cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/jdfd2b/8ms6bl
title: 0V

description: sigma=0volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/9ydzpb
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/ydoprh
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/24gri0
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931439.v3
title: Streptococcus pyogenes PopPUNK database

description: Database for assigning cluster ID to new S. pyogenes isolates.First extract:tar xf GAS_query_v2.tar.bz2<br>Then assign new genomes:poppunk --assign-query --ref-db GAS_query_v2 --distances GAS_query_v2/GAS_query_v2.dists --model-dir GAS_query_v2 --q-files query.list --output GAS_assignment --threads 8<br><br>Where query.list is a list of the assemblies to assign<br>
DOI: 10.7910/dvn/jdfd2b/vqg0yd
title: 0V

description: sigma=0volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/3p2yex
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931439.v2
title: Streptococcus pyogenes PopPUNK database

description: Database for assigning cluster ID to new S. pyogenes isolates.First extract:tar xf GAS_query_db.tar.bz2<br>Then assign new genomes:poppunk --assign-query --ref-db GAS_query --distances GAS_query/GAS_query.dists --model-dir GAS_query --q-files query.list --output GAS_assignment --threads 8<br><br>Where query.list is a list of the assemblies to assign<br>
DOI: 10.6084/m9.figshare.6931418
title: Supplementary data from Rapid aggregation of biofilm-covered microplastics with marine biogenic particles

description: This file provides data on the proportions of microplastics and particulate organic carbon that were included in the aggregates during the experiments.
DOI: 10.7910/dvn/jdfd2b/bpqfr6
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/1jykmu
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/tyj1mr
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/7pbzrg
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/fljrmg
title: 0V

description: sigma=0volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/lryecn
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931394
title: File S3. Supplemental tables from <i>In silico</i> mouse study identifies tumour growth kinetics as biomarkers for the outcome of anti-angiogenic treatment

description: Combined file with all supplemental tables
DOI: 10.6084/m9.figshare.6931391
title: File S2. Compressed file containing the computational model from <i>In silico</i> mouse study identifies tumour growth kinetics as biomarkers for the outcome of anti-angiogenic treatment

description: Model provided as a MATLAB SimBiology file, SBML, a MATLAB .m file, and a list of all equations
DOI: 10.6084/m9.figshare.6931394.v1
title: File S3. Supplemental tables from <i>In silico</i> mouse study identifies tumour growth kinetics as biomarkers for the outcome of anti-angiogenic treatment

description: Combined file with all supplemental tables
DOI: 10.7910/dvn/jdfd2b/jbsyc9
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931391.v1
title: File S2. Compressed file containing the computational model from <i>In silico</i> mouse study identifies tumour growth kinetics as biomarkers for the outcome of anti-angiogenic treatment

description: Model provided as a MATLAB SimBiology file, SBML, a MATLAB .m file, and a list of all equations
DOI: 10.7910/dvn/jdfd2b/pwmznj
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/aannqj
title: 0V

description: sigma=0volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/ododyc
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/vx4hpu
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931367
title: Interactive Overlays on the basis of Aggregated Journal-Journal Citations 2011

description: <b>Interactive Overlays on the basis of Aggregated Journal-Journal Citations 2011</b>
DOI: 10.7910/dvn/jdfd2b/43nxsy
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/rx1slh
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931364
title: DepMap Achilles 18Q3 public

description: This dataset contains the results of screening 485 cancer cell lines with the Avana CRISPR-Cas9 genome-scale knockout library generated as part of the Broad Institute's Cancer Dependency Map project. It includes the results 49 lines not previously made public. <br><br>As our screening efforts continue, we will be releasing additional cancer dependency data on a quarterly basis for unrestricted use. For the latest datasets available, further analyses, and to subscribe to our mailing list visit https://depmap.org. <br>Descriptions of the experimental methods and the CERES algorithm are published in http://dx.doi.org/10.1038/ng.3984. Some cell lines were process using copy number data based on the Sanger Institute whole exome sequencing data (COSMIC: http://cancer.sanger.ac.uk.cell_lines, EGA accession number: EGAD00001001039) reprocessed using CCLE pipelines.<br><br><br><br><br>
DOI: 10.7910/dvn/jdfd2b/oc9gp9
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/kg7xpi
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931334
title: Supplemental Table 2.xlsx

description: Supplemental Table: <i>in silico</i> prediction tools for c.570 C&gt;A; p.S190R in <i>TBX3</i> gene (NM_005996,hg19 g.115118771) missense variant
DOI: 10.7910/dvn/jdfd2b/ubyriv
title: 0V

description: sigma=0volts
DOI: 10.17632/zh4d8dk282.1
title: Romantic Attachment and Support Adequacy in New Mothers

