cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5481541.v4
title: DFT study of the reaction mechanism of the Das/Chechik/Marek synthesis of stereodefined quaternary carbon centers

description: Inputs, outputs, and orbitals accompanying the manuscript<br><br>Bernardo and Silva (2017) DFT study of the reaction mechanism of the Das/Chechik/Marek synthesis of stereodefined quaternary carbon centers (in preparation )<br>
DOI: 10.6084/m9.figshare.5481541.v3
title: DFT study of the reaction mechanism of the Das/Chechik/Marek synthesis of stereodefined quaternary carbon centers

description: Inputs, outputs, and orbitals accompanying the manuscript<br><br>Bernardo and Silva (2017) DFT study of the reaction mechanism of the Das/Chechik/Marek synthesis of stereodefined quaternary carbon centers (in preparation )<br>
DOI: 10.6084/m9.figshare.13439894.v3
title: FLACK: Counterexample-Guided Fault Localization for Alloy Models

description: Replication package for FLACK in ICSE21
DOI: 10.6084/m9.figshare.13439894.v2
title: FLACK: Counterexample-Guided Fault Localization for Alloy Models

description: Replication package for FLACK in ICSE21<br>Each component in /flack/ directory:<br>/benchmark/alloy/ : Alloy models from AlloyFL/benchmark/large/ : large real-world Alloy models/src/alloy/ : modified Alloy by adding Pardinus to KodKod/src/flack/ : source code of FLACK/libs/ : sat solvers libraries<br>Running instruction:(1) Docker 1. docker build -t flack . 2. docker run -it flack 3. java -cp ./libs/*:./target/flack-1.0-jar-with-dependencies.jar -f benchmark/alloy/addr1.als -m 5(2) Build from source 1. Install Java8 and Maven 2. mvn clean package 3. java -cp ./libs/*:./target/flack-1.0-jar-with-dependencies.jar -f benchmark/alloy/addr1.als -m 5<br> -f, path to the Alloy model-m, max number of pairs of counterexample and sat instance-a, if the path is a directory, run FLACK on all models in the directory-w, write results to csv<br>
DOI: 10.6084/m9.figshare.13416227.v5
title: KHAP

description: KHAP
DOI: 10.6084/m9.figshare.13416227.v4
title: KHAP

description: KHAP
DOI: 10.6084/m9.figshare.13387082.v2
title: Multi-temporal LCZ maps for Canadian functional urban areas

description: Multi-temporal Local Climate Zone maps for seven functional urban areas (Calgary, Edmonton, Halifax, Montreal, Toronto, Vancouver, Winnipeg), and seven census years (1986, 1991, 1996, 2001, 2006, 2011, 2016). Regions of interest are defined by each cities' functional urban area, and the LCZ maps are available per city and census year, on a 100 m spatial resolution.
DOI: 10.6084/m9.figshare.13383176.v2
title: 2020 01 30 VID00018 Original Frames

description: Relevant frames of VID00018 taken on 30 January 2020. Frames are extracted using Cisco FlipShare software distributed with the Cisco Flip UltraHD 720p digital video recorder used to record the video. This folder includes all frames with the animal in view, plus the adjacent before and after frames that just miss having the animal in view. The camera is panned slowly to the right during frames 0 01 40-20 through 0 01 43-35, and then is panned back to the left at a quicker rate during frames 0 02 00-25 through 0 02 02-25. The black colored primate is seen behind the whitetail deer that it has taken as prey. Frame 0 01 40-45 captures fine details of the face with a nearly unoccluded view. Additional frames capture a second animal seen in a different direction, with similar left to right then back to the left panning.
DOI: 10.6084/m9.figshare.13383176.v1
title: 2020 01 30 VID00018 Original Frames

description: Relevant frames of VID00018 taken on 30 January 2020. Frames are extracted using Cisco FlipShare software distributed with the Cisco Flip UltraHD 720p digital video recorder used to record the video. This folder includes all frames with the animal in view, plus the adjacent before and after frames that just miss having the animal in view. The camera is panned slowly to the right during frames 0 01 40-20 through 0 01 43-35, and then is panned back to the left at a quicker rate during frames 0 02 00-25 through 0 02 02-25. The black colored primate is seen behind the whitetail deer that it has taken as prey. Frame 0 01 40-45 captures fine details of the face with a nearly unoccluded view.
DOI: 10.6084/m9.figshare.13383215.v2
title: 2020 01 30 VID00018 Stabilized Cropped Frames

