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191 results for “Transparency”
Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling
<p><strong>egon-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project <strong>eGo<sup>n</sup></strong>. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGo<sup>n</sup> <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a></p> <p>egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li><strong>climate_zones_germany</strong> <ul> <li>Climate zones in Germany</li> <li>source: Own representation based on DWD TRY climate zones</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>emobility</strong> <ul> <li>Data on eMobility mit_trip_data:<br> motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).</li> <li>Reiner Lemoine Institut, June 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>geothermal_potential</strong> <ul> <li>Spatial distribution of deep geothermal potentials in Germany</li> <li>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>household_electricity_demand_profiles</strong> <ul> <li>Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.<br> The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.<br> The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.<br> A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>household_heat_demand_profiles</strong> <ul> <li>Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>hydrogen_storage_potential_saltstructures</strong> <ul> <li>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</li> <li>Source: Flach lagernde Salze, (c) BGR Hannover, 2021.<br> Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &<br> Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &<br> Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und<br> Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,<br> Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen<br> zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –<br> Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.<br> Hannover: BGR.</li> <li>License: The original data are licensed under the GeoNutzV, see https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</li> </ul> </li> <li><strong>industrial_sites</strong> <ul> <li>Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.</li> <li>source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>nep2035_version2021</strong> <ul> <li>Data extracted from the German grid development plan - power</li> <li>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>pipeline_classification_gas</strong> <ul> <li>Parameters for the classification of gas pipelines</li> <li>source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>pypsa_eur_sec</strong> <ul> <li>Preliminary results from scenario generator pypsa-eur-sec</li> <li>source: own calculation using pypsa-eur-sec fork (https://github.com/openego/pypsa-eur-sec)</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>regions_dynamic_line_rating</strong> <ul> <li>German regions suitable to model dynamic line rating</li> <li>source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>re_potential_areas</strong> <ul> <li>Eligible areas for wind turbines and ground-mounted PV systems.</li> <li>Reiner Lemoine Institut, January 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>WZ_definition</strong> <ul> <li>Definitions of industrial and commercial branches</li> <li>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></li> <li>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</li> </ul> </li> <li><strong>zensus_households</strong> <ul> <li>Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.</li> <li>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps: <ul> <li>Search for: "1000A-2029"</li> <li>or choose topic: "Bevölkerung kompakt"</li> <li>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</li> <li>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</li> </ul> </li> <li>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</li> </ul> </li> </ol> <p> </p>
Data for "Transparent Josephson Junctions in Higher-Order Topological Insulator WTe2 via Pd Diffusion"
<p>Data for the publication "Transparent Josephson Junctions in Higher-Order Topological Insulator WTe<sub>2</sub> via Pd Diffusion".</p> <p>Updated version as accepted by Physical Review Materials. </p>
Snakemake report for manuscript "Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing"
<p>For viewing the report, unzip the file and open index.html in your browser.</p>
Experimental data for "Physical and chemical properties and degradation of MAPbBr3 films on transparent substrates"
Open the record for dataset details and reuse information.
Quantitative measures of corneal transparency, derived from objective analysis of depth-resolved corneal images, demonstrated with full-field optical coherence tomographic microscopy
<p>Supporting data for: <a href="https://zenodo.org/record/2579947">Quantitative measures of corneal transparency, derived from objective analysis of depth-resolved corneal images, demonstrated with full-field optical coherence tomographic microscopy</a></p>
RAW DATA: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent Thin Layer Electrochemical Cell
<p><span>Data behind the figures in the Manuscript: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent </span><span>Thin Layer </span><span>Electrochemical Cell submitted to J. Phys. Chem. </span></p>
Data for Transparent Porous Medium for Optical Fluid Flow Measurement using Refractive Index Matching
<p>This data repository contains shadowgraph images acquired during the design of three transparent porous media using refractive index matching. The images were captured through various liquid mixtures, including toluene/1-hexanol, potassium thiocyanate (KSCN), and cyclohexanol/toluene, both with (PM) and without glass beads (FM). The images were processed and analysed using ImageJ, an open-source software tool.</p>
