cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11945295
title: Additional file 3 of Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability

description: Additional file 3: Table S2. Read FPKM per gene.
DOI: 10.6084/m9.figshare.11945289.v1
title: Additional file 2 of Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability

description: Additional file 2: Table S1. Read counts of all genes.
DOI: 10.6084/m9.figshare.11945295.v1
title: Additional file 3 of Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability

description: Additional file 3: Table S2. Read FPKM per gene.
DOI: 10.6084/m9.figshare.11945283
title: salary 2020.xlsx

description: data for analyze the salary<br>
DOI: 10.6084/m9.figshare.11944986
title: snails data

description: Snail data for: "Risk prediction of two types of potential snail habitats in Anhui Province of China: model-based approaches"
DOI: 10.6084/m9.figshare.11944986.v1
title: Risk prediction of two types of potential snail habitats in Anhui Province of China: model-based approaches

description: Snail data for: "Risk prediction of two types of potential snail habitats in Anhui Province of China: model-based approaches"
DOI: 10.6084/m9.figshare.11944965
title: Anaerobic co-digestion of sludge with cellulose, protein, and lipids

description: These datasets include raw data for rheological and chemical properties of feed, feed mixed with inoculum, and digestate of final day. It also include SPSS file used for calculating the reaction kinetic parameters as well as MATLAB code for statistical analysis of rheological parameters.
DOI: 10.6084/m9.figshare.11944965.v2
title: Anaerobic co-digestion of sludge with cellulose, protein, and lipids

description: These datasets include raw data for rheological and chemical properties of feed, feed mixed with inoculum, and digestate of final day. It also include SPSS file used for calculating the reaction kinetic parameters as well as MATLAB code for statistical analysis of rheological parameters.
DOI: 10.6084/m9.figshare.11944965.v1
title: Anaerobic co-digestion of sludge with cellulose, protein, and lipids

description: These datasets include raw data for rheological and chemical properties of feed, feed mixed with inoculum, and digestate of final day. It also include SPSS file used for calculating the reaction kinetic parameters as well as MATLAB code for statistical analysis of rheological parameters.
DOI: 10.6084/m9.figshare.11944947.v1
title: More than one million negative reviews from a Chinese e-commerce platform

description: ***Note: Because of the COVID-19, the lab is shutdown and the data set can not be uploaded now. It will be avaiable later after we can get into the lab.***<br><br>The dataset is from a B2C e-commerce platform in China, with massive product negative reviews of four representative sectors including Computers, Phone&Accessories, Gifts&Flowers and Clothing.Here the negative reviews are defined as the reviews with scores 1. After the raw data was collected, deduplication, user anonymization & categorization and text classification was employed to process the raw data. The data contains fields of id for comment, anonymous id for user, review text, timestamp of the posting, negative reason label and user level. <br>The dataset contains four JSON files, with each file titled by the corresponding sector name.In each JSON file, each line represents a record of a negative review from this sector, in which the filed ‘id’ is the unique code we created for reviews, the filed ‘userID’ is the unique code we created for users, the field ‘userLevel’ is the user’s level in the platform, the field ‘creationTime’ is the timestamp a review was posted, the filed ‘content’ is the review text in Chinese and the field ‘label’ represent why the consumers post the negative reviews, in which 0 for Logistic, 1 for Product function, 2 for Consumer Service and 3 for False Marketing.<br>The dataset comes from our paper:<br>Menghan Sun and Jichang Zhao. How do online consumers review negatively? arXiv:2004.13463, 2020.<br>If it is helpful, please cite the paper. <br>This work was supported by NSFC (Grant No. 71871006).
DOI: 10.6084/m9.figshare.11944944
title: TISOLS2020.xlsx

description: This is the datasets used for drawing figures appeared in the proceeding of TISOLS.See Aichi M., Land subsidence modelling for decision making ongroundwater abstraction under emergency situation. Proc. IAHS, 97, 1–6, 2020https://doi.org/10.5194/piahs-97-1-2020
DOI: 10.17608/k6.auckland.11944935
title: 2 years of sea level data from Auckland Harbour

description: sea level data from station AUCT in the Auckland Harbour, from 2018 through 2019, sampled at 1000s.<br>
DOI: 10.6084/m9.figshare.11944932
title: NILM Data-Set