description: Stabilized cropped frames of VID00018 taken on 30 January 2020. Original pixel data are retained as frames are cropped relative to anchor points on the foreground tree seen to the left of the animal. The color streaks are a product of the custom Java code used to extract the cropped images. The code pads the pixel values of the edge into the space that is out of view from the camera. The images were cropped for a 5 x 7 aspect ratio. The large black primate is seen just behind the whitetail deer it has taken as prey. Additional frames capture a second animal seen in a different direction, with similar left to right then back to the left panning
DOI: 10.6084/m9.figshare.10718282.v9
title: Dataset for omicwas

description: The omicwas package implements ridge regression for cell-type-specific EWAS and differential gene expression. This dataset includes R notebooks and R data files for using omicwas.
DOI: 10.6084/m9.figshare.10718282.v8
title: Dataset for omicwas

description: The omicwas package implements ridge regression for cell-type-specific EWAS and differential gene expression. This dataset includes R notebooks and R data files for using omicwas.
DOI: 10.6084/m9.figshare.12130980.v64
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130980.v63
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12904127.v1
title: Test_Files

description: From the group of selected participant recordings (n=400), 200 were randomly allocated to the validation group using the random sampling function in R. The validation (test) group was used to validate DT prediction from
DOI: 10.6084/m9.figshare.13484601.v1
title: Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screening

description: This data accompanies a paper of the same title, <i>Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screening</i>. This data set includes the subjects' de-identified data from the study. The overall results of the study showed the ability of a neural network to differentiate between osteoporotic and healthy subjects was up to 95% specificity and 76% sensitivity.The CSV files are the output of the network analyzer, the MAT files are the same data after de-imbedding. Subject metadata is stored in list.mat (no identifying information is included).
DOI: 10.6084/m9.figshare.13488762.v1
title: S1 Table

description: Results obtained from all samples
DOI: 10.6084/m9.figshare.7119749.v1
title: Enrichment of CRISPR and IP-MS Data

description: All files required to do determine the overlap between proteins that are differentially bound in the IP-MS data and differentially dependent in the CRISPR-cas9 data as well as calculate gene set enrichment of the overlapping set.
DOI: 10.6084/m9.figshare.7111217.v2
title: Reaction times to stimuli (Leigh Woods)

description: Description for column titles:pptnum - participant identifierhardest - optimised for easiest (0) or hardest (1) to see colourprotan - dichromat (1) or trichromat (0) representationrtime - reaction time in secondsx51keycode63 - which button was pressed (51: left shift, 63: right shift)R - stimuli colour red channelG - stimuli colour green channelB - stimuli colour blue channelprotanR - dichromat stimuli colour channel 1protanG - dichromat stimuli colour channel 2protanB - dichromat stimuli colour channel 3rcentre - vertical target location on screenccentre - horizontal target location on screencoverage - percentage of target visible (1 = 100%)foregroundimage - foreground image (unused)backroundimage - background imageflipped - was the background image horizontally flipped (0: no, 1: yes)
DOI: 10.6084/m9.figshare.7111217.v1
title: Reaction times to stimuli (Leigh Woods)

description: Description for column titles:pptnum - participant identifierhardest - optimised for easiest (0) or hardest (1) to see colourprotan - dichromat (1) or trichromat (0) representationrtime - reaction time in millisecondsx51keycode63 - which button was pressed (51: left shift, 63: right shift)R - stimuli colour red channelG - stimuli colour green channelB - stimuli colour blue channelprotanR - dichromat stimuli colour channel 1protanG - dichromat stimuli colour channel 2protanB - dichromat stimuli colour channel 3rcentre - vertical target location on screenccentre - horizontal target location on screencoverage - percentage of target visible (1 = 100%)foregroundimage - foreground image (unused)backroundimage - background imageflipped - was the background image horizontally flipped (0: no, 1: yes)
DOI: 10.6084/m9.figshare.13493721.v2
title: 34 new Lak phage genomes

description: The new Lak phage genomes reconstructed from animal metagenomic datasets.
DOI: 10.5281/zenodo.5946923
title: Dati estratti da "Epidemia COVID-19", bollettino dell'Istituto Superiore di Sanità (ISS)

description: “EPIDEMIA COVID-19” è un bollettino prodotto dall’Istituto Superiore di Sanità (ISS) che riporta i dati della sorveglianza integrata concernente l’infezione da virus SARS-CoV-2. Si legge nella nota introduttiva che “Il conteggio dei casi viene effettuato sommando tutti i casi di infezione da virus SARS-CoV2 confermati tramite test molecolari e antigenici, e segnalati al sistema di sorveglianza attraverso una piattaforma web dedicata. Il database nazionale è quindi la somma dei casi presenti nei 21 sistemi informativi regionali/PPAA costruiti ad hoc per la sorveglianza COVID-19”. Seguono altre delucidazioni. Si riportano grafici da dati estratti dai rapporti, concernenti casistica su persone di età ≥ 12 anni, ricoveri e vittime. Le tabelle indicano anche lo stato vaccinale e la fascia d’età. Si presti attenzione che le classi relative alla vaccinazione sono state ridefinite a seconda dei Decreti Legge del Governo.
DOI: 10.6084/m9.figshare.13493721.v1
title: 34 new Lak phage genomes

