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191 results for “Transparency”
Survival data of five artificial morphs with different transparency characteritics
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Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms
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Data and R Script from: The ancestor of sharks and rays laid eggs, but ancestral state reconstructions need empirically supported traits and transparent reporting
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Climate change and underwater light: Large-scale changes in UV transparency associated with intensifying wet-dry cycles
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Patchy particle insights into self-assembly of transparent, graded index squid lenses
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Data for: Water depth and transparency drive the quantity and quality of organic matter in sediments of Alpine lakes on the Tibetan Plateau
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A reproducible model for magnetosensitivity: earthworms in transparent soil reduce their cumulative movement in extremely weak magnetic field
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Raw data for "From Transparent Conduction to Coulomb Blockade at Fixed Hole Number"
<p>This archive contains the raw data material for the manuscript [1]</p> <blockquote>"From Transparent Conduction to Coulomb Blockade at Fixed Hole Number"<br> D. R. Schmid, P. L. Stiller, A. Dirnaichner, A. K. Hüttel<br> <a href="https://doi.org/10.1002/pssb.202000253">Physica Status Solidi B (2020), doi: 10.1002/pssb.202000253</a></blockquote> <p>In particular, you will find here</p> <ul> <li>preparatory plots made with Gnuplot [2], and the corresponding Gnuplot scripts</li> <li>the raw data files in Gnuplot format, i.e., tab-separated columns</li> </ul> <p>The raw data files (ending in .dat) contain comment lines where in each file the data columns are explained.<br> The data has been measured using <a href="https://www.labmeasurement.de/">Lab::Measurement</a> [3,4].</p> <p>The canonic download location for this archive is on Zenodo [5].</p> <p>Changes compared to Version 1:</p> <ul> <li>Figure 1 has been re-worked, and the data files have been renamed according to the new panel numbering.</li> <li>The raw data for the new panel (c) has been included.</li> <li>The raw data of the former panel (b) has been moved to a subdirectory "old".</li> </ul> <p> </p> <p>[1] <a href="https://doi.org/10.1002/pssb.202000253">Physica Status Solidi B (2020), doi: 10.1002/pssb.202000253</a><br> [2] <a href="http://gnuplot.info/">http://gnuplot.info/</a><br> [3] <a href="https://www.labmeasurement.de/">https://www.labmeasurement.de/</a><br> [4] <a href="http://dx.doi.org/10.1016/j.cpc.2018.07.024">Computer Physics Communications <strong>234</strong>, 216–222 (2019)</a><br> [5] <a href="https://doi.org/10.5281/zenodo.3780580">doi:10.5281/zenodo.3780580</a></p>
Data from: Transparency improves concealement in cryptically coloured moths
<p>Predation is a ubiquitous and strong selective pressure on living organisms. Transparency is a predation defence widespread in water but rare on land. Some Lepidoptera display transparent patches combined with already cryptic opaque patches. A recent study showed that transparency reduced detectability of aposematic prey with conspicuous patches. However, whether transparency has any effect at reducing detectability of already cryptic prey is still unknown. We conducted field predation experiments with free avian predators where we monitored and compared survival of a fully opaque grey artificial form (cryptic), a form including transparent windows and a wingless artificial butterfly body. Survival of the transparent forms was similar to that of wingless bodies and higher than that of fully opaque forms, suggesting a reduction of detectability conferred by transparency. This is the first evidence that transparency decreases detectability in cryptic terrestrial prey. Future studies should explore the organization of transparent and opaque patches in animals and their interplay on survival, as well as the costs and other potential benefits associated with transparency on land.</p>
Data from: Phylotocol: promoting transparency and overcoming bias in phylogenetics
The integrity of science requires that the process be based on sound experimental design and objective methodology. Strategies that increase reproducibility and transparency in science protect this integrity by reducing conscious and unconscious biases. Given the large number of analysis options and the constant development of new methodologies in phylogenetics, this field is one that would particularly benefit from more transparent research design. Here, we introduce phylotocol (fī·lō·´tə·kôl), an a priori protocol-driven approach in which all analyses are planned and documented at the start of a project. The phylotocol template is simple and the implementation options are flexible to reduce administrative burdens and allow researchers to adapt it to their needs without restricting scientific creativity. While the primary goal of phylotocol is to increase transparency and accountability, it has a number of auxiliary benefits including improving study design and reproducibility, enhancing collaboration and education, and increasing the likelihood of project completion. Our goal with this Point of View article is to encourage a dialogue about transparency in phylogenetics and the best strategies to bring transparent research practices to our field.
