cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/acmm7a/mt8kxq
title: ave_Br_GONG.dat

description: :unav
DOI: 10.7910/dvn/acmm7a/ghk6ab
title: read_me.txt

description: :unav
DOI: 10.7910/dvn/acmm7a/zygky3
title: 2018090800_PM_SP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/u1vlf0
title: ave_Br_ADAPT.dat

description: :unav
DOI: 10.7910/dvn/acmm7a/b0r7y6
title: insitu_Br_HMI.dat

description: :unav
DOI: 10.7910/dvn/acmm7a/xyozs8
title: 2018030600_PM_SP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/yawphb
title: 2018090800CH_SPH.PLT

description: :unav
DOI: 10.7910/dvn/acmm7a/ieku2q
title: 2018060500CH_SPH.PLT

description: :unav
DOI: 10.7910/dvn/acmm7a/ozfhky
title: 2018060500CH_SPH.PLT

description: :unav
DOI: 10.7910/dvn/acmm7a/yiktim
title: ave_Br_HMI.dat

description: :unav
DOI: 10.7910/dvn/acmm7a/0uhtir
title: 2018060500_PM_NP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/hf5ewm
title: read_me.txt

description: :unav
DOI: 10.7910/dvn/acmm7a/qxn0lm
title: read_me.txt

description: :unav
DOI: 10.7910/dvn/acmm7a/qfeqz2
title: HS_PS_adapt.dat

description: :unav
DOI: 10.7910/dvn/acmm7a/ybcber
title: 2018090800_SS_DS.PLT

description: :unav
DOI: 10.7910/dvn/acmm7a/rd0mmq
title: 2018090800_PM_SP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/nsjcoz
title: 2018060500_PM_SP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/rur0qs
title: 2018030600_PM_SP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/jblvia
title: 2018030600CH_SPH.PLT

description: :unav
DOI: 10.7910/dvn/acmm7a/yyp2cb
title: read_me.txt

description: :unav
DOI: 10.7910/dvn/acmm7a/gjczat
title: read_me.txt

description: :unav
DOI: 10.7910/dvn/acmm7a/almrud
title: CH_area_GONG.dat

description: :unav
DOI: 10.7910/dvn/acmm7a/daoli8
title: 2018090800_PM_NP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/hhd2ms
title: 2018030600_SS_DS.PLT

description: :unav
DOI: 10.7910/dvn/acmm7a/mcovd1
title: 2018030600_PM_NP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/lgzmcu
title: 2018060500_SS_DS.PLT

description: :unav
DOI: 10.7910/dvn/acmm7a/en7xxo
title: 2018030600_PM_NP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/m3u1fz
title: 2018090800CH_SPH.PLT

description: :unav
DOI: 10.7910/dvn/acmm7a/ltc1xq
title: HS_PS_HMI.dat

description: :unav
DOI: 10.7910/dvn/acmm7a/1vsqcl
title: read_me.txt

description: :unav
DOI: 10.7910/dvn/acmm7a/vxiufd
title: 2018090800_PM_SP_DIST.plt

description: :unav
DOI: 10.7910/dvn/acmm7a/xpmitc
title: insitu_Br_mapped_ob.dat

description: :unav
DOI: 10.5281/zenodo.3893395
title: Research data supporting for Stress-induced amorphization triggers deformation in the lithospheric mantle

description: Original TEM micrographs used to prepare the figures of the article
DOI: 10.5281/zenodo.3891940
title: Data for "The Origin of the Bimodal Distribution of Magnetic Fields in Early-type Stars"

description: Accompanying data and inlists for Jermyn & Cantiello 2020 (ApJ). Models have been computed using MESA revision 11701. A Jupyter notebook is provided to reproduce the figures in the main text.
DOI: 10.5281/zenodo.3891939
title: Data for "The Origin of the Bimodal Distribution of Magnetic Fields in Early-type Stars"

