cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/tmrs92j7pv.12
title: COVID-19: Time Series Datasets India versus World

description: This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : nicresearchgroup@gmail.com) for more details. . [Dataset is updated Thrice a Week]
DOI: 10.5281/zenodo.3886395
title: PRJNA638224 - BCR repertoire sequencing from COVID-19 patients

description: <strong>Description</strong> These are the processed BCR repertoire sequence data that accompany the following manuscript: “Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures”. The manuscript preprint is available at doi: https://doi.org/10.1101/2020.05.20.106294. The raw sequence data are available on SRA under the BioProject PRJNA638224 <strong>Sequence processing</strong> The Immcantation framework (docker container v3.0.0) was used for sequence processing. Briefly, paired-end reads were joined based on a minimum overlap of 20 nt, and a max error of 0.2, and reads with a mean phred score below 20 were removed. Primer regions, including UMIs and sample barcodes, were then identified within each read, and trimmed. Together, the sample barcode, UMI, and constant region primer were used to assign molecular groupings for each read. Within each grouping, usearch, was used to subdivide the grouping, with a cutoff of 80% nucleotide identity, to account for randomly overlapping UMIs. Each of the resulting groupings is assumed to represent reads arising from a single RNA. Reads within each grouping were then aligned, and a consensus sequence determined. For each processed sequence, IgBlast was used to determine V, D and J gene segments, and locations of the CDRs and FWRs. Isotype was determined based on comparison to germline constant region sequences. Sequences annotated as unproductive by IgBlast were removed. <strong>Sequence data column description</strong> <strong>sample_id </strong>Unique identifier for each sequencing library <strong>sequence_id </strong>Unique identifier for a sequence within a sample_id <strong>sequence_alignment </strong>IMGT gapped nucleotide sequence <strong>germline_alignment </strong>IMGT gapped germline sequence <strong>v_call </strong>IGHV gene segment(s) and allele <strong>d_call </strong>IGHD gene segment(s) and allele <strong>j_call </strong>IGHJ gene segment(s) and allele <strong>c_call </strong>Isotype subclass <strong>junction </strong>Junction nucleotide sequence <strong>junction_aa </strong>Junction amino acid sequence <strong>duplicate_count </strong>UMI count for the given unique sequence <strong>consensus_count </strong>Raw read count for the given unique sequence <strong>Sequence metadata column description</strong> <strong>sample_id </strong>Unique identifier for each sequencing library <strong>bioproject_accession </strong>NCBI BioProject accession number <strong>biosample_accession </strong>NCBI BioSample accession number <strong>sra_accession </strong>NCBI SRA accession number <strong>sex </strong>Sex of patient <strong>age </strong>Age of patient at time of sampling <strong>ethnicity </strong>Ethnicity of patient <strong>health_state </strong>One of worsening, stable, or improving
DOI: 10.17632/z5xrgrycvv.1
title: Data for Car Wash Operation: impacts on the executives' liability insurance market

description: This is a database gathered from Brazilian insurance market regulator. We used this to verify the positive impacts of Car Wash Operation on D&O insurance sector with a two-stage panel data regression modeling. For a robustness test, the mothodology was applied to the entire Brazilian insurance. We found that Car Wash Operation has contributed for the market retraction since its outbreak.
DOI: 10.17632/5xtgndzw4g.3
title: Data for: Un-riped fruit pods of Prosopis cineraria (L.) Druce ameliorates Cisplatin therapy induced partial testicular atrophy in male wistar rats

description: The .zip file contains IAEC certificate (confidential details removed) obtained for the project in 2015, Plant authentication certificate (obtained from Dr. Jayaraman, Botanist), together with all statistical calculations done on Sigmaplot®14. The PDF files generated as a result of statistical calculations performed in 2018 via. Sigmaplot® 14 compares G1 vs. G2, G3, G4, and G5. These were performed again before submission of the first manuscript to JEP in 2018. However, after comments from the reviewers, we had rerun the statistical evaluations via. Sigmaplo®14 in May 2020 during the revision and submission of the R1 version of the manuscript. These evaluations were performed as G2 vs. G1, G3, G4, and G5.
DOI: 10.17632/rhgkb8wfxj.2
title: Sound strength parameter G determined from uncalibrated room impulse responses measurements of concert halls using a set of window functions and lengths