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>eGon</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 eGon <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> <p><strong>climate_zones_germany</strong></p> <ul> <li> <p>Climate zones in Germany</p> </li> <li> <p>source: Own representation based on DWD TRY climate zones</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>cutouts</strong></p> <ul> <li> <p>Weather data from Europe in 2011. Source: ERA5</p> </li> </ul> </li> <li> <p><strong>demand_regio_backup</strong></p> <ul> <li> <p>Electricity and heat demands</p> </li> </ul> </li> <li> <p><strong>emobility</strong></p> <ul> <li> <p>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).</p> </li> <li> <p>Reiner Lemoine Institut, June 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>entsoe</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>gas_data</strong></p> <ul> <li> <p>CH4 infrastructure</p> </li> <li> <p>Biogas demand</p> </li> <li> <p>CH4 demand</p> </li> <li> <p>Source: SciGRID_gas</p> </li> </ul> </li> <li> <p><strong>geothermal_potential</strong></p> <ul> <li> <p>Spatial distribution of deep geothermal potentials in Germany</p> </li> <li> <p>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_electricity_demand_profiles</strong></p> <ul> <li> <p>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</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_heat_demand_profiles</strong></p> <ul> <li> <p>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</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>hydrogen_network</strong></p> <ul> <li> <p>Planned H2 infrastructure</p> </li> <li> <p>Forecast H2 demand</p> </li> <li> <p>Source: fnb-gas</p> </li> </ul> </li> <li> <p><strong>hydrogen_storage_potential_saltstructures</strong></p> <ul> <li> <p>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</p> </li> <li> <p>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.</p> </li> <li> <p>License: The original data are licensed under the GeoNutzV, see <a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</a></p> </li> </ul> </li> <li> <p><strong>industrial_gas_demand</strong></p> </li> <li> <p><strong>industrial_sites</strong></p> <ul> <li> <p>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.</p> </li> <li> <p>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</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>mastr_geocoding</strong></p> </li> <li> <p><strong>nep2035_version2021</strong></p> <ul> <li> <p>Data extracted from the German grid development plan - power</p> </li> <li> <p>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pipeline_classification_gas</strong></p> <ul> <li> <p>Parameters for the classification of gas pipelines</p> </li> <li> <p>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></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pypsa_eur</strong></p> </li> <li> <p><strong>regions_dynamic_line_rating</strong></p> <ul> <li> <p>German regions suitable to model dynamic line rating</p> </li> <li> <p>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></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>re_potential_areas</strong></p> <ul> <li> <p>Eligible areas for wind turbines and ground-mounted PV systems.</p> </li> <li> <p>Reiner Lemoine Institut, January 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>wind_offshore_status2019</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>WZ_definition</strong></p> <ul> <li> <p>Definitions of industrial and commercial branches</p> </li> <li> <p>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></p> </li> <li> <p>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_households</strong><strong> </strong></p> <ul> <li> <p>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.</p> </li> <li> <p>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps:</p> <ul> <li> <p>Search for: "1000A-2029"</p> </li> <li> <p>or choose topic: "Bevölkerung kompakt"</p> </li> <li> <p>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</p> </li> <li> <p>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</p> </li> </ul> </li> <li> <p>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_population</strong></p> </li> <li> <p><strong>district_heating_shares_egon.csv</strong></p> </li> </ol>
Mirror of "ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials"
<h2>Mirrored from Joint Research Centre Data Catalogue</h2><p><a href="https://data.jrc.ec.europa.eu/collection/id-00138#datasets">https://data.jrc.ec.europa.eu/collection/id-00138#datasets</a></p><blockquote><p>This collection contains datasets from ENSPRESO, an EU-28 wide, open dataset for energy models on renewable energy potentials, at national (NUTS0) and regional levels (NUTS2) for the 2010-2050 period. Within ENSPRESO, ENergy Systems Potential Renewable Energy SOurces, technical potentials are provided for wind, solar and biomass, based on coherent GIS-based land-restriction scenarios. For wind, resource evaluation also considers setback distances as well as high resolution geo-spatial wind speed data. For solar, potentials are derived from irradiation data and available area for solar applications. For biomass, agriculture, forestry and waste sectors are considered. The temporal resolution for wind and solar is both annual and year fractions (timeslices as used by JRC-EU-TIMES). ENSPRESO complements the EMHIRES collection, that provides meteorologically derived power time series at high temporal and spatial resolution. ENSPRESO can impact the results of any energy model by improving its analyses of the competition and complementarity of energy technologies.