cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5572816.v1
title: CVR dataset

description: CVR and cognition in healthy ageing<br><br>Regional CVR values calculated from ASL BOLD data, demographic and cognitive data <br>
DOI: 10.6084/m9.figshare.5572834.v1
title: Synthetic spectra and photometry for models in the GRAMS carbon-star grid

description: The Grid of RSG and AGB ModelS (GRAMS) is a set of radiative transfer models for circumstellar dust around low- and intermediate-mass evolved stars. This file contains the synthetic spectra and photometry for the entire carbon-star GRAMS grid in the form of a FITS array of structures. For each model photosphere, the grid contains models over a large range of dust inner shell radii and optical depths. See Srinivasan, Sargent & Meixner (A&A 2011 532 54) for details.<br>Synthetic photometry available for the following filters:<br>MCPSU ,MCPSB ,MCPSV ,MCPSI ,2MASSJ ,2MASSH ,2MASSK ,IRAC36 ,IRAC45 ,IRAC58 ,IRAC80 ,MIPS24 ,MIPS70 ,MIPS160 ,AKARIN2 ,AKARIN3 ,AKARIN4 ,AKARIS7 ,AKARIS9W ,AKARIS11 ,AKARIL15 ,AKARIL18W ,AKARIL24 ,WISEW1 ,WISEW2 ,WISEW3 ,WISEW4 ,ISOCAMLW2 ,ISOCAMLW3 ,MIRSI_KBAND ,MIRSI4_9 ,MIRSI7_7 ,MIRSI8_7 ,MIRSI9_6 ,MIRSI10_5 ,MIRSI11_7 ,MIRSI12_28 ,MIRSI12_33 ,MIRSI18_5 ,MIRSI20_9 ,MIRSI24_5 ,GALEX_FUV ,GALEX_NUV ,SDSS_u ,SDSS_g ,SDSS_r ,SDSS_i ,SDSS_z ,MIR_NBF_1 ,MIR_NBF_2 ,MIR_NBF_3 ,MIR_NBF_4 ,MIR_NBF_5 ,MIR_NBF_6 ,MIR_NBF_7 ,MIR_NBF_8 ,MIR_NBF_9 ,MIR_NBF_10 ,MIR_NBF_11 ,MIR_NBF_12 ,MIR_NBF_13 ,MIR_NBF_14 ,MIR_NBF_15 ,MIR_NBF_16 ,MIR_NBF_17 ,MIR_NBF_18 ,MIR_NBF_19 ,MIR_NBF_20 ,MIR_NBF_21 ,MIR_NBF_22 ,MIR_NBF_23 ,Rband ,PACS70 ,PACS100 ,PACS160 ,SPIRE250 ,SPIRE350 ,SPIRE500 ,IRAS_12 ,IRAS_25 ,IRAS_60 ,IRAS_100
DOI: 10.6084/m9.figshare.5572789
title: Sturgeon figures

description: The dataset contains figures of different numbers of sturgeons collected in aquaculture ponds and can be used for sturgeon counting.
DOI: 10.23641/asha.5572786
title: Summarization differences for expository discourse in adolescents with TBI (Lundine et al., 2017)