description: The sound strength factor G computed using sixty three time windows in six frequency bands from two hundred and seventy seven uncalibrated room impulse responses (RIRs) corresponding to seven concert halls is reported. The RIRs were collected from the website of the Concert Hall Research Group of the Acoustical Society of America. The raw, processed, and compared data are provided as coma delimited, mat files and figures as well as the codes implementing the method. The maximum error across halls for mid frequencies using a Blackman-Harris time window of 23 ms was 1 dB. Figures p1, p2, p3 and p4 show the summary of the data for the 1/1 octave band centerd at 125 Hz, the low frequencies, the mid frequencies, and the high frequencies, repectively. The summary data are the mean error, maximum error and correlation across halls for the average G of each hall using seven time window functions and nine time window lengths. The whole data can be acessed in the folder DAT_files. The data is divided in 6 groups. In the first group, the strength factor G is estimated for the six 1/1 octave frequency bands from 125 Hz to 4 kHz. Each file contains a table of six columns corresponding to the frequency bands and a number of rows corresponding to the source-receiver combinations. Each file is labelled G_R_W_L.dat replacing R, W and L for the following codes. R codes the concert hall as follows: BM: Baltimore. Joseph Meyerhoff Symphony Hall (30 RIRs) BS: Boston. Symphony Hall (42 RIRs) BK: Buffalo. Kleinhans Music Hall (72 RIRs) PA: Philadelphia. Academy Of Music (36 RIRs) WM: Worcester. Massachusetts. The Grand Hall. Mechanics Hall (25 RIRs) CS: Cleveland. Severance Hall (36 RIRs) WK: Washington. D.C. JFK center. Concert hall (36 RIRs) W codes the window function as follows: TW: Taylor FT: Flattop RW: Rectangular HN: Hann HM: Hamming BW: Blackman BH: Blackman Harris L is the window length in ms with zero padding so as to complete three digits. The codes are the following: 001 003 006 012 023 046 093 186 372. A group (b) of sixty three files contains the average G for each of the seven halls in each row. This second group is coded as G_b_W_L.dat. A group (c) comprises the average for low, mid , and high frequencies coded as G_c_low.dat, G_c_mid.dat, and G_c_hig.dat. The columns of each file correspond to the halls and the rows to the windows. Each row correspond to a window length and each consecutive group of nine rows correspond to a window function. (e.g. the first nine correspond to a Taylor window of 1 ms, 3 ms, 6 ms, 12 ms, 23 ms, 46 ms, 93 ms, 186 ms, and 372 ms length, respectively). Groups (d), (e), and (f), are the data reported in figures p1-p4 obtained by comparison with results of G reported in the literature. All this data can be generated using the Matlab code result_figures.m and the Mat files. In turn, the mat files can be generated using the code calculate_Num_Den.m and the other auxiliary codes provided in the Matlab_code folder.
DOI: 10.5287/bodleian:qrnnqqe24
title: Supporting Data - Current Density Dependent Electroplating in Ca Electrolytes: From Globules to Dendrites
DOI: 10.5281/zenodo.3899030
title: Accompanied data files used in the paper "Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis"

description: lists of source file used in the paper "Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis" by Jing Yang, Amanda McGovern, Paul Martin, Kate Duffus, Xiangyu Ge, Peyman Zarrineh, Andrew P Morris, Antony Adamson, Peter Fraser, Magnus Rattray & Stephen Eyre. The paper has been accepted by Nature Communications.
description: raw sequencing data available on https://www.ncbi.nlm.nih.gov/gds/?term=GSE138767
DOI: 10.5281/zenodo.3840592
title: Accompanied data files used in the paper "Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis"

description: lists of source file used in the paper "Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis" by Jing Yang, Amanda McGovern, Paul Martin, Kate Duffus, Xiangyu Ge, Peyman Zarrineh, Andrew P Morris, Antony Adamson, Peter Fraser, Magnus Rattray & Stephen Eyre. The paper has been accepted by Nature Communications.
description: raw sequencing data available on https://www.ncbi.nlm.nih.gov/gds/?term=GSE138767
DOI: 10.5281/zenodo.3695685
title: mdahirel/pomatias-dispersal-2017: Initial release