</p></blockquote><p><a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:RUIZ%20CASTELLO%20Pablo">RUIZ CASTELLO Pablo</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:NIJS%20Wouter">NIJS Wouter</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:TARVYDAS%20Dalius">TARVYDAS Dalius</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:SGOBBI%20Alessandra">SGOBBI Alessandra</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ZUCKER%20Andreas">ZUCKER Andreas</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:PILLI%20Roberto">PILLI Roberto</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:CAMIA%20Andrea">CAMIA Andrea</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THIEL%20Christian">THIEL Christian</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:HOYER-KLICK%20Carsten">HOYER-KLICK Carsten</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:DALLA%20LONGA%20Francesco">DALLA LONGA Francesco</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:KOBER%20Tom">KOBER Tom</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BADGER%20Jake">BADGER Jake</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:VOLKER%20Patrick">VOLKER Patrick</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ELBERSEN%20Berien">ELBERSEN Berien</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BROSOWSKI%20Andre">BROSOWSKI Andre</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THR%C3%84N%20Daniela">THRÄN Daniela</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:JONSSON%20Klas">JONSSON Klas</a></p><h3>How to cite</h3><p>Ruiz Castello, P., Nijs, W., Tarvydas, D., Sgobbi, A., Zucker, A., Pilli, R., Camia, A., Thiel, C., Hoyer-Klick, C., Dalla Longa, F., Kober, T., Badger, J., Volker, P., Elbersen, B., Brosowski, A., Thrän, D. and Jonsson, K., ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials, European Commission, 2019, JRC116900.</p><p>European Commission</p><p>JRC116900</p><h3>Remarks</h3><p>The originator of this mirror requires stable and reliable URLs due to an integration of the dataset into an automated workflow. The data catalogue has frequent outages.</p>
Investigating Conditioning Factors for Transparency in Software Ecosystems
<p>Supplementary material for the paper "Investigating Conditioning Factors for Transparency in Software Ecosystems", published in the Journal of Software Engineering Research and Development (JSERD).</p> <p>Software Ecosystems (SECO) are a set of actors interacting with a distributed market centered on a common technological platform to develop products and services. In this context, transparency allows third-party developers to learn processes and elements that integrate the SECO platform. This non-functional requirement impacts the coordination of developers and the management of requirements that emerge in SECO. Although it is an essential requirement, there is still a lack of a roadmap on what constitutes transparency in SECO. Thus, this article aims to characterize conditioning factors for transparency in SECO. To do so, we conducted a systematic mapping study (SMS) and a field study to identify and analyze such factors. After investigating the literature, we selected 23 studies to analyze the state-of-the-art about transparency in SECO. Next, we conducted interviews with 16 software developers to characterize the importance of conditioning factors for transparency identified in their interaction with GitHub, a platform to support project-based ecosystems. As results, we obtained a comprehensive view of solutions, conditioning factors, processes, and concerns related to transparency in SECO, whose discussion is centered on three main topics: access to information, communication channels, and requirements engineering. We also present a conceptual framework that structures all the knowledge about transparency in SECO obtained in both studies. Regarding implications for academia and industry, researchers can find a conceptual framework to be used as a foundation for systematic approaches to understanding transparency in SECO. Practitioners can find solutions and conditioning factors that help them to adopt initiatives to contribute to the open flow of information in a SECO and, thus, attract and engage new actors to a common technological platform.</p>
Data/code for Mlawer et al. 2024 (A more transparent infrared window)
<p>Compressed file includes:</p> <ul> <li>LBLRTM input text files for both the SGP and MAO sites (TAPE5_inputs_SGP/ and TAPE5_inputs_MAO/ respectively) -- file names consist of date (YYYYMMDD) and radiosonde time in UTC (HHMMSS)</li> <li>netCDF data files with TROPoe retrieved aerosol AOD for SGP site (aerosol_data/*.nc), which input data for retrieval were taken from IMPROVE campaign (Downloaded from <a href="https://views.cira.colostate.edu//fed/QueryWizard/Default.aspx" target="_blank" rel="noopener noreferrer">https://views.cira.colostate.edu//fed/QueryWizard/Default.aspx</a> on 3 March 2024) -- file names consist of aerosol type, date (YYYYMMDD) and time in UTC (HHMMSS)</li> <li>A .csv file with retrieved aerosol AOD data provided by Connor Flynn (aerosol_data/Flynn_aod_data.csv)</li> <li>Miscellaneous data (misc_data/): <ul> <li>netCDF of data used as inputs for 3-variable retrieval script (IRwindow_SGP_daveRetrievals2_inputs.cdf)</li> <li>IDL .sav file of trace gas sensitivity analysis results (IRwindow_SGP_traceGasSensitivityThreshold_0.075thershold.sav)</li> <li>IDL .sav file of Otsu wavenumber analysis results (IRwindow_wavenumbers_that_passed_spectral_test_0bad1good_750-1290wOzone.sav)</li> <li>Excel file with three tabs containing other studies' datasets for: self, foreign, and self temperature dependence (supporting_data.xlsx)</li> </ul> </li> <li>Python example script of 3-variable retrieval (IRwindow_3varRetrieval_revised.py)</li> </ul>