description: <b>Purpose: </b>Annually, nearly 700,000 U.S. children and adolescents experience a traumatic brain injury (TBI). Many of them struggle academically, despite failing to qualify for special education services because their cognitive communication impairments are subtle.<b>Method: </b>In this exploratory study, five adolescents with TBI provided verbal summaries of two expository lectures (compare–contrast, cause–effect) and participated in cognitive and expressive syntax testing. Their performance on these tasks was compared descriptively to that of 50 adolescents with typical development.<b>Results: </b>For adolescents with TBI, mean summary quality scores for both exposition types were at least 1 SD lower than those of adolescents with typical development and notably 2 SDs below for the cause–effect passage. The adolescents with TBI who had below-average cognitive scores showed better performance on compare–contrast summaries compared to cause–effect, whereas the majority of adolescents with typical development showed the opposite tendency.<b>Conclusions:</b> These results provide preliminary evidence that students with TBI, particularly those with cognitive deficits, may struggle with expository discourse despite acceptable performance on a measure of expressive syntax. This study also indicates that researchers should explore how students with TBI perform on academically relevant discourse tasks in order to inform future assessment and intervention efforts.<br><b>Supplemental Material S1.</b> Comparison of cause–effect and compare–contrast expository discourse samples commonly used measures of length and reading level and Coh-Metrix.<br><b>Supplemental Material S2.</b> The five traits comprising the holistic scoring rubric and descriptors for each illustrating the lowest and highest possible point values, as well as scoring examples from summaries produced by 18-year-old female participants in both groups. Summaries from each participant are shared in the article, Figures 1 and 2. <br><b>Supplemental Material S3. </b>Expository summaries from the additional four participants with traumatic brain injury (TBI). <br>Lundine, J. P., Harnish, S. M., McCauley, R. J., Zezinka, A. B., Blackett, D. S., & Fox, R. A. (2017). Exploring summarization differences for two types of expository discourse in adolescents with traumatic brain injury. <i>American Journal of Speech-Language Pathology, 27, </i>247–257<i>.</i> https://doi.org/10.1044/2017_AJSLP-16-0131
DOI: 10.6084/m9.figshare.5572774
title: Interactome data

description: Interactome used for doing PPI based analysis
DOI: 10.6084/m9.figshare.5572768
title: STARD data

description: STARD analysis
DOI: 10.6084/m9.figshare.5572714
title: LP661-13 TRES Spectra

description: These are 14 epochs of TRES high resolution spectra of the M4 eclipsing binary LP661-13. These data were originally published in Dittmann et al. 2017, and were later used in Czekala et al. 2017 as a proof of concept for the PSOAP disentangling algorithm. <br>Feel free to use these data for instructional/tutorial purposes on how to use PSOAP. If you plan to write a paper based upon these spectra (i.e., results derived from this specific dataset will appear in your paper) please contact Jason and Ian for usage details at jason.dittmann@gmail.com and iancze@gmail.com.<br>Tools to read the HDF5 format are available in most programming languages---google will likely yield a package for your favorite language.<br>Documentation on the PSOAP project can be found in the references.
DOI: 10.6084/m9.figshare.5572762
title: Data to accompany "Standardized Lab Shoes Decrease Vertical Loading Rate and Increase Self-Selected Speed Compared to Running in Own Personal Shoes"

description: Raw data (N = 50 subjects) for basic descriptors of running mechanics when running at three self-selected speeds (slow, normal, fast) in two types of shoes (own shoes, lab shoes). The lab shoes were NB 780v5.
DOI: 10.6084/m9.figshare.5572768.v1
title: STARD data

description: STARD analysis
DOI: 10.6084/m9.figshare.5572762.v1
title: Data to accompany "Standardized Lab Shoes Decrease Vertical Loading Rate and Increase Self-Selected Speed Compared to Running in Own Personal Shoes"

description: Raw data (N = 50 subjects) for basic descriptors of running mechanics when running at three self-selected speeds (slow, normal, fast) in two types of shoes (own shoes, lab shoes). The lab shoes were NB 780v5.
DOI: 10.6084/m9.figshare.5572687
title: SEPSIS-neutrophil-spontaneous motility

description: "Ellett_data analysis codes.7z" contains several files with code for image analysis, cell tracking, and motility pattern analysis, for microfluidic assay that measures neutrophil spontaneous motility in whole blood. "Ellett_Numerical Values Fig 2-6.xlsx" contains all numerical values used for generating the graphs in figures 2 to 6. <br>
DOI: 10.6084/m9.figshare.5572675
title: Pseudo-tested Methods

description: We analyzed test cases of 19 open-source projects using mutation testing. We determined for each covered method whether at least one test case can detect if the method’s whole logic is removed. We studied method characteristics and relations to test cases to find indicators for covered methods that are pseudo-tested.<br><br>The SQL dump requires a MySQL database (version &gt;= 5.7).
DOI: 10.6084/m9.figshare.5572681
title: TITCO database of 16047 trauma patients