description: Accompanying data and inlists for Jermyn & Cantiello 2020 (ApJ). Models have been computed using MESA revision 11701. A Jupyter notebook is provided to reproduce the figures in the main text.
DOI: 10.5281/zenodo.3892979
title: DeepND - Spatio-temporal Brain Co-Expression Networks

description: DeepND - Spatio-temporal Brain Co-Expression Networks
DOI: 10.5281/zenodo.3892978
title: DeepND - Spatio-temporal Brain Co-Expression Networks

description: DeepND - Spatio-temporal Brain Co-Expression Networks
DOI: 10.21227/ht13-dt11
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Also: Coronavirus (COVID-19) Tweets Dataset (180+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.21227/n4x9-bh26
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Also: Coronavirus (COVID-19) Tweets Dataset (180+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.5281/zenodo.3893414
title: Original data for: Deracemization of Au38 Clusters: Breaking the Equal Status and Dynamic Inversion of Enantiomers

description: Original data for Figures 2, 6, S1, S2, S3, S4, S5, S6, S7, S8, S9
DOI: 10.5281/zenodo.3893413
title: Original data for: Deracemization of Au38 Clusters: Breaking the Equal Status and Dynamic Inversion of Enantiomers

description: Original data for Figures 2, 6, S1, S2, S3, S4, S5, S6, S7, S8, S9
DOI: 10.5281/zenodo.3364285
title: Supplementary materials for "Spatial runoff updating based on hydrologic system differential response for flood forecasting"

description: Supplementary material 1: Observations in the BSK basin including precipitation, evaporation and discharge. Supplementary material 2: Observations in the SW basin including precipitation, evaporation and discharge. Supplementary material 3: Results for floods 20071006 in the BSK basin Supplementary material 4: Results for floods 19890526 in the SW basin Supplementary material 5: Results with different lead time in the BSK and SW basins
DOI: 10.5281/zenodo.3856442
title: Supplementary materials for "Spatial runoff updating based on hydrologic system differential response for flood forecasting"

description: Supplementary material 1: Observations in the BSK basin including precipitation, evaporation and discharge. Supplementary material 2: Observations in the SW basin including precipitation, evaporation and discharge. Supplementary material 3: Results for floods 20071006 in the BSK basin Supplementary material 4: Results for floods 19890526 in the SW basin Supplementary material 5: Results with different lead time in the BSK and SW basins
DOI: 10.5281/zenodo.3893412
title: Improved Global Pressure and Temperature(IGPT) Model

description: This is the corresponding code and dataset of the paper " Refining the Empirical Global Pressure and Temperature Model with the ERA5 Reanalysis Data and Radiosonde Data".
DOI: 10.5281/zenodo.3554938
title: Improved Global Pressure and Temperature(IGPT) Model

description: This is the corresponding code and dataset of the paper " Refining the Empirical Global Pressure and Temperature Model with the ERA5 Reanalysis Data and Radiosonde Data".
DOI: 10.5281/zenodo.3893389
title: London Smart Meters Dataset (without Missing Values)

description: Kaggle London Smart Meters dataset contains 5560 half hourly time series that represent the energy consumption readings of London households in kilowatt hour (kWh) from November 2011 to February 2014. The original dataset contains missing values and they have been simply replaced by zeros.
description: {"references": ["Kaggle, 2019. Smart meter data from london area. URL https://www.kaggle.com/jeanmidev/smart-meters-in-london"]}
DOI: 10.5281/zenodo.3888433
title: The NMR dataset for manuscript nl-2020-017186

description: The NMR dataset for the determination of enthalpy change and entropy change of CNT-polymer association
DOI: 10.5281/zenodo.3888432
title: The NMR dataset for manuscript nl-2020-017186

description: The NMR dataset for the determination of enthalpy change and entropy change of CNT-polymer association
DOI: 10.15161/oar.it/30426
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5519/qd.iu6pwl9r
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.5519/qd.t1u6cp5j
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4559620 records)
DOI: 10.5519/qd.qmxp8x50
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (799699 records)
DOI: 10.25625/ame2u2/nygz0b
title: Coffee Lectures Wintersemester,2020, 21, 2020 Screen Res-konvertiert.pdf

description: :unav
DOI: 10.25625/ame2u2
title: Coffee Lectures zum Forschungsdatenmanagement an der SUH, Wintersemester 2020/21 (digital).

