cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.996889
title: Test-Retest Qt-Dmri Datasets For "Non-Parametric Graphnet-Regularized Representation Of Dmri In Space And Time"

description: We release these four diffusion MRI data sets as part of our recent journal publication; Fick, Rutger H.J., et al. "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time." <em>Medical Image Analysis</em> (2017). More detailed information about the use of these data sets can also be found in the publication.

We acquired test-retest diffusion MRI spin echo sequences from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The data consists of 80x160x5 voxels of size 110x110x500\(\mu\)m. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL's eddy. We then drew a region of interest in the middle slice in the corpus callosum, where the tissue is reasonably coherent.

- The diffusion MRI data are contained in the files with 'dwis' in the name.<br>
<br>
- The corpus callosum masks are contained in the files with 'mask' in the name.

- The acquisition parameters are contained in the .txt files.
description: This work was partly supported by ANR ``MOSIFAH" under ANR-13-MONU-0009-01, the ERC under the European Union's Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665:CoBCoM), MAXIMS grant funded by ICM's The Big Brain Theory Program and ANR-10-IAIHU-06.
description: {"references": ["Fick, R.H., Petiet, A., Santin, M., Philippe, A.C., Lehericy, S., Deriche, R. and Wassermann, D., 2017. Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time. Medical Image Analysis."]}
DOI: 10.5281/zenodo.1302388
title: Moma Collection - Automatic Monthly Update

description: Temporary Description
DOI: 10.5281/zenodo.1302389
title: Moma Collection - Automatic Monthly Update

description: Temporary Description
DOI: 10.5281/zenodo.1435999
title: Drug Indications Extracted From Faers

description: This dataset contains drug indications extracted from the FDA Adverse Event Reporting System (FAERS).

Source code here: https://github.com/stuppie/faers

<strong>Method Outline</strong>


	Data files are extracted from zip files, parsed from csvs, and imported into a MySQL database (see parser.py).
	Duplicate records are then de-duplicated by taking only the most recent version for each case ID (see dedupe.py).
	Indications are normalized by matching to UMLS terms by string matching. Cross-references to Human Phenotype Ontology are pulled from UMLS and xrefs to Monarch Disease Ontology (MONDO) are pulled from MONDO using the UMLS xrefs. (See normalize_indications.py)
	Drugs names are normalized first by applying a few simple string cleaning operations (strip, fix slashes and periods). Then they are attempted to be matched to rxnorm by exact string matching. Those that don't match are run against rxnorm's approximate matching service, and are accepted if the score is higher than 67/100. The matched rxnorm CUIs are then mapped to the their Ingredient level rxnorm ID. (See normalize_drugs.py)
	Indications are then retrieved for each drug ingredient and filtered to require a minimum of 20 individual occurances. (See get_indications.py)
DOI: 10.5281/zenodo.1438722
title: 20Th Century Press Archives Json-Ld Dump For Cdv 2018 Rhein-Main: Persons And Companies

description: Folder metadata for all person and company folders of PM20, which have publicly accessible documents.

For a preview and further information, please see https://github.com/zbw/cdv2018-pressemappe20 (mostly in German)
DOI: 10.5281/zenodo.1439878
title: Moma Collection - Automatic Monthly Update

description: Temporary Description
DOI: 10.5281/zenodo.1439879
title: Moma Collection - Automatic Monthly Update

description: Temporary Description
DOI: 10.5281/zenodo.154505
title: Further Analysis By Contentmine Of Oa Papers Returned By Querying Europepmc With 'Zika'

description: Facts and metadata extracted from papers returned from querying EuropePMC with the phrase 'zika' using the ContentMine Cambridge pipeline. With dictionaries available at https://github.com/contentmine/dictionaries. Query and dictionaries used as of 2016-09-19.

This dataset contains metadata and facts from 524 papers. A large number of the terms in the dictionaries used now contain wikidata IDs.