description: This dataset was collected for a doctoral research project examining the relationship between attachment and support perceptions in new mothers.
DOI: 10.7910/dvn/jdfd2b/4fozgi
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931328
title: Bioética: origens e complexidade

description: Breve apresentação sobre as origens e a complexidade da Bioética<br><br>Infográfico com o resumo do modelo de Bioética Complexa<br>Brief presentation about the origins and complexity of Bioethics<br>Complex Bioethics theoretical framework
DOI: 10.7910/dvn/jdfd2b/zyn6iw
title: 0V

description: sigma=0volts
DOI: 10.17632/34rczh25kc.3
title: KU-MWQ: A Dataset for Monitoring Water Quality Using Digital Sensors.

description: Water quality depends on many factors. Among them some factors have direct effect in maintaining minimum sustainable environment. The following dataset contains parameters of three basic water quality factors. Temperature, pH factor and Water Turbidity. Two sets of Arduino based digital sensors were used to measure data from two different depths of the water label. Data were collected from a fish pond within the premises of Khan Jahan Ali Hall, Khulna University. Data were recorded 24 hours continuously from 15 January 2020 to 22 January 2020. Recording rate was 1 set of data per minute in average. All the sensors collected data in parallel at the same time. An Arduino Mega microcontroller board acted as the central processing unit and recorded all the sensors’ data altogether. Data file contents: Data 1:: Sensor data for 30 cm.xlsx: Contains raw data from pH sensor, Temperature sensor and Turbidity sensor. This set of sensors was 30 cm underwater. This file has 9623 sets of data each set having 3 data samples from the corresponding sensors. Data arrangement is described below: Column 1: Date and time of data recording in the format of YYYY-MM-DD [hh]:[mm]:[ss]. Time is in 24-hour format. Data are identical for all files. Column 2: Data from Temperature sensor in °C. Temperature values have maximum two decimal places. Column 3: Data from pH sensor. pH values have maximum two decimal places. Column 4: Data from turbidity sensor in “NTU” unit. Data 2:: Sensor data for 60 cm.xlsx: Contains raw data from Temperature sensor and Turbidity sensor. This set of sensors was 60 cm underwater. This file has 9623 sets of data each set having 2 data samples from the corresponding sensors. Data arrangement is described below: Column 1: Date and time of data recording, same as the previous file. Column 2: Data from Temperature sensor in °C. Temperature values have maximum two decimal places. Column 3: Data from turbidity sensor in “NTU” unit. Row-wise data categorisation: Each row represents one set of data for that consecutive time of the day. Considering when the data were taken the rows can be divided into two groups, day time data and night time data. Row 1-117, 811-1446, 2170-2856, 3634-4411, 5156-5887, 6554-7190, 7833-8477, 9078-9623:: Contains day time data from 6:00am to 6:00pm. Row 118-810, 1447-2169, 2857-3633, 4412-5155, 5888-6553, 7191-7832, 8478-9077:: Contains night time data from 6:00pm to 6:00am. These data can also be categorised row-wise based on the weather condition of the day. There are two groups of data that were collected under two different weather conditions. Row 1-6147, 7461-9623:: Contains sensor data from dry day. Row 6148-7460:: Contains sensor data from rainy day. This dataset can be used to determine environmental suitability of the local area for fish farming. Machine learning based projects can use this data for detecting anomalies and forecasting near future aquatic environmental condition.
DOI: 10.7910/dvn/jdfd2b/fbkuhr
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931334.v2
title: Supplemental Table 2.xlsx

description: Supplemental Table: <i>in silico</i> prediction tools for c.570 C&gt;A; p.S190R in <i>TBX3</i> gene (NM_005996,hg19 g.115118771) missense variant
DOI: 10.7910/dvn/jdfd2b/4mbcek
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931334.v4
title: Supplemental Table 2.xlsx

description: Supplemental Table: <i>in silico</i> prediction tools for c.570 C&gt;A; p.S190R in <i>TBX3</i> gene (NM_005996,hg19 g.115118771) missense variant
DOI: 10.7910/dvn/jdfd2b/omv1ak
title: 0V

description: sigma=0volts
DOI: 10.17632/x2wp2rky3n.2
title: Zebrafish STING modelling

description: Evolutionary and Structural Modelling Analyses of the Stimulator of Interferon Genes Sequences Datasets
DOI: 10.7910/dvn/jdfd2b/koghw6
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/e8bm82
title: 0V

description: sigma=0volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/jjmzoi
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931307
title: DHS Data - Individual Recode Haiti 2012.SAV