description: There are 6 files corresponding to aggregate energy data collected at 6 different source voltages from 190V to 240V.The features in the data are Voltage,Current, Active Power(P), Apparent Power(S), Reactive Power(Q), Power Factor, Phase angle, Class, Resistance, Pdiff, Sdiff, Qdiff, Rdiff, Device. <br>It is a continuous time series data collected at 10 Hz from automated data collection setup. Collect aggregate data of 7 home appliances namely Geyser, Kettle, Mixer, Oven, Fan, Air Purifier and Vacuum Cleaner.<br><br>The Class column contains a numeric value whose binary equivalent represents the configuration of appliance in ON/OFF state that results the aggregate data. The numbers are grey code sequence, allowing only one device change state at any time.<br>Device column contains the label of the event happened in data such as Geyser in ON state event labelled as GON. Fan in OFF state event is FOFF. The No events data are labelled as NN.<br> <br>Resistance column is calculated feature, is the ratio of voltage and current. Other features such as Pdiff, Sdiff, Qdiff and Rdiff are the single difference values of P, S,Q and R features.<br>
DOI: 10.17608/k6.auckland.11944935.v2
title: 2 years of sea level data from Auckland Harbour

description: sea level data from station AUCT in the Auckland Harbour, from 2018 through 2019, sampled at 1000s.<br>
DOI: 10.17608/k6.auckland.11944935.v1
title: 2 years of sea level data from Auckland Harbour

description: sea level data from station AUCT in the Auckland Harbour, from 2018 through 2019, sampled at 1000s.<br>
DOI: 10.6084/m9.figshare.11944824
title: amExample.R

description: Example code for assisted migration paper.
DOI: 10.6084/m9.figshare.11944824.v1
title: amExample.R

description: Example code for assisted migration paper.
DOI: 10.6084/m9.figshare.11944761
title: The inter- and intra observer agreement and repeatability of two grading systems of Euqine Gastric Ulcer SyndromeThe inter- and intraobserver reliability of two grading systems of Equine Gastric Ulcer Syndrome.

description: Dataset of the raw answers for the observations of equine gastric mucosa and grading with a novel visual analouge scale and the EGUS council grading system
DOI: 10.6084/m9.figshare.11944761.v1
title: The inter- and intra observer agreement and repeatability of two grading systems of Euqine Gastric Ulcer Syndrome

description: Dataset of the raw answers for the observations of equine gastric mucosa and grading with a novel visual analouge scale and the EGUS council grading system
DOI: 10.6084/m9.figshare.11944290
title: blandford mckee init cond 50000

description: blandford mckee init cond 50000<br>
DOI: 10.6084/m9.figshare.11944290.v1
title: blandford mckee init cond 50000

description: blandford mckee init cond 50000<br>
DOI: 10.6084/m9.figshare.11944263
title: IJGIS_Data.zip

description: · Geodatabase IJGIS_FL.gdb with two features (Hospitals and ZIP_Code_Area) and table (OD_AllFlow_Time). The feature “Hospitals” contains fields OBJECTID and NUMBEDS. The feature “ZIP_Code_Area” contains fields ZONEID and Popu. The O-D travel time table “OD_AllFlow_Time” contains fields PatientZIP_ZoneID (corresponding to ZONEID in the feature ZIP_Code_Area), Hospital_ObjectID (corresponding to OBJECTID in the feature Hospitals), and Total_Minutes (travel time in minutes). All feature, table and their associated field names are self-explanatory. · ArcGIS toolkit program file Accessibility.tbx, with four associated Python scripts grouped under folder Scripts. As stated previously, this case study uses the 4<sup>th</sup> tool “Generalized 2SFCA (w External Distance Table)”, with interface shown in Figure 2. For installing or accessing an ArcGIS tool, refer to Wang (2015: 90).
DOI: 10.6084/m9.figshare.11942139
title: Support.rar

description: Support material of 'A revised range of variability approach considering the morphological alteration of hydrological indicators'
DOI: 10.6084/m9.figshare.11942223
title: Data for Self-propelling and rolling of a sessile-motile bacterial aggregate

description: Source data for figures.
DOI: 10.6084/m9.figshare.11942136
title: Cuttlefish buoyancy in response to food availability and ocean acidification

description: Dataset of <i>Cuttlefish buoyancy in response to food availability and ocean acidification.</i>
DOI: 10.6084/m9.figshare.11942139.v1
title: Support.rar

description: Support material of 'A revised range of variability approach considering the morphological alteration of hydrological indicators'
DOI: 10.6084/m9.figshare.11941506
title: MMRF_Fusions.Source_Data.scRNA_revision.Foltz_et_al.tar.gz

description: Source Data files associated with Evolution and structure of clinically relevant gene fusions in multiple myeloma by Foltz et al published in Nature Communications (2020). This is a secondary Source Data file for scRNA data. Please move the folder 'scRNA_revision' to the primary Source Data data/ folder to run analysis scripts.
DOI: 10.6084/m9.figshare.11941494
title: MMRF_Fusions.Source_Data.Foltz_et_al.tar.gz