Canary Version: 2ab2e9e7d95c2d6f81636739f383e52543befeff

Dictionaries Version: 2498cf1bae660ae44437bcba388ef8436cc8554c
DOI: 10.5281/zenodo.803333
title: Actionable Information During A Disaster (Self-Organize Relief Efforts Via #Porteouverte)

description: <strong>Abstract</strong> (our paper)

Web-based social and communication technologies enable citizens to self-organize relief efforts in response to crises. This work focuses on a question fundamental to the concept of collective intelligence: how effective are such self-organized channels, ungoverned by any central authority, in conforming to their intended function? In this study we examine the hashtag #PorteOuverte ("#OpenDoor") introduced during the 2015 Paris terrorist attacks, as an "improvised logistical channel" (ILC) to help individuals to find a safe shelter near the attack sites. We analyze the dynamics and effectiveness of #PorteOuverte by comparing its proportion of relevant logistical messages -- individuals requesting or offering shelter -- to other messages such as those offering emotional consolation or commenting on the hashtag itself.  Our results reveal that the vast majority of messages are not relevant, however the crowd senses and spreads relevant messages more than others.  We further demonstrate that relevant messages can be automatically detected and thus algorithmic promotion may be possible.

<strong>Data</strong>

The #PorteOuverte hashtag ("opendoor" in English), created right after the 2015 terrorist attacks in Paris, was used by individuals to offer shelter to strangers stranded by the attacks and by individuals in need of shelter to request help and post their whereabouts. The file #PorteOuverte _tweet_ids.txt contains all the original tweet ids that used this hashtag.

The first tweet was posted on Friday, 13 Nov 2015 21:34:06 GMT.

Duration: 2015-11-13 to 2015-11-16 (retweets not included).

Total number of tweets: 75547

<strong>Publication</strong>

This data set was created for our study. If you make use of this data set, please cite:

He, X., Lu, D., Margolin, D., Wang, M., Idrissi, S., Lin, Y.-R. (2017). "The Signals and Noise: Actionable Information in Improvised Social Media Channels During a Disaster," Proceedings of Web Science 2017 (WebSci 2017), 2017. doi:10.1145/3091478.3091501
DOI: 10.13012/b2idb-2204122_v1
title: Sorghum bicolor GBS: diversity study

description: GBS data from diverse sorghum lines. Project funded by DOE, ARPA-E, and startup funds to PJ Brown.
DOI: 10.13012/b2idb-4650469_v1
title: Smartphone recorded driving sensor data: Indianapolis International Airport to Urbana, IL

description: Indianapolis Int'l Airport to Urbana: Sampling Rate: 2 Hz Total Travel Time: 5901534 ms or 98.4 minutes Number of Data Points: 11805 Distance Traveled: 124 miles via I-74 Device used: Samsung Galaxy S6 Date Recorded: 2016-11-27 Parameters Recorded: * ACCELEROMETER X (m/s²) * ACCELEROMETER Y (m/s²) * ACCELEROMETER Z (m/s²) * GRAVITY X (m/s²) * GRAVITY Y (m/s²) * GRAVITY Z (m/s²) * LINEAR ACCELERATION X (m/s²) * LINEAR ACCELERATION Y (m/s²) * LINEAR ACCELERATION Z (m/s²) * GYROSCOPE X (rad/s) * GYROSCOPE Y (rad/s) * GYROSCOPE Z (rad/s) * LIGHT (lux) * MAGNETIC FIELD X (microT) * MAGNETIC FIELD Y (microT) * MAGNETIC FIELD Z (microT) * ORIENTATION Z (azimuth °) * ORIENTATION X (pitch °) * ORIENTATION Y (roll °) * PROXIMITY (i) * ATMOSPHERIC PRESSURE (hPa) * SOUND LEVEL (dB) * LOCATION Latitude * LOCATION Longitude * LOCATION Altitude (m) * LOCATION Altitude-google (m) * LOCATION Altitude-atmospheric pressure (m) * LOCATION Speed (kph) * LOCATION Accuracy (m) * LOCATION ORIENTATION (°) * Satellites in range * GPS NMEA * Time since start in ms * Current time in YYYY-MO-DD HH-MI-SS_SSS format Quality Notes: There are some things to note about the quality of this data set that you may want to consider while doing preprocessing. This dataset was taken continuously as a single trip, no stop was made for gas along the way making this a very long continuous dataset. It starts in the parking lot of the Indianapolis International Airport and continues directly towards a gas station on Lincoln Avenue in Urbana, IL. There are a couple parts of the trip where the phones orientation had to be changed because my navigation cut out. These times are easy to account for based on Orientation X/Y/Z change. I would also advise cutting out the first couple hundred points or the points leading up to highway speed. The phone was mounted in the cupholder in the front seat of the car.
DOI: 10.13012/b2idb-5975383_v1
title: Smartphone recorded driving sensor data: Leesburg, VA to Indianapolis, IN

