cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7331273
title: Single crystal X-ray diffraction data for Rhizobium radiobacter N-carbamoyl-beta-alanine amidohydrolase

description: Single crystal X-ray diffraction data for Rhizobium radiobacter N-carbamoyl-beta-alanine amidohydrolase collected from crystals produced as below: Purified recombinant RrCβAA was concentrated to 15 mg/mL using a 10 kDa MWCO centrifugal concentrator (Vivaspin) and subjected to sitting drop vapor diffusion crystallization screening with commercial screens from Molecular Dimensions and Hampton Research. Drops of 100 nL protein plus 100 nL well solution were set up against wells containing 70 L of crystallisation solutions. After two weeks crystals were found in xxx condition. An optimisation screen based on this condition was set up in 24 well plates by varying the PEG1500 concentration and MMT buffer pH. Drops of 1 μL protein and 1 μL well solution were set up on plastic cover slips over wells containing 1 ml crystallisation solution. Crystals grew in a well solution containing 23 % (w/v) PEG1500 and 100 mM MMT pH 6.0. Crystals were harvested with a LithoLoop (Molecular Dimensions Limited) and transferred to a cryoprotection solution of well solution supplemented with 50 % PEG400. Cryoprotected crystals were flash cooled in liquid nitrogen.
DOI: 10.5281/zenodo.6985546
title: A Grand Scan of the pMSSM Parameter Space for Snowmass 2021

description: SLHA (SUSY Les Houches Accord) files for each pMSSM point in the Snowmass 2021 pMSSM scan produced according to the procedure outlined in this whitepaper: https://arxiv.org/abs/2207.05103 Files pMSSM_MCMC_1 through pMSSM_MCMC_200 include the measured value of muon g-2 in the Markov chain Monte Carlo likelihood. Files containing the labels "log05", "log10", "log20", and "log30" correspond to the alternative McMC stepping configurations discussed in Section 2.2. Additional files are available on request, corresponding to the Markov chain Monte Carlo that does not include muon g-2 in the likelihood.
DOI: 10.5281/zenodo.6985545
title: A Grand Scan of the pMSSM Parameter Space for Snowmass 2021

description: SLHA (SUSY Les Houches Accord) files for each pMSSM point in the Snowmass 2021 pMSSM scan produced according to the procedure outlined in this whitepaper: https://arxiv.org/abs/2207.05103 Files pMSSM_MCMC_1 through pMSSM_MCMC_200 include the measured value of muon g-2 in the Markov chain Monte Carlo likelihood. Files containing the labels "log05", "log10", "log20", and "log30" correspond to the alternative McMC stepping configurations discussed in Section 2.2. Additional files are available on request, corresponding to the Markov chain Monte Carlo that does not include muon g-2 in the likelihood.
DOI: 10.5281/zenodo.7325710
title: Dataset Literature Review Digital Forensic AND Methods

description: Data ini diambil dari website lens.org dengan memasukkan keyword/kata kunci “Digital Forensic” “Methods” pada Sholarly Works yang kemudian difilter sebanyak 3 kali. yang pertama di filter pada bagian Document Type yaitu pada “Journal Articel”. kedua, di filter pada Years yang mana data rate 10 tahun sebelumnya yaitu mulai tahun 2012 sampai dengan tahun 2022. dan yang terakhir di filter pada bagian Subject, Subject Matter “Law”. Pada setiap tahapan mulai awal hingga akhir semua data tersebut di export dengan format CSV dan BIBTEX. Kemudian semua data yang udah terkumpul akan digunakan untuk membuat penelitian sesuai dengan Literature Review dengan kata kunci Digital Forensic AND Methods.
DOI: 10.5281/zenodo.7325709
title: Dataset Literature Review Digital Forensic AND Methods

description: Data ini diambil dari website lens.org dengan memasukkan keyword/kata kunci “Digital Forensic” “Methods” pada Sholarly Works yang kemudian difilter sebanyak 3 kali. yang pertama di filter pada bagian Document Type yaitu pada “Journal Articel”. kedua, di filter pada Years yang mana data rate 10 tahun sebelumnya yaitu mulai tahun 2012 sampai dengan tahun 2022. dan yang terakhir di filter pada bagian Subject, Subject Matter “Law”. Pada setiap tahapan mulai awal hingga akhir semua data tersebut di export dengan format CSV dan BIBTEX. Kemudian semua data yang udah terkumpul akan digunakan untuk membuat penelitian sesuai dengan Literature Review dengan kata kunci Digital Forensic AND Methods.
DOI: 10.25573/serc.20054420.v4
title: 2022 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2022 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.5281/zenodo.7304162
title: YbiB: a Novel Interactor of the GTPase ObgE