description: The new Lak phage genomes reconstructed from animal metagenomic datasets.
DOI: 10.6084/m9.figshare.8794859.v1
title: Imine formation from neat aromatic amines and benzaldehyde : optimizations using (aug)-pcseg-1 basis set and D3-BJ, single points using pcseg2 and DSD-BLYP(D3-BJ)

description: Input and output files from quantum chemical computation of the reaction mechanism
DOI: 10.6084/m9.figshare.9893987.v3
title: Imine formation under catalysis by protonated amine: optimizations using (aug)-pcseg-1 basis set and D3-BJ, single points using pcseg2 and DSD-BLYP(D3-BJ)

description: Input and output files from quantum chemical computation of the reaction mechanism
DOI: 10.6084/m9.figshare.12272360.v7
title: 2bRAD-M simulated and sequenced datasets 1

description: 2bRAD-M paper related datasets16S, WMS and 2bRAD-M for MOCK_CAS and MOCK-MSA1002, and 2bRAD-M for FFPE.
DOI: 10.6084/m9.figshare.12272360.v6
title: 2bRAD-M simulated and sequenced datasets 1

description: 2bRAD-M paper related datasets16S, WMS and 2bRAD-M for MOCK_CAS and MOCK-MSA1002, and 2bRAD-M for FFPE.
DOI: 10.6084/m9.figshare.12756020.v5
title: The association of smoking status with SARS-CoV-2 infection, hospitalisation and mortality from COVID-19: A living rapid evidence review and Bayesian meta-analyses (version 10) - Data

description: Data extraction from identified articles reporting on SARS-CoV-2 infection, COVID-19 hospitalisation, severity or mortality.<br>Datset has been updated to v10 with searches up to the 15th of December 2020<br><br>The data is accessed through the reference displayed below<br>The article published in <i>Addiction</i> is available here:https://doi.org/10.1111/add.15276
DOI: 10.6084/m9.figshare.13515170.v1
title: STOTEN-D-20-24939-data

description: This dataset is only for the Science of total environment
DOI: 10.6084/m9.figshare.13284452.v1
title: Model output (Wu_and_Tsai_2020)

description: Climatological model output used in Wu and Tsai (2020) “Impact of orbitally-driven seasonal insolation changes on Afro-Asian summer monsoons through the Holocene”
DOI: 10.48370/ofd/txwbah/duwqtj
title: Betula pendula_liście - ekstrakt substancji lotnych.pdf

description: :unav
DOI: 10.48370/ofd/txwbah
title: Betula pendula_liście - ekstrakt substancji lotnych

description: ekstrakt substancji lotnych
DOI: 10.6084/m9.figshare.7578218.v1
title: Cassava growers behaviour and planting material movement questionnaire data from Zambia 20015 and 2017

description: Cassava growers behaviour and planting material movement questionnaire data from Zambia 20015 and 2017.<br>Zambia_cassava_growers_survey_rawdata.csv contains raw data with specific geographic coordinates and names of respondents removed.<br>Zambia Questionnaire.docx represents questionnaire template with sample answers.<br>
DOI: 10.6084/m9.figshare.5100619.v3
title: Abies cilicica chorology

description: Distribution map of Cilician fir (<i>Abies cilicica</i>)<br><br>These maps were produced by combining numerous and heterogeneous data collected from atlas monographs providing complete species distribution maps, from national to regional atlases, occurrence geo-databases, scientific and grey literature.<br>The maps were created using ESRI shapefiles (*.shp, *.shx, *.dbf, *.prj files) archived in the ZIP file. Species range is mapped with polygon features (name suffix "plg"), which define continuous areas of occupancy of the species, and with point features (name suffix "pnt"), which identify more fragmented and isolated populations. If synanthropic occurrences are reported outside the species natural range, additional point and/or polygon shapefiles are also present (suffix "syn"). <br>Polygon borders delimiting species ranges are generalized across the mainland and sea boundaries. This offers the possibility to mask sea areas or to clip and extract the terrestrial range parts using GIS data layers of the users' choice. An additional version of polygon ranges are clipped with a coastline (name suffix "clip"), which have been derived from Natural Earth dataset "Admin 0 - Countries" 1:50M version 4.1.0 (https://www.naturalearthdata.com). <br><br>Please cite as:<br>Caudullo, G., Welk, E., San-Miguel-Ayanz, J., 2017. Chorological maps for the main European woody species. Data in Brief 12, 662-666. DOI: doi.org/10.1016/j.dib.2017.05.007 <br><br>Additional information and used references are on 'supplementary materials' document:<br>https://doi.org/10.6084/m9.figshare.5091901<br><br>Chorological maps are part of the "European Atlas of Forest Tree Species" project:<br>https://w3id.org/mtv/FISE-Comm/v01
DOI: 10.6084/m9.figshare.12656165.v4
title: COVID-19 Case Reports in China