description: Leesburg, VA to Indianapolis, Indiana: Sampling Rate: 0.1 Hz Total Travel Time: 31100007 ms or 518 minutes or 8.6 hours Distance Traveled: 570 miles via I-70 Number of Data Points: 3112 Device used: Samsung Galaxy S4 Date Recorded: 2017-01-15 Parameters Recorded: * ACCELEROMETER X (m/s²) * ACCELEROMETER Y (m/s²) * ACCELEROMETER Z (m/s²) * GRAVITY X (m/s²) * GRAVITY Y (m/s²) * GRAVITY Z (m/s²) * LINEAR ACCELERATION X (m/s²) * LINEAR ACCELERATION Y (m/s²) * LINEAR ACCELERATION Z (m/s²) * GYROSCOPE X (rad/s) * GYROSCOPE Y (rad/s) * GYROSCOPE Z (rad/s) * LIGHT (lux) * MAGNETIC FIELD X (microT) * MAGNETIC FIELD Y (microT) * MAGNETIC FIELD Z (microT) * ORIENTATION Z (azimuth °) * ORIENTATION X (pitch °) * ORIENTATION Y (roll °) * PROXIMITY (i) * ATMOSPHERIC PRESSURE (hPa) * Relative Humidity (%) * Temperature (F) * SOUND LEVEL (dB) * LOCATION Latitude * LOCATION Longitude * LOCATION Altitude (m) * LOCATION Altitude-google (m) * LOCATION Altitude-atmospheric pressure (m) * LOCATION Speed (kph) * LOCATION Accuracy (m) * LOCATION ORIENTATION (°) * Satellites in range * GPS NMEA * Time since start in ms * Current time in YYYY-MO-DD HH-MI-SS_SSS format Quality Notes: There are some things to note about the quality of this data set that you may want to consider while doing preprocessing. This dataset was taken continuously but had multiple stops to refuel (without the data recording ceasing). This can be removed by parsing out all data that has a speed of 0. The mount for this dataset was fairly stable (as can be seen by the consistent orientation angle throughout the dataset). It was mounted tightly between two seats in the back of the vehicle. Unfortunately, the frequency for this dataset was set fairly low at one per ten seconds.
DOI: 10.13012/b2idb-6534803_v1
title: Sorghum bicolor GBS: recombination study

description: GBS data from biparental sorghum populations provided by Dr. Bill Rooney, TAMU. Data produced and analyzed by Pradeep Hirannaiah to study recombination in sorghum. Funding for this study was provided by the Sorghum Checkoff.
DOI: 10.22021/ethoscsv201810
title: UK Doctoral Thesis Metadata from EThOS

description: This dataset supersedes 10.22021/ETHOSCSV201803
DOI: 10.23645/epacomptox.6062629.v4
title: ToxCast Database (invitroDB) ReadMe

description: ReadMe for ToxCast high-throughput assay information including
assay annotation user guide, assay target information, study design information
and quality statistics on the assays.
DOI: 10.5281/zenodo.151912
title: A Sharper View Of Pal 5'S Tails: Discovery Of Stream Perturbations With A Novel Non-Parametric Technique

description: Here we release some data on the Pal 5 stellar stream from the  arXiv:1609.01282 paper.