description: Contains all scripts and data needed to recreate analyses in https://www.biorxiv.org/content/10.1101/2020.02.28.970160v1
DOI: 10.5281/zenodo.3897507
title: Ivrea Geophysical Body (IGB) model geometry

description: Ivrea Geophysical Body (IGB) model geometry Original publication: Scarponi et al. (2020), Geophys J Int, doi: 10.1093/gji/ggaa263 We here provide our reference density model for the Ivrea Geophysical Body (IGB), developed by means of 3D gravity data modelling. The IGB model is defined by a single crustal interface, extending across the study area. <br> The density contrast associated with the interface is +400 kg/m3. Study area (latitude, longitude in degrees): - min longitude: 7.5 E<br> - max longitude: 9.0 E<br> - min latitude: 45.4 N<br> - max latitude: 46.3 N Study area lower left corner in latitude, longitude: <br> - 45.4 N, 7.5 E <br> This point is marked as a red circle in the presented figures, and it is used as the origin for the Cartesian reference frame in kilometres (x[km],y[km]). Files' content: - 3D_IGB_Model_Geometry.txt: <br> <br> ASCII text file containing the model geometry in the format:<br> <br> x[km], y[km], lon[deg], lat[deg], depth[km]<br> <br> The Cartesian reference frame (x[km],y[km]) has its origin in the lower left corner of the study area and extends up to 800 km distance from the origin (the study area is extended on purpose during gravity modelling, to avoid edge effects). Here we report the complete model geometry as extracted from the modelling software (IGMAS+).<br> * The reader should focus only on the study area domain, where gravity data has been measured and modelled! * - 3D_IGB_Reference_Model.fig: <br> <br> MATLAB .fig format file showing a rotatable 3D view of the interface defining the IGB model geometry. - 2D_IGB_CrossSections.png: <br> <br> .png format picture showing East-West oriented vertical cross sections of the IGB model. The view is towards the South across the cross-sections. For any question and/or discussion please contact the authors via email at: matteo.scarponi@unil.ch Best regards,<br> Matteo Scarponi and co-authors
DOI: 10.5281/zenodo.3897506
title: Ivrea Geophysical Body (IGB) model geometry

description: Ivrea Geophysical Body (IGB) model geometry Original publication: Scarponi et al. (2020), Geophys J Int, doi: 10.1093/gji/ggaa263 We here provide our reference density model for the Ivrea Geophysical Body (IGB), developed by means of 3D gravity data modelling. The IGB model is defined by a single crustal interface, extending across the study area. <br> The density contrast associated with the interface is +400 kg/m3. Study area (latitude, longitude in degrees): - min longitude: 7.5 E<br> - max longitude: 9.0 E<br> - min latitude: 45.4 N<br> - max latitude: 46.3 N Study area lower left corner in latitude, longitude: <br> - 45.4 N, 7.5 E <br> This point is marked as a red circle in the presented figures, and it is used as the origin for the Cartesian reference frame in kilometres (x[km],y[km]). Files' content: - 3D_IGB_Model_Geometry.txt: <br> <br> ASCII text file containing the model geometry in the format:<br> <br> x[km], y[km], lon[deg], lat[deg], depth[km]<br> <br> The Cartesian reference frame (x[km],y[km]) has its origin in the lower left corner of the study area and extends up to 800 km distance from the origin (the study area is extended on purpose during gravity modelling, to avoid edge effects). Here we report the complete model geometry as extracted from the modelling software (IGMAS+).<br> * The reader should focus only on the study area domain, where gravity data has been measured and modelled! * - 3D_IGB_Reference_Model.fig: <br> <br> MATLAB .fig format file showing a rotatable 3D view of the interface defining the IGB model geometry. - 2D_IGB_CrossSections.png: <br> <br> .png format picture showing East-West oriented vertical cross sections of the IGB model. The view is towards the South across the cross-sections. For any question and/or discussion please contact the authors via email at: matteo.scarponi@unil.ch Best regards,<br> Matteo Scarponi and co-authors
DOI: 10.5281/zenodo.3899040
title: Temporally-Informed Analysis of Named Entity Recognition