description: This dataset contains the raw data that lie at the basis of the results discussed in the paper entitled "YbiB: a Novel Interactor of the GTPase ObgE". <br> The accompanying README.txt file describes the specific data that can be found in the different raw data files.
description: Funding was provided by: Research Foundation Flanders (Fonds Wetenschappelijk Onderzoek) - Grant numbers: G0I1522N, G0B0420N and 11D4621N; Vrije Universiteit Brussel - Grant number: SRP50 KU Leuven - Grant number: C16/17/006
DOI: 10.5281/zenodo.7115650
title: AR Data Storytelling

description: <pre>Database for the comparative analysis of a set of case studies within the scope of Narrative Visualization with Augmented Reality. The first sheet collects the cases taken from the journalistic context, and the second sheet analyzes a set of 50 cases. The graphs on the third page summarize and present some information about the previous analysis.</pre>
DOI: 10.5281/zenodo.7115651
title: AR Data Storytelling

description: <pre>Database for the comparative analysis of a set of case studies within the scope of Narrative Visualization with Augmented Reality. The first sheet collects the cases taken from the journalistic context, and the second sheet analyzes a set of 50 cases. The graphs on the third page summarize and present some information about the previous analysis.</pre>
DOI: 10.5281/zenodo.7231512
title: AR Data Storytelling

description: <pre>Database for the comparative analysis of a set of case studies within the scope of Narrative Visualization with Augmented Reality. The first sheet collects the cases taken from the journalistic context, and the second sheet analyzes a set of 50 cases. The graphs on the third page summarize and present some information about the previous analysis.</pre>
DOI: 10.5281/zenodo.7331412
title: AR Data Storytelling

description: <pre>Database for the comparative analysis of a set of case studies within the scope of Narrative Visualization with Augmented Reality. The first sheet collects the cases taken from the journalistic context, and the second sheet analyzes a set of 50 cases. The graphs on the third page summarize and present some information about the previous analysis.</pre>
DOI: 10.21223/p3/2vcyrq
title: Dataset for: Analyze the use of thresholds based on crop water stress index calculus in potato "Unica" under drip irrigation

description: The objective of this study was to evaluate the UNICA potato crop variety's yield relate with the threshold of Crop Water Stress Index subjected to different water stress levels and under drip of irrigation. The trial was carried out in the experimental field of National Agrarian University La Molina during the months of June-2018 to September-2018. Three treatments of irrigation moments based on the Crop Water Stress Index (CWSI) were applied for this trial: Full irrigated (T1), CWSI less than 0.4 (T2), CWSI less than 0.7 (T3).
DOI: 10.5287/bodleian:dp0q8m885
title: Thermal Impulse Response in Porous Media for Transpiration Cooling Systems - Dataset
DOI: 10.5281/zenodo.7331336
title: Characterizing the automatic radon flux Transfer Standard system Autoflux: laboratory calibration and field experiments

description: Data from the manuscript: Characterizing the automatic radon flux Transfer Standard system Autoflux: laboratory calibration and field experiments. The present folder contains: Exhalation Bed data (data and plots from the characterization of the exhalation bed of the Cantabria University) ANSTO Autoflux (data and plots of the calibration of the Autoflux radon flux system) INTE_UPC (data and plots of the calibration of the INTE_UPC radon flux system)
description: This project 19ENV01 has received funding from the EMPIR programme co-financed by the Participating States and from the Europe'n Union's Horizon 2020 research and innovation programme.
DOI: 10.6084/m9.figshare.21383947
title: Table S3: from Demography of an ice-obligate mysticete in a region of rapid environmental change

description: Data associated with UAS-derived measurements and maturity classification
DOI: 10.6084/m9.figshare.21383947.v1
title: Table S3: from Demography of an ice-obligate mysticete in a region of rapid environmental change

description: Data associated with UAS-derived measurements and maturity classification
DOI: 10.6084/m9.figshare.21383950
title: Table S1: from Demography of an ice-obligate mysticete in a region of rapid environmental change

description: Data associated with sample collection and genetic information
DOI: 10.6084/m9.figshare.21383950.v1
title: Table S1: from Demography of an ice-obligate mysticete in a region of rapid environmental change