 The file pal5_measurements.fits stores the Pal 5 stream measurements , as shown on Figure 5 of the paper, with the following columns


	phi1 Angle along the stream in degrees ( the coordinate system is  defined in the paper)
	phi2 Angle across the stream in degrees
	sbstream Central surface brightness of the stream in stars per square arcmin
	ldens Linear density of the stream in stars/arcmin
	cumdens Cumulative density of the (leading/trailing) tail from the progenitor
	width Gaussian sigma of the stream in degrees
	bgdens density of background stars


The columns with _16, _50, _84 appended to their name refer to the 16%, 50% 84% percentiles inferred from the posterior samples.

We also provide the MCMC chains that could be used to measure the covariance matrices of different parameters:

The chains are stored in the fits file with the columns

phi2_i, logI_j, logSig_j, logB0_j, logB1_j referring to the stream track values at the nodes, log of  stream surface brightness, log of stream width, log of the background density and log of background density slope (see paper for more details).
DOI: 10.5281/zenodo.154753
title: Movelab/Mosquito_Alert_Map_Data: V1.268.2016

description: Release of version 1.268.2016
DOI: 10.5281/zenodo.155079
title: Movelab/Mosquito_Alert_Map_Data: V1.269.2016

description: Release of version 1.269.2016
DOI: 10.5281/zenodo.155119
title: Movelab/Mosquito_Alert_Map_Data: V1.270.2016

description: Release of version 1.270.2016
DOI: 10.5281/zenodo.293818
title: Testkaterina

description: zxxaxaxa
DOI: 10.5281/zenodo.599520
title: A Sharper View Of Pal 5'S Tails: Discovery Of Stream Perturbations With A Novel Non-Parametric Technique

description: Here we release some data on the Pal 5 stellar stream from the  arXiv:1609.01282 paper.

 The file pal5_measurements.fits stores the Pal 5 stream measurements , as shown on Figure 5 of the paper, with the following columns


	phi1 Angle along the stream in degrees ( the coordinate system is  defined in the paper)
	phi2 Angle across the stream in degrees
	sbstream Central surface brightness of the stream in stars per square arcmin
	ldens Linear density of the stream in stars/arcmin
	cumdens Cumulative density of the (leading/trailing) tail from the progenitor
	width Gaussian sigma of the stream in degrees
	bgdens density of background stars


The columns with _16, _50, _84 appended to their name refer to the 16%, 50% 84% percentiles inferred from the posterior samples.

We also provide the MCMC chains that could be used to measure the covariance matrices of different parameters:

The chains are stored in the fits file with the columns

phi2_i, logI_j, logSig_j, logB0_j, logB1_j referring to the stream track values at the nodes, log of  stream surface brightness, log of stream width, log of the background density and log of background density slope (see paper for more details).
DOI: 10.12751/g-node.0f3ce8
title: A general prospective motion correction framework that improves the diagnostic quality of structural and angiographic MRI of the brain

description: This depository contains MRI image data for a motion correction study using the iMOCO framework at the Danish Research Centre for Magnetic Resonance (http://www.drcmr.dk).
DOI: 10.12751/g-node.5a4628
title: Detection of Children/Youth with Fetal Alcohol Spectrum Disorder through Eye Movement, Psychometric and Neuroimaging Data