description: Poster: Coffee Lecture Forschungsdatenmanagement
DOI: 10.5281/zenodo.3893504
title: KDD Cup Dataset (with Missing Values)

description: This dataset was used in the KDD Cup 2018 forecasting competition. It contains long hourly time series representing the air quality levels in 59 stations in 2 cities: Beijing (35 stations) and London (24 stations) from 01/01/2017 to 31/03/2018. The air quality level is represented in multiple measurements such as PM2.5, PM10, NO2, CO, O3 and SO2. The dataset uploaded here contains 282 hourly time series which have been categorized using city, station name and air quality measurement.
description: {"references": ["Kdd cup 2018. URL https://www.kdd.org/kdd2018/kdd-cup"]}
DOI: 10.5281/zenodo.3893513
title: KDD Cup Dataset (without Missing Values)

description: This dataset was used in the KDD Cup 2018 forecasting competition. It contains long hourly time series representing the air quality levels in 59 stations in 2 cities: Beijing (35 stations) and London (24 stations) from 01/01/2017 to 31/03/2018. The air quality level is represented in multiple measurements such as PM2.5, PM10, NO2, CO, O3 and SO2. The dataset uploaded here contains 282 hourly time series which have been categorized using city, station name and air quality measurement. The original dataset contains missing values and they have been simply replaced by zeros.
description: {"references": ["Kdd cup 2018. URL https://www.kdd.org/kdd2018/kdd-cup"]}
DOI: 10.5281/zenodo.3786404
title: Electromagnetic data (FDEM and ERT) collected in the Venice coastland (Zennare basin)

description: FDEM and ERT data collected southern of the Venice lagoon (Italy) in the Zennare basin in 2019-2020. FDEM_ZENNARE37.csv: Raw output of Quadrature and Inphase values for the 6 frequencies adopted with the GEM2 FDEM probe.<br> ERT_ROUGHoutput_Zennare.dat: Apparent resistivity data as retrieved with the 48 channels Syscal Pro georesistivimeter ERT.
DOI: 10.5281/zenodo.3786403
title: Electromagnetic data (FDEM and ERT) collected in the Venice coastland (Zennare basin)

description: FDEM and ERT data collected southern of the Venice lagoon (Italy) in the Zennare basin in 2019-2020. FDEM_ZENNARE37.csv: Raw output of Quadrature and Inphase values for the 6 frequencies adopted with the GEM2 FDEM probe.<br> ERT_ROUGHoutput_Zennare.dat: Apparent resistivity data as retrieved with the 48 channels Syscal Pro georesistivimeter ERT.
DOI: 10.5281/zenodo.3893535
title: KDD Cup Dataset (with Missing Values)

description: This dataset was used in the KDD Cup 2018 forecasting competition. It contains long hourly time series representing the air quality levels in 59 stations in 2 cities: Beijing (35 stations) and London (24 stations) from 01/01/2017 to 31/03/2018. The air quality level is represented in multiple measurements such as PM2.5, PM10, NO2, CO, O3 and SO2. The dataset uploaded here contains 270 hourly time series which have been categorized using city, station name and air quality measurement.
description: {"references": ["Kdd cup 2018. URL https://www.kdd.org/kdd2018/kdd-cup"]}
DOI: 10.5281/zenodo.3893536
title: KDD Cup Dataset (without Missing Values)

