cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7934681.v1
title: gVCF_NA06985

description: 1000 Genomes gVCF mapped to hs37d5 for NA06985. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934708.v1
title: gVCF_HG02645

description: 1000 Genomes gVCF mapped to hs37d5 for HG02645. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934612
title: gVCF_NA19917

description: 1000 Genomes gVCF mapped to hs37d5 for NA19917. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934621
title: gVCF_NA06984

description: 1000 Genomes gVCF mapped to hs37d5 for NA06984. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934621.v1
title: gVCF_NA06984

description: 1000 Genomes gVCF mapped to hs37d5 for NA06984. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934645
title: gVCF_HG02643

description: 1000 Genomes gVCF mapped to hs37d5 for HG02643. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934675.v1
title: gVCF_NA19920

description: 1000 Genomes gVCF mapped to hs37d5 for NA19920. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934612.v1
title: gVCF_NA19917

description: 1000 Genomes gVCF mapped to hs37d5 for NA19917. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934579
title: gVCF_HG04239

description: 1000 Genomes gVCF mapped to hs37d5 for HG04239. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934582
title: gVCF_HG02642

description: 1000 Genomes gVCF mapped to hs37d5 for HG02642. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934579.v1
title: gVCF_HG04239

description: 1000 Genomes gVCF mapped to hs37d5 for HG04239. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934582.v1
title: gVCF_HG02642

description: 1000 Genomes gVCF mapped to hs37d5 for HG02642. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934549
title: gVCF_NA19916

description: 1000 Genomes gVCF mapped to hs37d5 for NA19916. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934495
title: CCDB_3660.jpg

description: Tomographic reconstruction of caulobacter
DOI: 10.6084/m9.figshare.7934522
title: gVCF_HG04238

description: 1000 Genomes gVCF mapped to hs37d5 for HG04238. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934471
title: gVCF_HG04235

description: 1000 Genomes gVCF mapped to hs37d5 for HG04235. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934495.v1
title: CCDB_3660.jpg

description: Tomographic reconstruction of caulobacter
DOI: 10.6084/m9.figshare.7934513
title: gVCF_HG02635

description: 1000 Genomes gVCF mapped to hs37d5 for HG02635. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934525
title: Detection of location and dispersion effects from partially replicated two-level factorial designs

description: During the preliminary stage of a quality improvement process, identification of active location and dispersion effects is an important issue. After understanding the impacts of different factorial effects on the system response, a quality engineer can improve the system performance by adjusting the levels of identified factors. Based on the concept of generalized inference, a new testing procedure is proposed in this article; it can be used to identify active location effects from partially replicated two-level factorial designs. Moreover, a two-stage procedure is introduced for integrating the analyses of location and dispersion effects. Two real-world data sets are analyzed for illustrating our method. Based on the simulation results, it is further shown that the proposed method can maintain the empirical size sufficiently close to the nominal level and have satisfactory power. In addition, a catalog of partially replicated designs with a repeated quarter fraction is generated for practical applications.
DOI: 10.6084/m9.figshare.7934441
title: gVCF_NA19914

description: 1000 Genomes gVCF mapped to hs37d5 for NA19914. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934471.v1
title: gVCF_HG04235

description: 1000 Genomes gVCF mapped to hs37d5 for HG04235. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934522.v1
title: gVCF_HG04238

description: 1000 Genomes gVCF mapped to hs37d5 for HG04238. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934525.v1
title: Detection of location and dispersion effects from partially replicated two-level factorial designs

description: During the preliminary stage of a quality improvement process, identification of active location and dispersion effects is an important issue. After understanding the impacts of different factorial effects on the system response, a quality engineer can improve the system performance by adjusting the levels of identified factors. Based on the concept of generalized inference, a new testing procedure is proposed in this article; it can be used to identify active location effects from partially replicated two-level factorial designs. Moreover, a two-stage procedure is introduced for integrating the analyses of location and dispersion effects. Two real-world data sets are analyzed for illustrating our method. Based on the simulation results, it is further shown that the proposed method can maintain the empirical size sufficiently close to the nominal level and have satisfactory power. In addition, a catalog of partially replicated designs with a repeated quarter fraction is generated for practical applications.
DOI: 10.6084/m9.figshare.7934420
title: gVCF_HG04229

description: 1000 Genomes gVCF mapped to hs37d5 for HG04229. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934432
title: gVCF_HG02634