description: Source Data files associated with Evolution and structure of clinically relevant gene fusions in multiple myeloma by Foltz et al published in Nature Communications (2020).
DOI: 10.6084/m9.figshare.11941506.v1
title: MMRF_Fusions.Source_Data.scRNA_revision.Foltz_et_al.tar.gz

description: Source Data files associated with Evolution and structure of clinically relevant gene fusions in multiple myeloma by Foltz et al published in Nature Communications (2020). This is a secondary Source Data file for scRNA data. Please move the folder 'scRNA_revision' to the primary Source Data data/ folder to run analysis scripts.
DOI: 10.6084/m9.figshare.11941494.v1
title: MMRF_Fusions.Source_Data.Foltz_et_al.tar.gz

description: Source Data files associated with Evolution and structure of clinically relevant gene fusions in multiple myeloma by Foltz et al published in Nature Communications (2020).
DOI: 10.6084/m9.figshare.11941323
title: Sup_Table_1.xlsx

description: Samples used in the Project II: Analysis of Parasites in Publicly available Livestock and Human Metagenomic Samples
DOI: 10.6084/m9.figshare.11941344
title: Datasets supporting Manry J, Vincent QB et al. Communications Biology

description: <i><b>Article:</b></i> Genome-wide association study of Buruli ulcer in rural Benin highlights role of two LncRNAs and the autophagy pathway<br><i><b>Authors:</b></i> Jeremy Manry, Quentin B. Vincent, Christian Johnson, Maya Chrabieh, Lazaro Lorenzo, Ioannis Theodorou, Marie-Françoise Ardant, Estelle Marion, Annick Chauty, Laurent Marsollier, Laurent Abel, Alexandre Alcaïs <br><i><b>Journal:</b></i> Communications Biology<br><br><i>We provide GWAS summary statistics for:</i><br>The binary phenotype (Buruli ulcer cases vs. controls)<br><b>File:</b> Manry-Vincent_Buruli-ulcer-GWAS_Summary-Stastistics_Binary-phenotype.txt.gz<br>The age at onset phenotype<br><b>File</b>: Manry-Vincent_Buruli-ulcer-GWAS_Summary-Stastistics_age-at-onset-phenotype.txt.gz<br>Variant’s positions are given according to GRCh37.p13.<br><i>Abbreviations used in the header: </i><br>CHR: chromosome<br>info: imputation info value from IMPUTE v2.3.2<br>MAF: minor allele frequency<br>OR: odds ratio<br>OR_lower: 95% Confidence Interval lower bound<br>OR_upper: 95% Confidence Interval upper bound<br>HR: hazard ratio<br>HR_lower: 95% Confidence Interval lower bound<br>HR_upper: 95% Confidence Interval upper bound<br><br><i>We also provide source data for producing Figure 5</i><br><b>File:</b> rs76647377_survival-curve-data_Figure5.txt
DOI: 10.6084/m9.figshare.11941344.v1
title: Datasets supporting Manry J, Vincent QB et al. Communications Biology

description: <i><b>Article:</b></i> Genome-wide association study of Buruli ulcer in rural Benin highlights role of two LncRNAs and the autophagy pathway<br><i><b>Authors:</b></i> Jeremy Manry, Quentin B. Vincent, Christian Johnson, Maya Chrabieh, Lazaro Lorenzo, Ioannis Theodorou, Marie-Françoise Ardant, Estelle Marion, Annick Chauty, Laurent Marsollier, Laurent Abel, Alexandre Alcaïs <br><i><b>Journal:</b></i> Communications Biology<br><br><i>We provide GWAS summary statistics for:</i><br>The binary phenotype (Buruli ulcer cases vs. controls)<br><b>File:</b> Manry-Vincent_Buruli-ulcer-GWAS_Summary-Stastistics_Binary-phenotype.txt.gz<br>The age at onset phenotype<br><b>File</b>: Manry-Vincent_Buruli-ulcer-GWAS_Summary-Stastistics_age-at-onset-phenotype.txt.gz<br>Variant’s positions are given according to GRCh37.p13.<br><i>Abbreviations used in the header: </i><br>CHR: chromosome<br>info: imputation info value from IMPUTE v2.3.2<br>MAF: minor allele frequency<br>OR: odds ratio<br>OR_lower: 95% Confidence Interval lower bound<br>OR_upper: 95% Confidence Interval upper bound<br>HR: hazard ratio<br>HR_lower: 95% Confidence Interval lower bound<br>HR_upper: 95% Confidence Interval upper bound<br><br><i>We also provide source data for producing Figure 5</i><br><b>File:</b> rs76647377_survival-curve-data_Figure5.txt
DOI: 10.6084/m9.figshare.11941323.v1
title: Sup_Table_1.xlsx