description: This repository contains the data set developed for the paper: “Shruti Rijhwani and Daniel Preoțiuc-Pietro. <em>Temporally-Informed Analysis of Named Entity Recognition.</em> In Proceedings of the Association for Computational Linguistics (ACL). 2020.” It includes 12,000 tweets annotated for the named entity recognition task. The tweets are uniformly distributed over the years 2014-2019, with 2,000 tweets from each year. The goal is to have a temporally diverse corpus to account for data drift over time when building NER models. The entity types annotated are locations (LOC), persons (PER) and organizations (ORG). The tweets are preprocessed to replace usernames and URLs with a unique token. Hashtags are left intact and can be annotated as named entities. <strong>Format</strong> The repository contains the annotations in JSON format. Each year-wise file has the tweet IDs along with token-level annotations. The Public Twitter Search API (https://developer.twitter.com/en/docs/tweets/search) can be used extract the text for the tweet corresponding to the tweet IDs. <strong>Data Splits</strong> Typically, NER models are trained and evaluated on annotations available at the model building time, but are used to make predictions on data from a future time period. This setup makes the model susceptible to temporal data drift, leading to lower performance on future data as compared to the test set. To examine this effect, we use tweets from the years 2014-2018 as the training set and random splits of the 2019 tweets as the development and test sets. These splits simulate the scenario of making predictions on data from a future time period. The development and test splits are provided in the JSON format. <strong>Use</strong> Please cite the data set and the accompanying paper if you found the resources in this repository useful.
DOI: 10.5281/zenodo.3899039
title: Temporally-Informed Analysis of Named Entity Recognition

description: This repository contains the data set developed for the paper: “Shruti Rijhwani and Daniel Preoțiuc-Pietro. <em>Temporally-Informed Analysis of Named Entity Recognition.</em> In Proceedings of the Association for Computational Linguistics (ACL). 2020.” It includes 12,000 tweets annotated for the named entity recognition task. The tweets are uniformly distributed over the years 2014-2019, with 2,000 tweets from each year. The goal is to have a temporally diverse corpus to account for data drift over time when building NER models. The entity types annotated are locations (LOC), persons (PER) and organizations (ORG). The tweets are preprocessed to replace usernames and URLs with a unique token. Hashtags are left intact and can be annotated as named entities. <strong>Format</strong> The repository contains the annotations in JSON format. Each year-wise file has the tweet IDs along with token-level annotations. The Public Twitter Search API (https://developer.twitter.com/en/docs/tweets/search) can be used extract the text for the tweet corresponding to the tweet IDs. <strong>Data Splits</strong> Typically, NER models are trained and evaluated on annotations available at the model building time, but are used to make predictions on data from a future time period. This setup makes the model susceptible to temporal data drift, leading to lower performance on future data as compared to the test set. To examine this effect, we use tweets from the years 2014-2018 as the training set and random splits of the 2019 tweets as the development and test sets. These splits simulate the scenario of making predictions on data from a future time period. The development and test splits are provided in the JSON format. <strong>Use</strong> Please cite the data set and the accompanying paper if you found the resources in this repository useful.
DOI: 10.21227/b2pv-w753
title: Beamspace Channel Dataset for mmWave Massive MIMO

description: This is the dataset of mmWave massive MIMO beamspace channels, which is used for the experiment implementation of the paper "Acquiring Measurement Matrices via Deep Basis Pursuit for Sparse Channel Estimation in mmWave Massive MIMO Systems". The source code of the experiment implementation is also open-access on the Github repository DeepBP-AE.
DOI: 10.21227/0pqp-y320
title: Discriminative Concept Learning Network: Sample Source Code and Supplement (Project Archive)

description: Summary: The archive of DCLN project (https://sourceforge.net/projects/dcln/) is provided.Code & Script: Written in C/C++, running Shell scripts on Linux system. Mature package release v2.0 available for download. Check './dcln.sh' for usage info.Document: Details of hyperparameters tuning, data preprocessing and code compiling are given.Data: Four nonlinear simulation datasets are provided (Fig. 2 of the main paper). Each study has ~2000 training samples and ~2000 test samples.
DOI: 10.21227/j3gv-np89
title: Discriminative Concept Learning Network: Sample Source Code and Supplement (Project Archive)

description: Summary: The archive of DCLN project (https://sourceforge.net/projects/dcln/) is provided.Code & Script: Written in C/C++, running Shell scripts on Linux system. Mature package release v2.0 available for download. Check './dcln.sh' for usage info.Document: Details of hyperparameters tuning, data preprocessing and code compiling are given.Data: Four nonlinear simulation datasets are provided (Fig. 2 of the main paper). Each study has ~2000 training samples and ~2000 test samples.
DOI: 10.5281/zenodo.3899253
title: mdahirel/cepaea-personality-2017: Personality and shell morph in *Cepaea nemoralis* snails - v1.1