description: Database of 16,047 trauma patients from trauma clinics in Mumbai, Delhi and Kolkata. Data was gathered between 2013-2015.<br><b><br>Columns description:</b><br>Patient ID: Deidentified patient IDHospital ID: Deidentified hospital IDSex: Patient sexDied: If patient died or not during observationTime_arrival_deathdischarge: Time from arrival at hospital until death or discharge in hoursAge: Patient ageSBP: Patient systolic blood pressure at first surveyGCS Patient Glasgow coma scale score at first surveyHR: Patient heart rate at first surveyTransferred: If patient was transferred from other hospital, yes or noSurgical_category: Category according to NOMESCO classification of surgical procedures (A-Q, W= uncategorizable)- Multiple letters indicates that multiple procedures were performed, in which cases the first letter is the main surgery's category- NA indicates that no surgery was performed<br><br><br>
DOI: 10.6084/m9.figshare.5572663
title: Dataset for "One Step at a Time: Representational Overlap Between Active-voice, Be-passive, and Get-passive in English"

description: This is the data corresponding to the submitted paper "<i>One Step at a Time: Representational Overlap Between Active-voice, Be-passive, and Get-passive in English</i>".<br>There are three experiments; one per sheet. These show raw ratings, presence or absence of a suggested rephrase, and the type of rephrase suggested.
DOI: 10.6084/m9.figshare.5572636
title: Gaze doesn't always lead steering

description: Data and analysis for Gaze doesn't always lead steering by Lehtonen et al, published in Accident Analysis and Prevention (https://doi.org/10.1016/j.aap.2018.09.026).<br>Participants drove on a motorway with an instrumented car performing a visual secondary task. See Description of the data.txt for the variables. Analysis code written in R using R Markdown format.
DOI: 10.6084/m9.figshare.5572675.v1
title: Pseudo-tested Methods

description: We analyzed test cases of 19 open-source projects using mutation testing. We determined for each covered method whether at least one test case can detect if the method’s whole logic is removed. We studied method characteristics and relations to test cases to find indicators for covered methods that are pseudo-tested.<br><br>The SQL dump requires a MySQL database (version &gt;= 5.7).
DOI: 10.6084/m9.figshare.5572591
title: Code of word network topic model

description: <pre>1. Introduction --------------- <br></pre><pre><br></pre><pre>Class PrepareInput can convert the original documents into word co-occurrence network and re-weight it, then save it as pseudo documents. The output of PrepareInput can be used directly as the input of jGibbsLDA. Class InferenceTopicsForOrgDocs is used to inference topics of original documents, after the execution of jGibbsLDA. * Note that jGibbsLDA is a free software written by Xuan-Hieu Phan. More details can be found in http://jgibblda.sourceforge.net. <br></pre><pre><br></pre><pre>2. Installation --------------- Straightforward Java compilation can be done with the following commands: &gt; tar -xzcf wntm.tar.gz &gt; cd wntm &gt; javac *.java <br></pre><pre><br></pre><pre>3. Usage -------- &gt; java PrepareInput Example usage: &gt; java PrepareInput sample.txt ./ sample 10 * Note that constructing word network might require lots of memory, especially when the original input file is large. For example, original file size of 250MB needs 7GB memory in our experiment. In this case, one might use following command to configure the maximum memory can be used by Java. &gt; java -Xmx10g PrepareInput sample.txt ./ sample 10 <br></pre><pre><br></pre><pre>4. Output --------- When PrepareInput completes, it will output two files. The file named with suffix ".word" stores all the nodes in the word network. The file named with suffix ".adjacent" stores pseudo documents ready to be used in jGibbsLDA. <br></pre><pre><br></pre><pre>5. Inference ------------ Since WNTM models topics for a word's adjacent node list. Therefore, we need to inference the topic distribution of original documents, when jGibbsLDA finished training pseudo documents. One can use following command to do inference: &gt; java InferenceTopicsForOrgDocs to get topics of original documents, aka the .theta file for original corpus. <br></pre><pre><br></pre><pre>6. Contact ---------- If you have any problem, please feel free to contact Jichang Zhao(jichang@buaa.edu.cn) </pre>
DOI: 10.6084/m9.figshare.5572600
title: IEPS Rasch Analysis - Data Set