description: Samples used in the Project II: Analysis of Parasites in Publicly available Livestock and Human Metagenomic Samples
DOI: 10.6084/m9.figshare.11941494.v2
title: MMRF_Fusions.Source_Data.Foltz_et_al.tar.gz

description: Source Data files associated with Evolution and structure of clinically relevant gene fusions in multiple myeloma by Foltz et al published in Nature Communications (2020).
DOI: 10.6084/m9.figshare.11941224
title: CLIWOC Slim and Routes

description: This data package contains two derivatives of the original CLIWOC data product that make using it for visualisation and teaching purposes more convenient. The derivatives are the following: <code>cliwoc_slim</code>: a version of CLIWOC 2.1 including only records with a longitude and latitude, a subset of the full variable list (<code>YR</code>, <code>MO</code>, <code>DY</code>, <code>HR</code>, <code>latitude</code>, <code>longitude</code>, <code>ID</code>, <code>C1</code>), and two additional columns, <code>date</code> with a processed date (<code>YR-MO-DY</code>), and <code>dys_since_start</code> with the count of days elapsed since the first record. The resulting file has a substantially smaller footprint.<code>cliwoc_routes</code>: a transformation of individual logs by the same ship into a single linestring or route. Crossings of longitude -180/180 are dealt with. In addition to the geometries, start date (<code>start</code>), end date (<code>end</code>), count of records (<code>records</code>), country (<code>C1</code>), and length of the route in days (<code>length_days</code>) are included. <strong>NOTE</strong>: this product is not intended for climatological or historical research as it makes certain assumptions that favor visualisation. For those goals, the original CLIWOC package is preferred. Original Sources The original CLIWOC database was the result of a EU-funded project. Main website available at: https://webs.ucm.es/info/cliwoc/The input data to create these derivatives comes from the "HistoricalClimatology.com" team. The link for the input data file in geopackage format is available at: https://www.historicalclimatology.com/cliwoc.html#To process the data, these notes by Steven Ottens were most useful: https://stvno.github.io/page/cliwoc/ Contents This data package includes the following files: <code>cliwoc_slim.geopackage/mbtiles</code>: Geopackage and vector tileset versions of the <code>cliwoc_slim</code> table, expressed in lon/lat (<code>EPSG:4326</code>).<code>cliwoc_routes.geopackage/mbtiles</code>: Geopackage and vector tileset versions of the <code>cliwoc_routes</code> table, expressed in lon/lat (<code>EPSG:4326</code>).<code>cliwoc_process.ipynb</code>: Jupyter Notebook with code required to create both tables in each file format.<code>README.md</code>: documentation file. Computational Platform The code in <code>cliwoc_process.ipynb</code> can be run using the <code>darribas/gds:4.0</code> Docker container with <code>tippecanoe</code> installed to create vector tilesets.
DOI: 10.6084/m9.figshare.11940966
title: Water chemistry and estimation of background levels of elements in surface water bodies from a protected area in the vicinity of Fe deposits, Southeastern Amazon

description: This paper aimed to provide the knowledge of water chemistry and quality and background values of the elements in surface waters around the Fe-ore deposits of N3 and N4WSul of Serra dos Carajás, Brazil. Water samples were collected from 17 monitoring points monthly/quarterly between 2013 and 2016 and the elemental analyses were carried out using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The waters are slightly acidic to slightly alkaline in nature and are classified into “good” and “optimum” water quality category. Seasonal variation impacted water quality, with significantly higher content of metals (such as Fe<sub>d</sub>, Fe<sub>t</sub>, and Al<sub>d</sub>) during the rainy period that is due to more intense surface-runoff and leaching. Comparing with CONAMA 357/05/WHO limits, nonconformities were mainly observed for pH, Fe and Mn, and in some cases for Al<sub>d</sub>, Zn<sub>t</sub>, Cu<sub>d</sub>, Cd<sub>t</sub> and Zn<sub>t</sub>, and were significantly higher in the N4WSul area than in the N3. Although, Fe and Mn mostly enter the water bodies from catchment weathering, the poor correlation between Fe and Mn indicated that their source and transport processes are different. In fact, elevated Mn<sub>d</sub> concentrations associated with low DO content in the dry period are explained by a reductive dissolution of manganese oxides via bacterial decomposition of OM. The background threshold values (BTVs) of elements were estimated by the upper 95% simultaneous limit (USL95; using ProUCL software) and the median ± 2 Median Absolute Deviation (mMAD) method, while the former was considered as the most suitable method for defining BTVs. The BTVs for Al(d), Fe (d), Mn and Zn are mostly exceeding the maximum limits stipulated by the legislation. However, since these areas are located in a protected region, these values are seen as representative of the natural conditions of the study area and reflect geogenic influence.
DOI: 10.6084/m9.figshare.11940966.v1
title: Water chemistry and estimation of background levels of elements in surface water bodies from a protected area in the vicinity of Fe deposits, Southeastern Amazon