Characterization of the semi-transparent perovskite solar cells
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Exploring Transparency Concerns in Social Media App Reviews: A Human-LLM Perspective
<p>This is a dataset for the "Exploring Transparency Concerns in Social Media App Reviews: A Human-LLM Perspective." paper submitted to EASE2025.</p>
Materials in Vessels Dataset, Annotated images of materials in transparent vessels for semantic segmentation
<p> Data set of materials in vessels<br> The handling of materials in glassware vessels is the main task in chemistry laboratory research as well as a large number of other activities. Visual recognition of the physical phase of the<br> materials is essential for many methods ranging from a simple task such as fill-level evaluation to the<br> identification of more complex properties such as solvation, precipitation, crystallization and phase<br> separation. To help train neural nets for this task, a new data set was created. The data set contains a<br> thousand images of materials, in different phases and involved in different chemical processes, in a<br> laboratory setting. Each pixel in each image is labeled according to several layers of classification, as<br> given below:</p> <p>a. Vessel/Background: For each pixel assign value of one if it is part of the vessel and zero otherwise.<br> This annotation was used as the ROI map for the valve filter method.</p> <p>b. Filled/Empty: This is similar to the above, but also distinguishes between the filled and empty<br> regions of the vessel. For each pixel, one of the following three values is assigned:0 (background); 1<br> (empty vessel); or 2 (filled vessel).</p> <p>c. Phase type: This is similar to the above but distinguishes between liquid and solid regions of the<br> filled vessel. For each pixel, one of the following four values: 0 (background); 1 (empty vessel); 2<br> (liquid); or 3 (solid).</p> <p>d. Fine-grained physical phase type: This is similar to the above but distinguishes between specific<br> classes of physical phase. For each pixel, one of 15 values is assigned: 1 (background); 2 (empty<br> vessel); 3 (liquid); 4 (liquid phase two, in the case where more than one phase of the liquid appears in<br> the vessel); 5 (suspension); 6 (emulsion); 7 (foam); 8 (solid); 9 (gel); 10 (powder); 11 (granular); 12<br> (bulk); 13 (solid-liquid mixture); 14 (solid phase two, in the case where more than one phase of solid<br> exists in the vessel): and 15 (vapor).<br> The annotations are given as images of the size of the original image, where the pixel value is the<br> class number. The annotation of the vessel region (a) is used in the ROI input for the valve filter net .</p> <p>4.1. Validation/testing set<br> The data set is divided into training and testing sets. The testing set is itself divided into two subsets;<br> one contains images extracted from the same YouTube channels as the training set, and therefore was<br> taken under similar conditions as the training images. The second subset contains images extracted<br> from YouTube channels not included in the training set, and hence contains images taken under<br> different conditions from those used to train the net.</p> <p>4.2. Creating the data set<br> The creation of a large number of images with a variety of chemical processes and settings could have<br> been a daunting task. Luckily, several YouTube channels dedicated to chemical experiments exist<br> which offer high-quality footage of chemistry experiments. Thanks to these channels, including<br> NurdRage, NileRed, ChemPlayer, it was possible to collect a large number of high-quality images in a<br> short time. Pixel-wise annotation of these images was another challenging task, and was performed by<br> Alexandra Emanuel and Mor Bismuth.</p> <p>For more details see: <a href="https://arxiv.org/pdf/1708.08711.pdf">Setting attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels</a></p> <p>This dataset was first published in 2017.8</p> <p>For newer and Bigger datasets see</p> <p>https://zenodo.org/record/4736111#.YbG-RrtyZH4</p> <p>https://zenodo.org/record/3697452#.YbG-TLtyZH4</p> <p> </p>
Affordable prices without threatening the oncological R&D pipeline - An economic experiment on transparency in price negotiations
<p>This is the data repository of an economic experiment on the effects of transparency regulations, conducted by the Netherlands Cancer Institute and the University of Amsterdam. We replicated the EU pharmaceutical market in a laboratory setting. In a randomized-controlled study, we analyzed how participants, 400 students located in 4 European countries, negotiated in the current system of Price Secrecy in comparison to innovative bargaining settings where either prices only (Price Transparency) or prices and R&D costs (Full Transparency) were made transparent to buyers.</p>
Procedurally generated simulation/animation of liquids in transparent containers with depth map/ segmentation map (part of transproteus dataset)
<p>Procedurally generated simulation/animation of liquids in transparent containers with depth map/ segmentation map (part of transproteus dataset)</p> <p>https://arxiv.org/ftp/arxiv/papers/2109/2109.07577.pdf</p>
Single Photon Switch and Facilitation Induced Transparency with Dual-channel Rydberg Interactions
<p>Original data for "Single Photon Switch and Facilitation Induced Transparency with Dual-channel Rydberg Interactions". Data can be opened with Matlab.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.