description: This dataset was used in the KDD Cup 2018 forecasting competition. It contains long hourly time series representing the air quality levels in 59 stations in 2 cities: Beijing (35 stations) and London (24 stations) from 01/01/2017 to 31/03/2018. The air quality level is represented in multiple measurements such as PM2.5, PM10, NO2, CO, O3 and SO2. The dataset uploaded here contains 270 hourly time series which have been categorized using city, station name and air quality measurement. The original dataset contains missing values and they have been simply replaced by zeros.
description: {"references": ["Kdd cup 2018. URL https://www.kdd.org/kdd2018/kdd-cup"]}
DOI: 10.5281/zenodo.3893443
title: Neural representation of threat features and behavior in a human approach-avoidance conflict anxiety task: Unthresholded SPM activation maps

description: This dataset includes group-level results from multifold parametric analyses of a high-resolution fMRI approach-avoidance conflict anxiety task (n = 19). SPM Results are divided in primary<br> analyses using a 8 mm FWHM smoothing kernel, and secondary (localization)<br> anaylses using a 4 mm FWHM kernel. Parametric modulators of BOLD activation were set up with serial orthogonalization in the following order:<br> P1: TP, TM, TP x TM<br> P2: A, TP, TM, A x TP, A x TM, TP x TM, A x TP x TM<br> P3: Two separate analyses for approach and avoidance trials. Order of modulators as in P1. Abbreviations:<br> TP = threat probability<br> TM = threat magnitude<br> A = action (approach / avoidance) The study is reported in the following reference:<br> Abivardi A.*, Khemka S.*, Bach D.R. (2020) Neural representation of threat features<br> and behavior in a human approach-avoidance conflict anxiety task (submitted)
DOI: 10.25430/researchdata.cab.unipd.it.00000357
title: Tele-endo during covid-19 outbreak in Padova

description: The analyses of Outpatients visits in the Endocrine Unit during COVID-19 outbreak.
DOI: 10.21227/7che-9135
title: A proposal to adjust the time-keeping systems to reduce emissions – A case study on USA

description: The integrated power grid in USA is the largest in the world with the highest hourly power demand. In 2019, the highest hourly demand was more than 704 GW and the lowest hourly demand was less than 340 GW. The average daily variation in hourly demand is about 40% with respect to the highest hourly demand. A mathematical model is used to reduce the differential between peak and off-peak electrical power demand in this territory. The reduction is achieved with 5 time zones set up across US 48 states such that time difference between Far East and Far west is 4 hours. For the territory of US 48 states, it is demonstrated that the differential between peak and off-peak electrical power demand is reduced by 3% to 12%. This paper attempts to quantify the reduction in cycling of Natural Gas thermal power plant operation and emissions with restructuring of time zones in US 48 states.
DOI: 10.21227/fx97-qs33
title: A proposal to adjust the time-keeping systems to reduce emissions – A case study on USA

description: The integrated power grid in USA is the largest in the world with the highest hourly power demand. In 2019, the highest hourly demand was more than 704 GW and the lowest hourly demand was less than 340 GW. The average daily variation in hourly demand is about 40% with respect to the highest hourly demand. A mathematical model is used to reduce the differential between peak and off-peak electrical power demand in this territory. The reduction is achieved with 5 time zones set up across US 48 states such that time difference between Far East and Far west is 4 hours. For the territory of US 48 states, it is demonstrated that the differential between peak and off-peak electrical power demand is reduced by 3% to 12%. This paper attempts to quantify the reduction in cycling of Natural Gas thermal power plant operation and emissions with restructuring of time zones in US 48 states.
DOI: 10.5281/zenodo.3893537
title: DATASET - Baseline data LIFT Synthesis Writing project

description: Dataset with data on text quality, keystroke logging process measures, and writer characteristics. This dataset serves as a baseline for studies on synthesis writing.
DOI: 10.5281/zenodo.3893571
title: DATASET - Intervention: process-oriented feedback on synthesis writing

description: Dataset corresponding to an intervention study carried out within the LIFT-project on synthesis writing. The dataset contains product (text quality) and process (KSL measures) data of 65 participants.
DOI: 10.5281/zenodo.3893570
title: DATASET - Intervention: process-oriented feedback on synthesis writing