description: This paper aimed to provide the knowledge of water chemistry and quality and background values of the elements in surface waters around the Fe-ore deposits of N3 and N4WSul of Serra dos Carajás, Brazil. Water samples were collected from 17 monitoring points monthly/quarterly between 2013 and 2016 and the elemental analyses were carried out using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The waters are slightly acidic to slightly alkaline in nature and are classified into “good” and “optimum” water quality category. Seasonal variation impacted water quality, with significantly higher content of metals (such as Fe<sub>d</sub>, Fe<sub>t</sub>, and Al<sub>d</sub>) during the rainy period that is due to more intense surface-runoff and leaching. Comparing with CONAMA 357/05/WHO limits, nonconformities were mainly observed for pH, Fe and Mn, and in some cases for Al<sub>d</sub>, Zn<sub>t</sub>, Cu<sub>d</sub>, Cd<sub>t</sub> and Zn<sub>t</sub>, and were significantly higher in the N4WSul area than in the N3. Although, Fe and Mn mostly enter the water bodies from catchment weathering, the poor correlation between Fe and Mn indicated that their source and transport processes are different. In fact, elevated Mn<sub>d</sub> concentrations associated with low DO content in the dry period are explained by a reductive dissolution of manganese oxides via bacterial decomposition of OM. The background threshold values (BTVs) of elements were estimated by the upper 95% simultaneous limit (USL95; using ProUCL software) and the median ± 2 Median Absolute Deviation (mMAD) method, while the former was considered as the most suitable method for defining BTVs. The BTVs for Al(d), Fe (d), Mn and Zn are mostly exceeding the maximum limits stipulated by the legislation. However, since these areas are located in a protected region, these values are seen as representative of the natural conditions of the study area and reflect geogenic influence.
DOI: 10.6084/m9.figshare.11940579
title: Data-collected papers.xlsx

description: This dataset consisted of 70 papers of empirical studies.<br>Collected papers for "Design Support from the HCI Field for Service Designers: A Systematic Review of Tool Evaluation Outcomes"<br>
DOI: 10.6084/m9.figshare.11940606.v1
title: Supplementary data for Frontzek et al.

description: Uncropped western blot images from "Rationally designed neuroprotective antibodies against prions"
DOI: 10.6084/m9.figshare.11940576
title: Metadata record for: A global ensemble of ocean wave climate projections from CMIP5-driven models

description: This dataset contains key characteristics about the data described in the Data Descriptor A global ensemble of ocean wave climate projections from CMIP5-driven models. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> <br>
DOI: 10.6084/m9.figshare.11940579.v2
title: Data-collected papers.xlsx

description: This dataset consisted of 70 papers of empirical studies.<br>Collected papers for "Design Support from the HCI Field for Service Designers: A Systematic Review of Tool Evaluation Outcomes"<br>
DOI: 10.6084/m9.figshare.11940576.v1
title: Metadata record for: A global ensemble of ocean wave climate projections from CMIP5-driven models

description: This dataset contains key characteristics about the data described in the Data Descriptor A global ensemble of ocean wave climate projections from CMIP5-driven models. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> <br>
DOI: 10.6084/m9.figshare.11940579.v1
title: Data-collected papers.xlsx

description: Collected papers for "Design Support from the HCI Field: A Systematic Literature Review of the Supportiveness of Design Support Tools for Service Designers"<br>
DOI: 10.6084/m9.figshare.11940606.v2
title: Supplementary data for Frontzek et al.

description: Uncropped western blot images from "Rationally designed neuroprotective antibodies against prions"
DOI: 10.25810/gnet-gz66.6
title: Russian Newspaper Coverage of Climate Change or Global Warming, 2000-2020 - July 2020

description:         The Media and Climate Change Observatory Data monitors 117 sources (across newspapers, radio and TV) in 53 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.6084/m9.figshare.11940459
title: Dataset climbing chalk

description: Dataset
DOI: 10.6084/m9.figshare.11940459.v1
title: Dataset climbing chalk

description: Dataset