description: Data for the Rasch analysis of the Interdisciplinary Education Perception Scale
DOI: 10.6084/m9.figshare.5572636.v1
title: Gaze doesn't always lead steering

description: Data and analysis for Gaze doesn't always lead steering by Lehtonen et al, published in Accident Analysis and Prevention (https://doi.org/10.1016/j.aap.2018.09.026).<br>Participants drove on a motorway with an instrumented car performing a visual secondary task. See Description of the data.txt for the variables. Analysis code written in R using R Markdown format.
DOI: 10.6084/m9.figshare.5572588
title: Datasets of word network topic model

description: Abstract: This dataset holds the content of one day's micro-blogs sampled from Weibo(http://weibo.com) in the form of bags-of-words.<br>-----------------------------------------------------<br>Data Set Characteristics: TextNumber of Micro-blogs:189,223Total Number of Words:3,252,492Size of the Vocabulary:20,942Associated Tasks: short text topic modeling and etc.<br>-----------------------------------------------------<br>About Preprocessing<br>For tokenization, we use NLPIR. Stop words and those with term-frequence less than 20 were removed. Besides,words contain only one chinese-character were also removed.<br>-----------------------------------------------------<br>Data Format<br>The format of released data is setted as follows:<br>[document_1][document_2]...[document_M]<br>in which each line is one document. [document_i] is the ith document of the dataset that consists of a list of Ni words/terms.<br>[document_i] = [word_i1] [word_i2] ... [word_iNi]<br>in which all [word_ij] (i=1..M, j=1..Ni) are text strings and they are separated by the blank character.<br>-----------------------------------------------------<br>If you have any questions about the data set, please contact: jichang@buaa.edu.cn.
DOI: 10.6084/m9.figshare.5572591.v1
title: Code of word network topic model

description: <pre>1. Introduction --------------- <br></pre><pre><br></pre><pre>Class PrepareInput can convert the original documents into word co-occurrence network and re-weight it, then save it as pseudo documents. The output of PrepareInput can be used directly as the input of jGibbsLDA. Class InferenceTopicsForOrgDocs is used to inference topics of original documents, after the execution of jGibbsLDA. * Note that jGibbsLDA is a free software written by Xuan-Hieu Phan. More details can be found in http://jgibblda.sourceforge.net. <br></pre><pre><br></pre><pre>2. Installation --------------- Straightforward Java compilation can be done with the following commands: &gt; tar -xzcf wntm.tar.gz &gt; cd wntm &gt; javac *.java <br></pre><pre><br></pre><pre>3. Usage -------- &gt; java PrepareInput Example usage: &gt; java PrepareInput sample.txt ./ sample 10 * Note that constructing word network might require lots of memory, especially when the original input file is large. For example, original file size of 250MB needs 7GB memory in our experiment. In this case, one might use following command to configure the maximum memory can be used by Java. &gt; java -Xmx10g PrepareInput sample.txt ./ sample 10 <br></pre><pre><br></pre><pre>4. Output --------- When PrepareInput completes, it will output two files. The file named with suffix ".word" stores all the nodes in the word network. The file named with suffix ".adjacent" stores pseudo documents ready to be used in jGibbsLDA. <br></pre><pre><br></pre><pre>5. Inference ------------ Since WNTM models topics for a word's adjacent node list. Therefore, we need to inference the topic distribution of original documents, when jGibbsLDA finished training pseudo documents. One can use following command to do inference: &gt; java InferenceTopicsForOrgDocs to get topics of original documents, aka the .theta file for original corpus. <br></pre><pre><br></pre><pre>6. Contact ---------- If you have any problem, please feel free to contact Jichang Zhao(jichang@buaa.edu.cn) </pre>
DOI: 10.6084/m9.figshare.5572471
title: Visitas a la wikipedia página "Cataluña", septiembre-octubre 2016 y 2017