description: Dataset corresponding to an intervention study carried out within the LIFT-project on synthesis writing. The dataset contains product (text quality) and process (KSL measures) data of 65 participants.
DOI: 10.5281/zenodo.3893555
title: Database_of_negative_coordinators

description: A database of negative coordinators in a sample of 250 languages.
DOI: 10.5281/zenodo.3885088
title: Genomic sequences and annotations for Solanum lycopersicum, Solanum pennellii and Solanum habrochaites

description: <strong>Genome sequences</strong> These are the different genome references (fasta formats) available for: <em>Solanum lycopersicum</em> (assemblies 3.0 and 4.0): S_lycopersicum_chromosomes.3.00.fa.tar.gz S_lycopersicum_chromosomes.4.00.fa.tar.gz <em>Solanum pennellii </em>(one version only from Bolger et al., 2014) : Spenn.fasta.tar.gz <em>Solanum habrochaites</em> LA1777 (technology hotel project 2018): LA1777.final.fasta <em>Solanum habrochaites</em> PI127826 (technology hotel project 2018): PI127826.final.fasta <em>Solanum</em> <em>habrochaites</em> LYC4 (from the paper of Aflitos et al. 2014. 3rd assembly version): S_habrochaites_LYC4... <em>Solanum arcanum</em> LA2172 (from the paper of Aflitos et al. 2014. 3rd assembly version): LA2172.fasta.tar.gz <em>Solanum chilense</em> LA3111 (from the paper of Stam et al. 2019, NCBI assembly ASM601370v1): LA3111.fasta.tar.gz The two genome assemblies of S. habrochaites LA1777 and PI127826 were obtained through a combination of 10X Linked-Reads and BioNano Optical Mapping. This sequencing has been funded by the DTL Technology Hotel 2018 funding scheme. <strong>Transcriptomes and proteomes</strong> <em>Solanum lycopersicum (assembly</em> 4.0): Transcriptome: ITAG4.0_cDNA.fasta Proteome: ITAG4.0_proteins.fasta <em>Solanum pennellii </em>(one version only from Bolger et al., 2014): Transcriptome: Spenn-v2-cds-annot.fa <strong>Genome annotations files</strong> <em>Solanum lycopersicum</em> Gene File Format: ITAG4.0_gene_models.gff MapMan annotation: S_lycopersicum_ITAG4.0_mapping_Mercator_v.3.6.tsv was obtained with Mercator 3.6 using the ITAG4.0_proteins.fasta file. <em>Solanum habrochaites</em> PI127826: a GFF file was produced using RepeatMasker (see below) and Braker. <pre><code class="language-bash">RepeatMasker -qq -e rmblast -small -xsmall -pa 10 -lib mipsREdat_9.3p_Eudicot_TEs.fasta -dir repeat_masking_run/ PI127826.fasta</code></pre> LA1777 <strong>Reference:</strong> Tomato Genome Sequencing Consortium. 2012. The tomato genome sequence provides insights into fleshy fruit evolution. Nature volume 485, pages 635–641. Bolger et al. 2014. The genome of the stress-tolerant wild tomato species Solanum pennellii http://www.nature.com/ng/journal/v46/n9/full/ng.3046.html Hosmani et al. 2019. An improved de novo assembly and annotation of the tomato reference genome using single-molecule sequencing, Hi-C proximity ligation and optical maps. https://www.biorxiv.org/content/10.1101/767764v1 Aflitos et al. 2014. Exploring genetic variation in the tomato (<em>Solanum</em> section <em>Lycopersicon</em>) clade by whole‐genome sequencing. https://onlinelibrary.wiley.com/doi/full/10.1111/tpj.12616 Stam et al. 2019. The <em>de Novo</em> Reference Genome and Transcriptome Assemblies of the Wild Tomato Species <em>Solanum chilense</em> Highlights Birth and Death of NLR Genes Between Tomato Species. G3: Genes, Genomes, Genetics December 1, 2019 vol. 9 no. 12 3933-3941; https://doi.org/10.1534/g3.119.400529