description: 1000 Genomes gVCF mapped to hs37d5 for HG02634. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934513.v1
title: gVCF_HG02635

description: 1000 Genomes gVCF mapped to hs37d5 for HG02635. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934402
title: gVCF_NA19913

description: 1000 Genomes gVCF mapped to hs37d5 for NA19913. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934420.v1
title: gVCF_HG04229

description: 1000 Genomes gVCF mapped to hs37d5 for HG04229. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934432.v1
title: gVCF_HG02634

description: 1000 Genomes gVCF mapped to hs37d5 for HG02634. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934441.v1
title: gVCF_NA19914

description: 1000 Genomes gVCF mapped to hs37d5 for NA19914. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934381
title: gVCF_HG02629

description: 1000 Genomes gVCF mapped to hs37d5 for HG02629. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934372
title: gVCF_HG04227

description: 1000 Genomes gVCF mapped to hs37d5 for HG04227. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934381.v1
title: gVCF_HG02629

description: 1000 Genomes gVCF mapped to hs37d5 for HG02629. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934402.v1
title: gVCF_NA19913

description: 1000 Genomes gVCF mapped to hs37d5 for NA19913. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934360
title: gVCF_HG02628

description: 1000 Genomes gVCF mapped to hs37d5 for HG02628. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934363
title: gVCF_NA19909

description: 1000 Genomes gVCF mapped to hs37d5 for NA19909. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934315
title: gVCF_NA19908

description: 1000 Genomes gVCF mapped to hs37d5 for NA19908. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934354
title: gVCF_HG04225

description: 1000 Genomes gVCF mapped to hs37d5 for HG04225. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934363.v1
title: gVCF_NA19909

description: 1000 Genomes gVCF mapped to hs37d5 for NA19909. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934372.v1
title: gVCF_HG04227

description: 1000 Genomes gVCF mapped to hs37d5 for HG04227. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934300
title: gVCF_HG04222

description: 1000 Genomes gVCF mapped to hs37d5 for HG04222. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934315.v1
title: gVCF_NA19908

description: 1000 Genomes gVCF mapped to hs37d5 for NA19908. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934354.v1
title: gVCF_HG04225

description: 1000 Genomes gVCF mapped to hs37d5 for HG04225. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934360.v1
title: gVCF_HG02628

description: 1000 Genomes gVCF mapped to hs37d5 for HG02628. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934258
title: gVCF_HG04219

description: 1000 Genomes gVCF mapped to hs37d5 for HG04219. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934228
title: Dissociating the neural correlates of the sociality and plausibility effects in simple conceptual combination

description: Task fMRI results (Whole brain) and ROI data.
DOI: 10.6084/m9.figshare.7934282
title: gVCF_HG02623

description: 1000 Genomes gVCF mapped to hs37d5 for HG02623. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934300.v1
title: gVCF_HG04222

description: 1000 Genomes gVCF mapped to hs37d5 for HG04222. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934198
title: gVCF_NA19904

description: 1000 Genomes gVCF mapped to hs37d5 for NA19904. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934258.v1
title: gVCF_HG04219

description: 1000 Genomes gVCF mapped to hs37d5 for HG04219. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934165
title: gVCF_NA19901

description: 1000 Genomes gVCF mapped to hs37d5 for NA19901. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934171
title: gVCF_HG04216

description: 1000 Genomes gVCF mapped to hs37d5 for HG04216. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934198.v1
title: gVCF_NA19904

description: 1000 Genomes gVCF mapped to hs37d5 for NA19904. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934168
title: gVCF_HG02621

description: 1000 Genomes gVCF mapped to hs37d5 for HG02621. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934153
title: gVCF_HG04214

description: 1000 Genomes gVCF mapped to hs37d5 for HG04214. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934165.v1
title: gVCF_NA19901

description: 1000 Genomes gVCF mapped to hs37d5 for NA19901. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7933811
title: gVCF_HG04212

description: 1000 Genomes gVCF mapped to hs37d5 for HG04212. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7933970
title: gVCF_NA19900

description: 1000 Genomes gVCF mapped to hs37d5 for NA19900. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934150
title: gVCF_HG02614

description: 1000 Genomes gVCF mapped to hs37d5 for HG02614. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934153.v1
title: gVCF_HG04214

description: 1000 Genomes gVCF mapped to hs37d5 for HG04214. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934168.v1
title: gVCF_HG02621