description: This is data that was collected during the DHS 2012 Haitian survey at the individual level. It was used for a secondary analysis of abortion care in Haiti.
DOI: 10.6084/m9.figshare.6931307.v1
title: DHS Data - Individual Recode Haiti 2012.SAV

description: This is data that was collected during the DHS 2012 Haitian survey at the individual level. It was used for a secondary analysis of abortion care in Haiti.
DOI: 10.7910/dvn/jdfd2b/hhn6qb
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931328.v5
title: Bioética: origens e complexidade

description: Breve apresentação sobre as origens e a complexidade da Bioética<br><br>Infográfico com o resumo do modelo de Bioética Complexa<br>Brief presentation about the origins and complexity of Bioethics<br>Complex Bioethics theoretical framework
DOI: 10.7910/dvn/jdfd2b/rroetl
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931328.v4
title: Bioética: origens e complexidade

description: Aula Disciplina de Bioética PPG Medicina - Escola de Medicina - PUCRSBreve apresentação sobre as origens e a complexidade da BioéticaReferencial teórico da Ética utilizado pela Bioética Complexa<br>Brief presentation about the origins and complexity of Bioethics<br>Complex Bioethics theoretical framework
DOI: 10.7910/dvn/jdfd2b/21vilu
title: 0V

description: sigma=0volts
DOI: 10.17632/536hzs29dw.1
title: The prevalence of traumatic dental injuries in deciduous teeth: A systematic review and meta-analysis.
DOI: 10.6084/m9.figshare.6931286
title: Post Hospital Mortality in Children Aged 2-12 in Tanzania.xlsx

description: Post-Hospital Mortality in Children in Tanzania 1 year after initial admission
DOI: 10.7910/dvn/jdfd2b/bmxhol
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931271
title: Photon-HDF5 files

description: These files hold experimental data for measurement of single-molecule FRET between ATTO 550 & ATTO 647N spaced by either 7 or 17 base pairs. The measurement module was nsALEX smFRET.
DOI: 10.6084/m9.figshare.6931274
title: Source data Fig 2E-I, Fig 3.xlsx

description: Source data for paper "Genome-edited skin epidermal stem cells protect mice from cocaine-seeking behavior and cocaine-overdose". Figure 2 and 3.<br>
DOI: 10.7910/dvn/jdfd2b/2bbrmq
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931274.v1
title: Source data Fig 2E-I, Fig 3.xlsx

description: Source data for paper "Genome-edited skin epidermal stem cells protect mice from cocaine-seeking behavior and cocaine-overdose". Figure 2 and 3.<br>
DOI: 10.17632/wnf5jwzp3c.1
title: Dataset on Helically Coiled Oscillating Heat Pipe (HCOHP)

description: This dataset contains raw primary temperature data obtained from the three HCOHPs charged with ethanol, methanol, and deionized water respectively and tested in the laboratory using three different heat inputs. The maximum heat input element temperatures were 100°C, 125°C and 192°C, for test Run 1, 2 and 3, respectively. The temperature data were obtained from three Omega K-type thermocouples attached to each condenser and evaporator of the HCOHP respectively, two attached to the adiabatic sections, and two attached to the inner and outer surfaces of the cylindrical copper vessel for the heat transfer. The other dataset is derived from the average temperatures from the raw primary temperature data. Here, relevant equations for the determination of the heat flux, power input, thermal contact resistance and overall thermal resistance was used. The data are presented on six separate Excel files. The first three files are captioned HCOHP Primary Data – Run 1, HCOHP Primary Data – Run 2, and HCOHP Primary Data – Run 3 representing raw primary temperature data for the three HCOHPs for each test run. The second three Excel files are captioned HCOHP Derived Data – Run 1, HCOHP Derived Data – Run 2, and HCOHP Derived Data – Run 3 represent the derived data obtained from the average raw primary temperature data for each test run. In the Excel files EOHP, MOHP and WOHP represents the ethanol charged HCOHP, methanol charged HCOHP and deionized water charged HCOHP, respectively.
DOI: 10.7910/dvn/jdfd2b/7jrzqw
title: 0V

description: sigma=0volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/p5i8ar
title: 0V

description: sigma=0volts
DOI: 10.17632/kjp4swr5f2.2
title: Assessing the nexus between knowledge management and firm performance: a data article