Clinical Trial Transparency and Data-Sharing Among Bio-Pharmaceutical Companies and the Role of Company Size, Location, and Product Type: A Cross-Sectional Descriptive Analysis
<p><b>Objective</b>: To examine company characteristics associated with better transparency and to apply a tool used to measure and improve clinical trial transparency among large companies and drugs, to smaller companies and biologics.</p> <p><b>Design</b>: Cross-sectional descriptive analysis.</p> <p><b>Setting and participants. </b>Novel drugs and biologics FDA approved in 2016 and 2017, and their company sponsors.</p> <p>Using established Good Pharma Scorecard (GPS) measures, companies and products were evaluated on their clinical trial registration, results dissemination, and FDA Amendments Act (FDAAA) implementation; Companies were ranked using these measures and a multi-component data sharing measure. Associations between company transparency scores with company size (large vs non-large), location (US vs non-US), and sponsored product type (drug vs biologic) were also examined. 26% of products (16/62) had publicly available results for all clinical trials supporting their FDA approval and 67% (39/58) had public results for trials in patients by 6 months after their FDA approval; 58% (32/55) were FDAAA compliant. Large companies were significantly more transparent than non-large companies (overall median transparency score of 95% [IQR 91-100] vs 59% [IQR 41-70], p<0.001), attributable to higher FDAAA compliance (median of 100% [IQR 88-100] vs 57% [0-100], p=0.01) and better data sharing (median of 100% [IQR 80-100] vs 20% [IQR 20-40], p<0.01). No significant differences were observed by company location or product type. It was feasible to apply the GPS transparency measures and ranking tool to non-large companies and biologics. Large companies are significantly more transparent than non-large companies, driven by better data sharing procedures and implementation of FDAAA trial reporting requirements. Greater research transparency is needed, particularly among non-large companies, to maximize the benefits of research for patient care and scientific innovation. </p>
Perceived fairness and perceived transparency of AI systems according to system's characteristics, personality traits and demographic characteristics
<p>We collected data of 3197 users' fairness perception regarding various configurations of a AI-based system in the recruitment domain, as well as, the demographic and personally characteristics of the participants.</p> <p>The dataset includes the following columns:</p> <p><strong>:System characteristics</strong></p> <p> :Certification</p> <p>Uncertificated system (U)</p> <p>Certificated system (C)</p> <p>:Input data</p> <p>High quality input data (H)</p> <p>Low quality input data (L)</p> <p>:Output</p> <p>Positive outcome (P)</p> <p>Borderline outcome (B)</p> <p>Negative outcome (N)</p> <p>:Explanation style</p> <p>Control- no explanation (CON)</p> <p>Case-based (CAS)</p> <p>Certification-based (CER)</p> <p>Demographic-based (DEM)</p> <p>Input influence-based (INP)</p> <p>Sensitivity-based (SEN)</p> <p><strong>:Demographic characteristics</strong></p> <p>:Gender</p> <p>Female</p> <p>Male</p> <p>:Age</p> <p>18-34</p> <p>35-50</p> <p>50+</p> <p>:Residence</p> <p>Unites states of America</p> <p>India</p> <p>Other</p> <p>:Education level</p> <p>High school degree or less</p> <p>Bachelor's degree</p> <p>Master's or doctoral degree</p> <p>:Employment status</p> <p>Not employed</p> <p>Employed</p> <p>:Income level</p> <p>Above average</p> <p>Average</p> <p>Below average</p> <p><strong>:Personality characteristics</strong></p> <p>(TIPI questionnaire)</p> <p>Extraverted, enthusiastic</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Critical, quarrelsome</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Dependable, self-disciplined</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Anxious, easily upset</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Open to new experiences, complex</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Reserved, quiet</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Sympathetic, warm</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Disorganized, careless</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Calm, emotionally stable</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Conventional, uncreative</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p><strong>:Participants responses</strong></p> <p>:Fairness evaluation</p> <p>The participants were requested to report their level of perceived fairness (their view about the fairness of the system - at what level they consider the system as a fair system) on a 6-point Likert scale, from "Extremely fair" (represented as 3) to "Extremely unfair" (represented as -3). The option of "neither fair or unfair" (represented as 0) was excluded from the scale.</p> <p>:Transparency evaluation</p> <p>the participants were requested to report their level of perceived transparency (their understanding why the system produced the specific output - at what level they understand why this output was given) on a 6-point Likert scale, from " Thoroughly understand" (represented as 3) to " Thoroughly don't understand" (represented as -3). The option of "neither understand or don't understand" (represented as 0) was excluded from the scale.</p> <p>:Output Expectation</p> <p>The participants were requested to report their expectation for the specific output based on the input they received according to the system's scale, 5-point Likert scale from "Strongly recommended" (represented as 2) to "Strongly not recommended" (represented as -2).</p> <p> </p>