description: Visits to the Spanish wikipedia page "Cataluña" during this year and the year before. <br>
DOI: 10.6084/m9.figshare.5572474
title: bibtex-records (1).txt

description: uhmiltfgciciutyitylcutyxujhcylcuktycluithyvukfuyctikuyc;ityvyjryuktitkyu
DOI: 10.6084/m9.figshare.5572558
title: Datasets for the paper: SDHRHA

description: Data of laboratory and in-the-wild studies for pattern mining approaches to discover periodic-frequent routines of people at home.
DOI: 10.6084/m9.figshare.5572471.v1
title: Visitas a la wikipedia página "Cataluña", septiembre-octubre 2016 y 2017

description: Visits to the Spanish wikipedia page "Cataluña" during this year and the year before. <br>
DOI: 10.6084/m9.figshare.5572474.v1
title: bibtex-records (1).txt

description: uhmiltfgciciutyitylcutyxujhcylcuktycluithyvukfuyctikuyc;ityvyjryuktitkyu
DOI: 10.6084/m9.figshare.5572558.v1
title: Datasets for the paper: SDHRHA

description: Data of laboratory and in-the-wild studies for pattern mining approaches to discover periodic-frequent routines of people at home.
DOI: 10.6084/m9.figshare.5572456
title: SnPM13 Associated files

description: Associated documents for the release of SnPM13.
DOI: 10.6084/m9.figshare.5572408
title: Spontaneous abortion as a response to reproductive conflict in the banded mongoose

description: This is the dataset used for analyses in the paper Inzani et al. Spontaneous abortion as a response to reproductive conflict in the banded mongoose. <br><br>Please note that the manuscript is currently under review, and the data are confidential pending publication of the paper. <br>
DOI: 10.6084/m9.figshare.5572399
title: OpenCitations Corpus provenance data of all the identifiers, archived on 2017-10-25

description: This archive contains the dump of the OpenCitations Corpus (OCC, http://opencitations.net) provenance data about identifiers, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.
DOI: 10.6084/m9.figshare.5572405
title: OpenCitations Corpus dataset of all the resource embodiments, archived on 2017-10-25

description: This archive contains the dump of the OpenCitations Corpus (OCC, http://opencitations.net) dataset about resource embodiments, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.
DOI: 10.6084/m9.figshare.5572408.v2
title: Spontaneous abortion as a response to reproductive conflict in the banded mongoose

description: This is the dataset used for analyses in the paper Inzani et al. Spontaneous abortion as a response to reproductive conflict in the banded mongoose. <br><br>Please note that the manuscript is currently under review, and the data are confidential pending publication of the paper. <br>
DOI: 10.6084/m9.figshare.5572384
title: OpenCitations Corpus provenance data of all the agent roles, archived on 2017-10-25

description: This archive contains the dump of the OpenCitations Corpus (OCC, http://opencitations.net) provenance data about author roles, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.
DOI: 10.6084/m9.figshare.5572396
title: OpenCitations Corpus dataset of all the agent roles, archived on 2017-10-25

description: This archive contains the dump of the OpenCitations Corpus (OCC, http://opencitations.net) dataset about author roles, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.
DOI: 10.6084/m9.figshare.5572399.v1
title: OpenCitations Corpus provenance data of all the identifiers, archived on 2017-10-25

description: This archive contains the dump of the OpenCitations Corpus (OCC, http://opencitations.net) provenance data about identifiers, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.
DOI: 10.6084/m9.figshare.5572405.v1
title: OpenCitations Corpus dataset of all the resource embodiments, archived on 2017-10-25

description: This archive contains the dump of the OpenCitations Corpus (OCC, http://opencitations.net) dataset about resource embodiments, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.
DOI: 10.6084/m9.figshare.5572396.v1
title: OpenCitations Corpus dataset of all the agent roles, archived on 2017-10-25

description: This archive contains the dump of the OpenCitations Corpus (OCC, http://opencitations.net) dataset about author roles, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.