548
title: ReadMe for Animal Toxicity Study ToxRefDB files

description: ReadMe file for the Animal Toxicity Study ToxRefDB files
DOI: 10.23645/epacomptox.6062548.v1
title: ReadMe for Animal Toxicity Study ToxRefDB files

description: ReadMe file for the Animal Toxicity Study ToxRefDB files
DOI: 10.5281/zenodo.1015425
title: Annex Dataset

description: expIdcin2-604020 experiment results with size 12*960

expIdcin2-604020 experiment results

expIdcin2-604020 compilation and first execution
DOI: 10.5281/zenodo.1015426
title: Annex Dataset

description: expIdcin2-604020 experiment results with size 12*960

expIdcin2-604020 experiment results

expIdcin2-604020 compilation and first execution
DOI: 10.5281/zenodo.1015720
title: Annex Dataset

description: expIdcin2GpuCpuRep results
DOI: 10.5281/zenodo.1015721
title: Annex Dataset

description: expIdcin2GpuCpuRep results
DOI: 10.5281/zenodo.1015888
title: Annex Dataset

description: Moving data to trace-task-visu/data
DOI: 10.5281/zenodo.1015889
title: Annex Dataset

description: Moving data to trace-task-visu/data
DOI: 10.5281/zenodo.1016330
title: Annex Dataset

description: Experiments with 8CPUs and 2 GPUs on bali1 testing different schedulers
DOI: 10.5281/zenodo.1016331
title: Annex Dataset

description: Experiments with 8CPUs and 2 GPUs on bali1 testing different schedulers
DOI: 10.5281/zenodo.1016343
title: Annex Dataset

description: Renaning data dir
DOI: 10.5281/zenodo.1316939
title: 111111

description: 11111111
DOI: 10.5281/zenodo.1016342
title: Annex Dataset

description: Renaning data dir
DOI: 10.5281/zenodo.1016348
title: Annex Dataset

description: Idgraf machine: results using different schedulers (gpu only executions)
DOI: 10.5281/zenodo.1016349
title: Annex Dataset

description: Idgraf machine: results using different schedulers (gpu only executions)
DOI: 10.5281/zenodo.1016360
title: Annex Dataset

description: Executions of StarPU cholesky on Orion3 with nblocks = 60 (for comparison with chameleon)
DOI: 10.5281/zenodo.1016361
title: Annex Dataset

description: Executions of StarPU cholesky on Orion3 with nblocks = 60 (for comparison with chameleon)
DOI: 10.5281/zenodo.1016370
title: Annex Dataset

description: Experiments on orion3 using 2 gpus and 8 cpus (schedulers dmda, ws, lws, dmdas, dmdar)
DOI: 10.5281/zenodo.1016371
title: Annex Dataset

description: Experiments on orion3 using 2 gpus and 8 cpus (schedulers dmda, ws, lws, dmdas, dmdar)
DOI: 10.5281/zenodo.1016403
title: Annex Dataset

description: Merge branch 'data' of https://scm.gforge.inria.fr/authscm/vgpinto/git/perf-ev-runtime/perf-ev-runtime into data

expChameleonSimuIdcin2 - simu - results 12*960 with hash data from installed packages

chameleon-simu: reorganizing results dir

expChameleonSimuIdcin2 - simu - results 12*960

expChameleonSimuIdcin2 - simu - compilation

expChameleonSimuIdcin2 - native - results 12*960

expChameleonSimuIdcin2 - native - compilation
DOI: 10.5281/zenodo.1016404
title: Annex Dataset

description: Merge branch 'data' of https://scm.gforge.inria.fr/authscm/vgpinto/git/perf-ev-runtime/perf-ev-runtime into data

expChameleonSimuIdcin2 - simu - results 12*960 with hash data from installed packages

chameleon-simu: reorganizing results dir

expChameleonSimuIdcin2 - simu - results 12*960

expChameleonSimuIdcin2 - simu - compilation

expChameleonSimuIdcin2 - native - results 12*960

expChameleonSimuIdcin2 - native - compilation
DOI: 10.5281/zenodo.1016437
title: Annex Dataset

description: expSimuVerc tests with interval - results and comments

expSimuVerc: results starpu+simgrid
DOI: 10.5281/zenodo.1016438
title: Annex Dataset

description: expSimuVerc tests with interval - results and comments

expSimuVerc: results starpu+simgrid
DOI: 10.5281/zenodo.1320182
title: Zenodo T01

description: test
DOI: 10.5281/zenodo.1320183
title: Zenodo T01

description: test
DOI: 10.23645/epacomptox.6062617.v1
title: Tipping Point .csv

description: EPA researchers develop mathematical models to predict perturbation of biological systems and determine when cellular systems are no longer able to recover.� EPA researchers use these models to determine the �Tipping Point�; the point when biological systems are unable to recover from or adapt to chemical exposure. When cellular systems are unable to recover chemical exposures could lead to adverse outcomes such as cancer.