description: 1000 Genomes gVCF mapped to hs37d5 for HG02621. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932581
title: gVCF_NA19835

description: 1000 Genomes gVCF mapped to hs37d5 for NA19835. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932614
title: gVCF_HG04211

description: 1000 Genomes gVCF mapped to hs37d5 for HG04211. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7933418
title: gVCF_HG02613

description: 1000 Genomes gVCF mapped to hs37d5 for HG02613. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7933811.v1
title: gVCF_HG04212

description: 1000 Genomes gVCF mapped to hs37d5 for HG04212. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7933970.v1
title: gVCF_NA19900

description: 1000 Genomes gVCF mapped to hs37d5 for NA19900. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7934150.v1
title: gVCF_HG02614

description: 1000 Genomes gVCF mapped to hs37d5 for HG02614. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932521
title: gVCF_HG02611

description: 1000 Genomes gVCF mapped to hs37d5 for HG02611. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932527
title: Untitled Item

description: cell line authentication data
DOI: 10.6084/m9.figshare.7932581.v1
title: gVCF_NA19835

description: 1000 Genomes gVCF mapped to hs37d5 for NA19835. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7933418.v1
title: gVCF_HG02613

description: 1000 Genomes gVCF mapped to hs37d5 for HG02613. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932515
title: gVCF_HG04210

description: 1000 Genomes gVCF mapped to hs37d5 for HG04210. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932521.v1
title: gVCF_HG02611

description: 1000 Genomes gVCF mapped to hs37d5 for HG02611. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932614.v1
title: gVCF_HG04211

description: 1000 Genomes gVCF mapped to hs37d5 for HG04211. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932527.v1
title: Untitled Item

description: cell line authentication data
DOI: 10.6084/m9.figshare.7932515.v1
title: gVCF_HG04210

description: 1000 Genomes gVCF mapped to hs37d5 for HG04210. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932488
title: gVCF_NA19834

description: 1000 Genomes gVCF mapped to hs37d5 for NA19834. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932485
title: Insecticide residue database for Africa

description: An insecticide residue database was compiled for Africa from a literature review in Web of Knowledge to identify studies that measured insecticide residues in soil, sediment, water and air. The search terms that were used for the extraction of the literature are available as additional file in the manuscript. The database extracted the following data systematically from the individual papers: year and month(s) of sampling, sample collection methods and depth, insecticide extraction method, insecticide quantification method, quantification and detection limits, insecticide and insecticide class, the measured insecticide concentration and geographical coordinates.
DOI: 10.6084/m9.figshare.7932488.v1
title: gVCF_NA19834

description: 1000 Genomes gVCF mapped to hs37d5 for NA19834. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932509.v1
title: Projection pursuit based on Gaussian mixtures and evolutionary algorithms

description: We propose a projection pursuit (PP) algorithm based on Gaussian mixture models (GMMs). The negentropy obtained from a multivariate density estimated by GMMs is adopted as the PP index to be maximised. For a fixed dimension of the projection subspace, the GMM-based density estimation is projected onto that subspace, where an approximation of the negentropy for Gaussian mixtures is computed. Then, Genetic Algorithms (GAs) are used to find the optimal, orthogonal projection basis by maximising the former approximation. We show that this semi-parametric approach to PP is flexible and allows highly informative structures to be detected, by projecting multivariate datasets onto a subspace, where the data can be feasibly visualised. The performance of the proposed approach is shown on both artificial and real datasets.
DOI: 10.6084/m9.figshare.7932467
title: gVCF_HG02610

description: 1000 Genomes gVCF mapped to hs37d5 for HG02610. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932467.v1
title: gVCF_HG02610

description: 1000 Genomes gVCF mapped to hs37d5 for HG02610. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932470
title: gVCF_HG04209

description: 1000 Genomes gVCF mapped to hs37d5 for HG04209. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932413
title: gVCF_HG02604

description: 1000 Genomes gVCF mapped to hs37d5 for HG02604. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932416
title: gVCF_NA19819

description: 1000 Genomes gVCF mapped to hs37d5 for NA19819. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932422
title: Insight4news Irish news related hashtagged tweet collection 15.07.2015-24.05.2017