description: Data associated with sample collection and genetic information
DOI: 10.6084/m9.figshare.21383953
title: Table S4: from Demography of an ice-obligate mysticete in a region of rapid environmental change

description: Antarctic minke whales (<i>Balaenoptera bonaerensis</i>, AMW) are an abundant, ice-dependent species susceptible to rapid climatic changes occurring in parts of the Antarctic. Here, we used remote biopsy samples and estimates of length derived from unoccupied aircraft system (UAS) to characterize for the first time the sex ratio, maturity, and pregnancy rates of AMWs around the Western Antarctic Peninsula (WAP). DNA profiling of 82 biopsy samples (2013–2020) identified 29 individual males and 40 individual females. Blubber progesterone levels indicated 59% of all sampled females were pregnant, irrespective of maturity. When corrected for sexual maturity, the median pregnancy rate was 92.3%, indicating that most mature females become pregnant each year. We measured 68 individuals by UAS (mean = 8.04 m) and estimated that 66.5% of females were mature. This study provides the first data on the demography of AMWs along the WAP and represents the first use of non-lethal approaches to studying this species. Furthermore, these results provide baselines against which future changes in population status can be assessed in this rapidly changing marine ecosystem.
DOI: 10.6084/m9.figshare.21383953.v1
title: Table S4: from Demography of an ice-obligate mysticete in a region of rapid environmental change

description: Antarctic minke whales (<i>Balaenoptera bonaerensis</i>, AMW) are an abundant, ice-dependent species susceptible to rapid climatic changes occurring in parts of the Antarctic. Here, we used remote biopsy samples and estimates of length derived from unoccupied aircraft system (UAS) to characterize for the first time the sex ratio, maturity, and pregnancy rates of AMWs around the Western Antarctic Peninsula (WAP). DNA profiling of 82 biopsy samples (2013–2020) identified 29 individual males and 40 individual females. Blubber progesterone levels indicated 59% of all sampled females were pregnant, irrespective of maturity. When corrected for sexual maturity, the median pregnancy rate was 92.3%, indicating that most mature females become pregnant each year. We measured 68 individuals by UAS (mean = 8.04 m) and estimated that 66.5% of females were mature. This study provides the first data on the demography of AMWs along the WAP and represents the first use of non-lethal approaches to studying this species. Furthermore, these results provide baselines against which future changes in population status can be assessed in this rapidly changing marine ecosystem.
DOI: 10.6084/m9.figshare.21383956
title: Table S2: from Demography of an ice-obligate mysticete in a region of rapid environmental change

description: Data associated with blubber hormone evaluations for pregnancy state.
DOI: 10.6084/m9.figshare.21383956.v1
title: Table S2: from Demography of an ice-obligate mysticete in a region of rapid environmental change

description: Data associated with blubber hormone evaluations for pregnancy state.
DOI: 10.23645/epacomptox.20422176
title: Chemistry Dashboard Data: Factotum

description: DSSTox Substance Identifier <br> Chemical functional use category, in its original form as extracted from original document. <br> Curated harmonized functional use category. Where possible, the functional uses have been harmonized to categories defined by the Organisation for Economic Cooperation and Development, in OECD monograph ENV/JM/MONO(2017)14, <em>Internationally Harmonised Functional, Product, and Article Use Categories</em> (https://one.oecd.org/document/ENV/JM/MONO(2017)14/en/pdf). Other harmonized categories have been defined by EPA ORD. Definitions of all categories are available at <strong>https://ccte-factotum.epa.gov/functional_use_categories/.</strong> <br> Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25 <br>
DOI: 10.23645/epacomptox.20422176.v1
title: Harmonized Functional Use Data Curated in Factotum: Data for the September 2022 release of the CompTox Chemicals Dashboard

description: DSSTox Substance Identifier <br> Chemical functional use category, in its original form as extracted from original document. <br> Curated harmonized functional use category. Where possible, the functional uses have been harmonized to categories defined by the Organisation for Economic Cooperation and Development, in OECD monograph ENV/JM/MONO(2017)14, <em>Internationally Harmonised Functional, Product, and Article Use Categories</em> (https://one.oecd.org/document/ENV/JM/MONO(2017)14/en/pdf). Other harmonized categories have been defined by EPA ORD. Definitions of all categories are available at <strong>https://ccte-factotum.epa.gov/functional_use_categories/.</strong> <br> Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25 <br>
DOI: 10.5281/zenodo.7331492
title: HBV X protein induces p16 gene promoter methylation through up-regulating DNA methylation transferases DNMT1 and DNMT3A