description: Chinese prefectural level governments started to report daily confirmed COVID-19 cases online, starting from January 2020. The disclosures may contain the mobility, potential exposure scenario, epidemiological characteristics, and other useful information of individual cases. We organized a group of content coders since early March 2020, kept monitoring the information updates, manually extracted useful information from the public disclosures, and compiled these datasets.We welcome any form of collaborations with us and non-commercial reuse of our dataset. We highly encourage interested parties to examine the data, report errors in our coding, and help us to keep the data updated.The detailed data description can be found on SSRN preprint server https://dx.doi.org/10.2139/ssrn.3705815.
DOI: 10.6084/m9.figshare.12656165.v3
title: COVID-19 Case Reports in China

description: Chinese prefectural level governments started to report daily confirmed COVID-19 cases online, starting from January 2020. The disclosures may contain the mobility, potential exposure scenario, epidemiological characteristics, and other useful information of individual cases. We organized a group of content coders since early March 2020, kept monitoring the information updates, manually extracted useful information from the public disclosures, and compiled these datasets.We welcome any form of collaborations with us and non-commercial reuse of our dataset. We highly encourage interested parties to examine the data, report errors in our coding, and help us to keep the data updated.The detailed data description can be found on SSRN preprint server https://dx.doi.org/10.2139/ssrn.3705815.
DOI: 10.6084/m9.figshare.12949295.v1
title: SNP data of durum wheat panel used for GWAS analyses

description: This data includes SNP data discovered by GBS after and before imputation.
DOI: 10.6084/m9.figshare.13203194.v1
title: Supplementary Data used for GWAS analyses

description: Supplementary Data 1: is the BLUPs of field response (phenotype data) to stem across five environments. The Taxa is indicating the genotype(lines) entry number preceded by DUR_ for durum wheat.<br>Supplementary Data 2: is the the SNP name, chromosome number and position in basepairs as per the format of the software used for analysis.<br>Supplementary Data 3: is the numeric format of the SNP data used for analysis.
DOI: 10.6084/m9.figshare.5481541.v6
title: DFT study of the reaction mechanism of the Das/Chechik/Marek synthesis of stereodefined quaternary carbon centers

description: Inputs, outputs, and orbitals accompanying the manuscript<br><br>Bernardo and Silva (2017) DFT study of the reaction mechanism of the Das/Chechik/Marek synthesis of stereodefined quaternary carbon centers (in preparation )<br>
DOI: 10.6084/m9.figshare.5481541.v5
title: DFT study of the reaction mechanism of the Das/Chechik/Marek synthesis of stereodefined quaternary carbon centers

description: Inputs, outputs, and orbitals accompanying the manuscript<br><br>Bernardo and Silva (2017) DFT study of the reaction mechanism of the Das/Chechik/Marek synthesis of stereodefined quaternary carbon centers (in preparation )<br>
DOI: 10.6084/m9.figshare.13520078.v1
title: Root endophytic bacterial community

description: Root endophytic bacterial communities in <i>K. pygmaea</i> and <i>E. nutans</i> along an altitude gradient in the natural grassland of Qinghai-Tibet Plateau, China.
DOI: 10.6084/m9.figshare.13072178.v1
title: Alignment file for Annulotesta cochlephila

description: This is the alignment file for <i>Annulotesta cochlephila</i>, which was used in the study on "Taxonomic study of a new green alga, <i>Annulotesta cochlephila</i> gen. et sp. nov. (Kornmanniaceae, Ulvales, Ulvophyceae), growing on the shells of door snails".<br>In this study, we discovered green algae growing on the shells of six species of door snails (Clausiliidae) from nine localities in Japan. We propose a new genus and new species of shell-attached green alga, <i>Annulotesta cochlephila</i> based on the results of morphological observation and molecular phylogenetic analysis. There are very few reports of algae that grow on land snails. Interestingly, we found all algae forming visible colonies on the shells of different clausiliid species from different localities were the same species. This finding indicates the presence of specificity between the alga and clausiliid shells.<br><br>