description: All the data for six assessments involved.
description: IsPartOf: Detection of Children/Youth with Fetal Alcohol Spectrum Disorder through Eye Movement, Psychometric and Neuroimaging Data
DOI: 10.22021/bbldntz201809
title: The Linked Open British National Bibliography – Books. 1950/2018-09 – N-Triples
DOI: 10.22021/bbldrdfz201809
title: The Linked Open British National Bibliography – Books. 1950/2018-09 – RDF/XML
DOI: 10.22021/bcldntz201809
title: The Linked Open British National Bibliography – Forthcoming Books. 2018-09 – N-Triples
DOI: 10.22021/bsldntz201809
title: The Linked Open British National Bibliography – Serials. 1950/2018-09 – N-Triples
DOI: 10.22021/bsldrdfz201809
title: The Linked Open British National Bibliography – Serials. 1950/2018-09 – RDF/XML
DOI: 10.23645/epacomptox.6062785.v3
title: ToxCast Data Pipeline Overview

description: The tcpl package was developed to process high-throughput and high-content screening data generated by the ToxCast program. ToxCast is screening thousands of chemicals with hundreds of assays coming from numerous and diverse biochemical and cell-based technology platforms. The diverse data, received in heterogeneous formats from numerous vendors, are transformed to a standard computable format and loaded into the tcpl database by vendor-specific R scripts. Once data is loaded into the database, ToxCast utilizes the generalized processing functions provided in this package to process, normalize, model, qualify, flag, inspect, and visualize the data. While developed primarily for ToxCast, we have attempted to make the tcpl package generally applicable to chemical-screening community.
DOI: 10.23645/epacomptox.6062785.v4
title: ToxCast Data Pipeline Overview

description: The tcpl package was developed to process high-throughput and high-content screening data generated by the ToxCast program. ToxCast is screening thousands of chemicals with hundreds of assays coming from numerous and diverse biochemical and cell-based technology platforms. The diverse data, received in heterogeneous formats from numerous vendors, are transformed to a standard computable format and loaded into the tcpl database by vendor-specific R scripts. Once data is loaded into the database, ToxCast utilizes the generalized processing functions provided in this package to process, normalize, model, qualify, flag, inspect, and visualize the data. While developed primarily for ToxCast, we have attempted to make the tcpl package generally applicable to chemical-screening community.
DOI: 10.5281/zenodo.1000093
title: Moma Collection - Automatic Monthly Update

description: Temporary Description
DOI: 10.5281/zenodo.1186462
title: Moma Collection - Automatic Monthly Update

description: Temporary Description
DOI: 10.5281/zenodo.1186463
title: Moma Collection - Automatic Monthly Update

description: Temporary Description
DOI: 10.13012/b2idb-8131709_v1
title: Data for: Identifying marginal land for multifunctional perennial cropping systems in the Upper Sangamon River Watershed, Illinois

description: Supplementary Material for article entitled: "Identifying marginal land for multifunctional perennial cropping systems in the Upper Sangamon River Watershed, Illinois". The material includes the methodology of GIS RUSLE model and details of the suitability analysis variables.
DOI: 10.22021/bcldrdfz201809
title: The Linked Open British National Bibliography – Forthcoming Books. 2018-09 – RDF/XML
DOI: 10.5281/zenodo.1000092
title: Moma Collection - Automatic Monthly Update

description: Temporary Description
DOI: 10.5281/zenodo.823296
title: Datasets For Predicting Tf Binding Using Virtual Chip-Seq

description: This repository contains datasets necessary for using the Virtual ChIP-seq software.

Virtual ChIP-seq requires the following datasets to predict transcription factor binding:


	
	chipExpDir_AtoH_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters A-H.
	
	
	chipExpDir_ItoZ_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters I-Z.
	
	
	refTables_V1.1.0.tar.gz: PhastCons genomic conservation, FIMO PWM scores for JASPAR motifs, and ChIP-seq data of ENCODE and Cistrome database.
	
	
	hg38_chrsize.tsv: Length of chromosomes in hg38
	
	
	trainedModels_V1.0.0.tar.gz: Virtual ChIP-seq scikit-learn trained models saved in joblib format
	
	
	&lt;CellType&gt;.tar.gz: Pre-calculated matrices suitable for training with other algorithms or re-training with Virtual ChIP-seq.
	