description: <pre>The 1.3GB .tar.gz file contains a 3.6GB (uncompressed) .txt file with <b>198'725'860</b> rows, each row of which is a tweet ID.</pre><pre>These tweets have been collected in <i>15.07.2015-24.05.2017</i> period with the <i>Hashtagger</i> platform (presented in https://doi.org/10.1145/2872427.2882982 by Shi et al.), which considered these tweets relevant to the monitored stream of news from Irish sources (The Irish Times, Irish Examiner, etc.). </pre><pre><br></pre><pre>All 198'725'860 tweets are in English (with 'en' in the 'lang' field of the json objects, privided by GNIP) and contain at least one hashtag. <br></pre><pre><br></pre><pre>Hydrate the tweet ids with Twarc (https://github.com/edsu/twarc) and write to a file. You will need to provide Twarc with a set of Twitter API keys.</pre><pre><i> twarc.py --hydrate tweet_ids.txt &gt; tweets.json</i><br></pre><pre></pre><pre>It is probably not a good idea to hydrate all the tweets in one go, and may be better to split the file into chunks and hydrate the tweets chunk-by-chunk. </pre><pre><br></pre><pre><br></pre><pre>When using the dataset, please cite the following paper, for which this dataset was generated for. <b><br></b></pre><pre><b>SocialTree: Socially Augmented Structured Summaries of News Stories</b></pre><pre><i>Gevorg Poghosyan</i>, <i>Georgiana Ifrim</i> </pre><pre>Proceedings of 30th ACM Conference on Hypertext & Social Media (HT ’19), 2019</pre><pre>https://doi.org/10.1145/3342220.3343668 </pre>
DOI: 10.6084/m9.figshare.7932416.v1
title: gVCF_NA19819

description: 1000 Genomes gVCF mapped to hs37d5 for NA19819. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932485.v2
title: Insecticide residue database for Africa

description: An insecticide residue database was compiled for Africa from a literature review in Web of Knowledge to identify studies that measured insecticide residues in soil, sediment, water and air. The search terms that were used for the extraction of the literature are available as additional file in the manuscript. The database extracted the following data systematically from the individual papers: year and month(s) of sampling, sample collection methods and depth, insecticide extraction method, insecticide quantification method, quantification and detection limits, insecticide and insecticide class, the measured insecticide concentration and geographical coordinates.
DOI: 10.6084/m9.figshare.7932413.v1
title: gVCF_HG02604

description: 1000 Genomes gVCF mapped to hs37d5 for HG02604. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932392
title: gVCF_HG04206

description: 1000 Genomes gVCF mapped to hs37d5 for HG04206. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932422.v1
title: Insight4news Irish news related tweet collection 15.07.2015-24.05.2017

description: <pre>The 1.3GB tar.gz file contains a 3.6GB (uncompressed) .txt file with 198'725'860 rows, each row of which is a tweet ID. These tweets have been collected in 15.07.2015-24.05.2017 period with the Hashtagger platform, which considered these tweets relevant to the monitored stream of news from Irish sources (The Irish Times, Irish Examiner, etc.). All 198'725'860 tweets contain at least one hashtag. Hydrate the tweet ids with Twarc and write to a file. You will need to provide Twarc with a set of Twitter API keys. For more information, see [Twarc's documentation](https://github.com/edsu/twarc#twitter-api-keys). twarc.py --hydrate tweet_ids.txt &gt; tweets.json It is probably not a good idea to hydrate all the tweets in one go, and may be better to split the file into chuncks and hydrate the tweets chunk-by-chunk. </pre>
DOI: 10.6084/m9.figshare.7932392.v1
title: gVCF_HG04206

description: 1000 Genomes gVCF mapped to hs37d5 for HG04206. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7932422.v2
title: Insight4news Irish news related hashtagged tweet collection 15.07.2015-24.05.2017

description: <pre>The 1.3GB tar.gz file contains a 3.6GB (uncompressed) .txt file with 198'725'860 rows, each row of which is a tweet ID. These tweets have been collected in 15.07.2015-24.05.2017 period with the Hashtagger platform, which considered these tweets relevant to the monitored stream of news from Irish sources (The Irish Times, Irish Examiner, etc.). All 198'725'860 tweets contain at least one hashtag. <br></pre><pre><br></pre><pre>Hydrate the tweet ids with Twarc and write to a file. You will need to provide Twarc with a set of Twitter API keys. For more information, see [Twarc's documentation](https://github.com/edsu/twarc#twitter-api-keys). twarc.py --hydrate tweet_ids.txt &gt; tweets.json It is probably not a good idea to hydrate all the tweets in one go, and may be better to split the file into chuncks and hydrate the tweets chunk-by-chunk. </pre>