description: This data article highlights the contingent role of company size in the relationship between knowledge management practices and firm performance. It also investigates the possible direct impact of these practices on performance. Data was collected from managers in large and medium-sized industrial companies in Jordan, using a self-administered questionnaire. 170 responses were obtained, 91 from medium-sized companies and 79 from large companies. Confirmatory factor analysis was used to ensure the validity and reliability of the measurement model. Multiple group structural equation modelling was used to check for significant differences in the path coefficients of the research model. The fitness indicators of the multi-group model showed that there was no significant difference in the interpretation of the measurement model; also, the path model was equivalent for both large and medium-sized companies. Testing the hypotheses showed that the application of knowledge has the greatest explanatory power for performance, whether in medium or large companies; however, for knowledge capturing the explanatory power on performance was only for medium-sized companies, and there was no effect on large companies. Moreover, the acquisition and sharing of knowledge had no statistically significant effect on performance in either group.
DOI: 10.7910/dvn/jdfd2b/3sm3zn
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/bgrihv
title: 0V

description: sigma=0volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/pv5goe
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/as320l
title: 0V

description: sigma=0volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/jtyjck
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931088
title: Electric Transmission and Distribution Infrastructure Imagery Dataset

description: <b>Overview</b>The dataset contains fully annotated electric transmission and distribution infrastructure for approximately 321 sq km of high resolution satellite and aerial imagery from around the world. The imagery and associated infrastructure annotations span 14 cities and 5 continents, and were selected to represent diversity in human settlement density (i.e. rural vs urban), terrain type, and development index. This dataset may be of particular interest to those looking to train machine learning algorithms to automatically identify energy infrastructure in satellite imagery or for those working on domain adaptation for computer vision. Automated algorithms for identifying electricity infrastructure in satellite imagery may assist policy makers identify the best pathway to electrification for unelectrified areas.<br><b>Data Sources</b>This dataset contains data sourced from the LINZ Data Service licensed for reuse under CC BY 4.0. <br>This dataset also contained extracts from the SpaceNet dataset:SpaceNet on Amazon Web Services (AWS). “Datasets.” The SpaceNet Catalog. Last modified April 30, 2018 (link below).<br>Other imagery data included in this dataset are from the Connecticut Department of Energy and Environmental Protection and the U.S. Geological Survey. <br>Links to each of the imagery data sources are provided below as well as the link to the annotation tool and the github repository that provides tools for using these data.<br><b>Acknowledgements</b>This dataset was created as part of the Duke University Data+ project, "Energy Infrastructure Map of the World" (link below) in collaboration with the Information Initiative at Duke and the Duke University Energy Initiative.
DOI: 10.7910/dvn/jdfd2b/radgff
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/huma05
title: 0V

description: sigma=0volts
DOI: 10.7910/dvn/jdfd2b/apbiu6
title: 0V

description: sigma=0volts
DOI: 10.6084/m9.figshare.6931001
title: Spatially biased versus extent of occurrence records in distribution modelling predictions: a study case with South American anurans

description: Ecological Niche Modelling (ENM) is used to estimate potential species distributions through the association of general climate data with precise geographic occurrence records. Occurrence data are mainly obtained from museums or other natural history collections. However, these data are usually incomplete and spatially biased compared to actual geographic species’ distribution. Here, we compared predictions of occurrence for 13 widely distributed South American anuran species generated from two series of distribution data: a) original (and biased) point records and b) random distribution points within the extent of occurrence of the species. We compared the distribution predictions for baseline and 2050 climate change scenarios. By using six modelling algorithms, we found that the accuracy measure AUC (Area Under the Curve) of three algorithms (ED, OM-GARP and SVM) presented higher AUC values when the ENMs were generated from the original point records, whereas the other algorithms presented similar AUC values between the ENMs generated from different sets of occurrence data. The size of the predicted areas is larger when the ENMs are generated by random occurrence records (except for the algorithms BIOCLIM and ED), both in the baseline and future climate scenario projections.
DOI: 10.6084/m9.figshare.6931001.v1
title: Spatially biased versus extent of occurrence records in distribution modelling predictions: a study case with South American anurans

description: Ecological Niche Modelling (ENM) is used to estimate potential species distributions through the association of general climate data with precise geographic occurrence records. Occurrence data are mainly obtained from museums or other natural history collections. However, these data are usually incomplete and spatially biased compared to actual geographic species’ distribution. Here, we compared predictions of occurrence for 13 widely distributed South American anuran species generated from two series of distribution data: a) original (and biased) point records and b) random distribution points within the extent of occurrence of the species. We compared the distribution predictions for baseline and 2050 climate change scenarios. By using six modelling algorithms, we found that the accuracy measure AUC (Area Under the Curve) of three algorithms (ED, OM-GARP and SVM) presented higher AUC values when the ENMs were generated from the original point records, whereas the other algorithms presented similar AUC values between the ENMs generated from different sets of occurrence data. The size of the predicted areas is larger when the ENMs are generated by random occurrence records (except for the algorithms BIOCLIM and ED), both in the baseline and future climate scenario projections.
DOI: 10.7910/dvn/jdfd2b/nofeun
title: 0V

description: sigma=0volts