Some predictive features of TF binding are the same in each cell type and are stored together for simplicity in refTables_V1.0.0.tar.gz. You can use datasets from other cell types (named here as  &lt;CellType&gt;.tar.gz) for the purpose of re-training the model. The &lt;CellType&gt;.tar.gz files contain pre-calculated predictive features of transcription factor binding in 4 chromosomes (5, 10, 15, 20).

These features include:


	
	PhastCons genomic conservation
	
	
	FIMO score for sequence motifs of TF in the JASPAR database
	
	
	Chromatin accessibility
	
	
	TF binding in ENCODE + Cistrome DB datasets
	
	
	Virtual ChIP-seq expression score
DOI: 10.5281/zenodo.823297
title: Datasets For Predicting Tf Binding Using Virtual Chip-Seq

description: This repository contains datasets necessary for using the Virtual ChIP-seq software.

Virtual ChIP-seq requires the following datasets to predict transcription factor binding:


	
	chipExpDir_AtoH_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters A-H.
	
	
	chipExpDir_ItoZ_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters I-Z.
	
	
	refTables_V1.1.0.tar.gz: PhastCons genomic conservation, FIMO PWM scores for JASPAR motifs, and ChIP-seq data of ENCODE and Cistrome database.
	
	
	hg38_chrsize.tsv: Length of chromosomes in hg38
	
	
	trainedModels_V1.0.0.tar.gz: Virtual ChIP-seq scikit-learn trained models saved in joblib format
	
	
	&lt;CellType&gt;.tar.gz: Pre-calculated matrices suitable for training with other algorithms or re-training with Virtual ChIP-seq.
	


Some predictive features of TF binding are the same in each cell type and are stored together for simplicity in refTables_V1.0.0.tar.gz. You can use datasets from other cell types (named here as  &lt;CellType&gt;.tar.gz) for the purpose of re-training the model. The &lt;CellType&gt;.tar.gz files contain pre-calculated predictive features of transcription factor binding in 4 chromosomes (5, 10, 15, 20).

These features include:


	
	PhastCons genomic conservation
	
	
	FIMO score for sequence motifs of TF in the JASPAR database
	
	
	Chromatin accessibility
	
	
	TF binding in ENCODE + Cistrome DB datasets
	
	
	Virtual ChIP-seq expression score
DOI: 10.12751/g-node.ef14cc
title: FDModel1

description: rsfMRI data comprising males and females of different age.
DOI: 10.5281/zenodo.1066931
title: Virtual Chip-Seq Predictions Of Binding Of 36 Transcription Factor In Roadmap Epigenomics Project Tissues

description: This dataset contains predictions of Virtual ChIP-seq for binding of 36 transcription factors in Roadmap Epigenomics dataset tissues with matched DNase-seq and RNA-seq data.

Tarball contains subfolders for each of the 36 TFs where Virtual ChIP-seq median MCC in validation cell types was &gt; 0.3.

Each subfolder contains gzipped BED files. Each file is named as &lt;Tissue&gt;_&lt;Age&gt;_&lt;TF&gt;_&lt;Accession&gt;_Predictions.bed.gz. Columns correspond to Chromosome, Start, End, &lt;Tissue&gt;_&lt;Age&gt;_&lt;TF&gt;_&lt;Accession&gt;, Posterior probability

You can use the posterior probabilities provided in Virchip_PosteriorCutoffs_V3.0.0.tsv. These are posterior probability cutoffs which maximized MCC in H1-hESC cell type, or are set to 0.4 if there was no ChIP-seq data of that TF in H1-hESC (0.4 is the mode of all optimal posterior probability cutoffs in H1-hESC).