description: This release contains all data and code needed to re-do the analyses and figures in our manuscript "Boldness and exploration are linked to shell morph but not environmental context in the snail <em>Cepaea nemoralis</em>" (by Maxime Dahirel, Valentin Gaudu, Armelle Ansart) The release contains both source code and (on GitHub only) output files (so you do not have to re-run the models from scratch if you don't want to). See READMEs in source code folder for details
DOI: 10.25387/g3.12014826
title: Supplemental Material for Ohta et al., 2020

description: Supplemental information for Ohta et al., entitled: <br> Asymmetric Fitness of Second-Generation Interspecific Hybrids Between <i>Ciona robusta </i>and<i> Ciona intestinalis</i>
DOI: 10.25387/g3.12014826.v2
title: Supplemental Material for Ohta et al., 2020

description: Supplemental information for Ohta et al., entitled: <br> Asymmetric Fitness of Second-Generation Interspecific Hybrids Between <i>Ciona robusta </i>and<i> Ciona intestinalis</i>
DOI: 10.5281/zenodo.3899268
title: The BrukerAPI test data set (Bruker ParaVision v5.1)

description: This data set contains data from 32 experiments performed using the Bruker ParaVision v5.1 software. The purpose of this data set is to test the unified application programming interface to the Bruker data format - the <strong>BrukerAPI</strong>. See the software repository for more information. The data was measured using the following pulse sequences: FLASH.ppg MGE.ppg MSME.ppg RARE.ppg FAIR_RARE.ppg RAREVTR.ppg RAREst.ppg MDEFT.ppg FISP.ppg FLOWMAP.ppg DtiStandard.ppg EPI.ppg FAIR_EPI.ppg DtiEpi.ppg T1_EPI.ppg T2_EPI.ppg T2S_EPI.ppg SPIRAL.ppg DtiSpiral.ppg UTE.ppg UTE3D.ppg ZTE.ppg CSI.ppg FieldMap.ppg NSPECT.ppg PRESS.ppg STEAM.ppg igFLASH.ppg The data was measured by the NMR group at the Institute of Scientific Instruments of the Czech Academy of Sciences
DOI: 10.6084/m9.figshare.12501155
title: TestDataset

description: test
DOI: 10.6084/m9.figshare.12501155.v2
title: TestDataset

description: test
DOI: 10.6084/m9.figshare.12501155.v1
title: TestDataset

description: test
DOI: 10.5281/zenodo.3662987
title: Normalized Stanford Helicopter Dataset

description: Normalized version of the Stanford Helicopter Dataset (http://heli.stanford.edu/dataset/), used in the paper Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM.
DOI: 10.5281/zenodo.3662986
title: Normalized Stanford Helicopter Dataset

description: Normalized version of the Stanford Helicopter Dataset (http://heli.stanford.edu/dataset/), used in the paper Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM.
DOI: 10.5281/zenodo.3899403
title: bimmer results for 405 phenotypes from the UKBB

description: Full data analysis results corresponding to the manuscript "Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization"
DOI: 10.5281/zenodo.3895124
title: bimmer results for 405 phenotypes from the UKBB

description: Full data analysis results corresponding to the manuscript "Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization"
DOI: 10.5281/zenodo.3895162
title: PIPD2020 - Pinterest Interests Profiling Dataset 2020

description: This dataset contains features extracted with EfficientNet-B3 from over 60000 images classified in 15 interests categories 4 of which are further analyzed in subcategories, features from the images of 12 users with hand-labeled Pinboards, the text extracted from the previous images and over 200000 features from the images of 422 Pinterest users along with their follower-followee relationships.
DOI: 10.5281/zenodo.3895161
title: PIPD2020 - Pinterest Interests Profiling Dataset 2020

description: This dataset contains features extracted with EfficientNet-B3 from over 60000 images classified in 15 interests categories 4 of which are further analyzed in subcategories, features from the images of 12 users with hand-labeled Pinboards, the text extracted from the previous images and over 200000 features from the images of 422 Pinterest users along with their follower-followee relationships.