Code & Data for "Adoption of Transparency and Openness Promotion (TOP) guidelines across journals"
<p>This entry contains code and data that was used in the publication: "Adoption of Transparency and Openness Promotion (TOP) guidelines across journals" submitted in Publications journal.</p> <p>*It was version 2 when we added Fig_3_Tab2_Defining_science_disciplines_plus_plot.R script to version 1.</p> <p>*It was version 3 because we added script that calculates median and mean values of the stringency levels to version 2 data.</p> <p>*Latest version is version 4: we added supplementary data.</p> <p>#IDEA:</p> <p>This project was about analyzing policies of two thousand journals within the framework of eight TOP standards: <br> data citation, transparency of data, material, code and design and analysis, replication, plan and study pre-registration, <br> and two effective interventions: “Registered reports” and “Open science badges”. </p> <p># MATERIALS & METHODS<br> We downloaded the TOP Factor (v33, 2022-08-29 3:12 PM) metric from the https://osf.io/kgnva/files/osfstorage/5e13502257341901c3805317 <br> website and analyzed its content with an in-house R script (in this repo):<br> 1) SCRIPT: fig1_Analyzing_journals_policies_and_TOP_guidelines.R<br> 2) SCRIPT: Figure2a_b_TOP_impl_journal_statistist_0_1_piechart_barplot.R<br> In order to get statistics about implementation of the TOP guidelines across discipline-specific journals, <br> we extracted information about journal’s disciplines from the Scopus content database. <br> We downloaded SCOPUS content coverage from the https://www.elsevier.com/solutions/scopus/how-scopus-works/content?dgcid=RN_AGCM_Sourced_300005030 (existJuly2022.xlsx)<br> and used the first Sheet.<br> We identified match between those 2 tables: <br> 3) SCRIPT: Rscript_overlapping_TOP_dataframe_and_SCOPUS_db.R<br> And resulted in Overlap_SCOPUS_TOP.rds file<br> And performed visualization and statistics:<br> 4) SCRIPT: Fig_3_Tab2_Defining_science_disciplines_plus_plot.R</p> <p> </p> <p>#RESULTS Submitted to Publications 30.9.2022.</p> <p>Reviewed 2.11.2022.</p> <p>Latest version: 25.11.2022.</p>
Study on a quantitative method for determining mixing proportion of transparent cemented soil for visual geotechnical model tests
<p>An effective mixing of transparent cemented soil is necessary for visual geotechnical model tests, so a quantitative method for determining mixing proportion of transparent cemented soil was generated in this paper. Firstly, quartz sand, Nanoscale silica powder and N-dodecane mixed 15# white oil were selected as the raw materials, and a series of orthogonal experiments were designed. Concurrently, the main physical and mechanical parameters (volumetric weight γ, internal friction angle φ, cohesion c) of transparent cemented soil were measured, caused by the change of "particle size of quartz sand" and " mass ratios between fumed silica and fused quartz". Subsequently, multiple linear regression equations of various physical and mechanical parameters (γ, φ, c) were obtained by fitting the original test data. Finally, the rationality of multiple linear regression equations was proved. The research results indicated: (1) the volumetric weight changes from 16.13kN/m<sup>3</sup> to 12.53kN/m<sup>3</sup>, the Internal friction angle is between 27.07° and 14.82°, and the cohesion varies from 31kPa to 2.3kPa, the parameters meet the similar requirements of the surrounding rock (grade ⅳ and ⅴ) and clay; (2) The values of Multiple R values (all greater than 0.88) and the Significance F value (all close to 0) proves the three regression equations were valid; (3) Combining the three regression equations and particle size of quartz sand, the mass ratio between fumed silica and fused quartz and geometry similarity constant were solved. All the conclusions mentioned could provide theoretical support and data reference for transparent soil model test implementation.</p>
Supporting data for "A new method for in vivo assessment of corneal transparency using spectral-domain OCT"
<p>Supporting SD-OCT images for PLOS ONE article "<a href="https://doi.org/10.1371/journal.pone.0291613">A new method for <em>in vivo </em>assessment of corneal transparency using spectral-domain OCT</a>" (DOI: 10.1371/journal.pone.0291613). </p>
Impacts of a Physician-targeted Price Transparency Tool on Medication Out-of-pocket Costs
ClinicalTrials.gov study NCT04940988. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Transparent, Self-drying Silicone Gel on the Treatment of Hypertrophic Abdominal Scars
ClinicalTrials.gov study NCT01078428. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Variations of Anonymity, Transparency, and Efficacy in Digital Health Applications
ClinicalTrials.gov study NCT06465589. IPD Sharing: YES. Countries: 1. Publications: 16.
Data from: Transparency reduces predator detection in mimetic clearwing butterflies
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Data from: Transparency improves concealement in cryptically coloured moths
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Study on a quantitative method for determining mixing proportion of transparent cemented soil for visual geotechnical model tests
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Data from: Phylotocol: promoting transparency and overcoming bias in phylogenetics
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Data from: Quantifying episodes of sexual selection: insights from a transparent worm with fluorescent sperm
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