description: <strong>Supplementary Table S1. Kruskal-Wallis test results.</strong> <strong>Supplementary Table S2. Dunn-test with Bonferroni adjustment as post-hoc comparison results</strong> <strong>Supplementary Table S3. Mann-Whitney U test results</strong>
DOI: 10.5281/zenodo.7331491
title: HBV X protein induces p16 gene promoter methylation through up-regulating DNA methylation transferases DNMT1 and DNMT3A

description: <strong>Supplementary Table S1. Kruskal-Wallis test results.</strong> <strong>Supplementary Table S2. Dunn-test with Bonferroni adjustment as post-hoc comparison results</strong> <strong>Supplementary Table S3. Mann-Whitney U test results</strong>
DOI: 10.5281/zenodo.7330360
title: DECCS - Datasets and Pretrained Models

description: Datasets and Pretrained Models for the paper Deep Clustering With Consensus Representations (DECCS).
DOI: 10.5281/zenodo.7330359
title: DECCS - Datasets and Pretrained Models

description: Datasets and Pretrained Models for the paper Deep Clustering With Consensus Representations (DECCS).
DOI: 10.25824/redu/0fmn6l
title: Avaliação da faixa mais adequada.xlsx
DOI: 10.25824/redu/ptqidr
title: Dados de para geração de imagens.xlsx
DOI: 10.25824/redu/mhs1xq
title: Avaliação das velocidades nas rotas da malha de difração.xlsx
DOI: 10.25824/redu/r0dptg
title: Tomographic Images: methodological proposals for cutting velocity, interpolation algorithm and confusion matrix metrics focusing on image quality

description: This data support the paper "Tomographic Images Generated from Measurements in Standing Trees Using Ultrasound and Postprocessed Images: Methodological Proposals for Cutting Velocity, Interpolation Algorithm and Confusion Matrix Metrics Focusing on Image Quality"
DOI: 10.23645/epacomptox.21502782
title: Chemistry Dashboard Data: High Throughput Phenotypic Profiling (HTPP)

description: Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25
DOI: 10.23645/epacomptox.21502782.v1
title: High Throughput Phenotypic Profiling (HTPP)

description: Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25
DOI: 10.23645/epacomptox.21502758
title: Chemistry Dashboard Data: High Throughput Transcriptomics (HTTr)

description: Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25 <br>
DOI: 10.23645/epacomptox.21502758.v3
title: High Throughput Transcriptomics (HTTr)

description: Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25 <br>
DOI: 10.5281/zenodo.7330530
title: Simulated industrial CT dataset for deep learning with dual-energy tomograms and ground truth material maps for copper and iron

description: We use this dataset for training and evaluation of a deep learning model to discriminate multi-material systems with X-ray CT. The dataset consists of: inputs: dual-energy tomograms as binary files without a header (tensor <strong>shape for numpy: 2x128x128 @float32</strong>) simulated spectra are 250kVp and 450kVp both prefiltered using 2mmCuSn outputs: the material maps a.k.a. ground truths for the training (same shape as inputs) sampled with a delaunay algorithm and randomly filled with iron and copper fractions The <strong>dataset is normalized to [0, 1]</strong>, so you have to multiply by the mass densities of copper and iron to obtain effective fractions in g/cm^3.
DOI: 10.5281/zenodo.7330529
title: Simulated industrial CT dataset for deep learning with dual-energy tomograms and ground truth material maps for copper and iron

description: We use this dataset for training and evaluation of a deep learning model to discriminate multi-material systems with X-ray CT. The dataset consists of: inputs: dual-energy tomograms as binary files without a header (tensor <strong>shape for numpy: 2x128x128 @float32</strong>) simulated spectra are 250kVp and 450kVp both prefiltered using 2mmCuSn outputs: the material maps a.k.a. ground truths for the training (same shape as inputs) sampled with a delaunay algorithm and randomly filled with iron and copper fractions The <strong>dataset is normalized to [0, 1]</strong>, so you have to multiply by the mass densities of copper and iron to obtain effective fractions in g/cm^3.
DOI: 10.5281/zenodo.7308894
title: SuperDARN data in netCDF format (2020-Dec)

description: 2020-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7309306
title: SuperDARN data in netCDF format (2020-Jun)

description: 2020-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'