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191 results for “Transparency”
Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling - Supplementary Material
<p>Supplementary material for the manuscript "Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling".</p> <p>This deposit contains all data and visualization scripts needed to replicate results in the manuscript.This includes user created figures, model input files, model output files, configuration files for running the workflow, and all scripts needed to process results.</p> <p>In addition to the European Commission, we acknowledge that Trevor Barnes' contribution to this paper was funded via a Mitacs Globalink Research Award, grant number IT2569</p>
Raw Data for: Fast rate dual-comb spectrometer in the water-transparent 7.5-11.5 μm region
<p>This repository shares the data acquired and used to produce the figures in the paper "Fast rate dual-comb spectrometer in the water-transparent 7.5-11.5 μm region" published in Optics Letters (DOI: <a href="https://doi.org/10.1364/OL.515199">https://doi.org/10.1364/OL.515199</a>)</p> <p><br>The "Figure_Data" zip file contains a ".txt" file for each Figure present in the Main Paper and Supplemental Document. The raw data acquired by the FT spectrometer and uploaded here for Figure 2 are afterward smoothed and normalized properly in the Main Paper to better show the spectra. Data shown in Figure 3 are calculated from the corresponding raw data. Data used for Figure 4 are calculated from the corresponding raw blank DCS data. Data for Figure 5, 6 and S1 are retrieved from the corresponding raw Gas DCS data upon Cepstral removal of the spectral baseline and spectral fit exploiting the Hitran Database.</p> <p><br>The "Raw_Data" zip file contains the postprocessed Data of the DCS measurements and the RIN data as acquired. The Raw data of the DCS measurements as acquired are too large to be uploaded here, but may be obtained from the authors upon request.<br>"NH3_1o6kHz_IGMmean", "N2O_5o1kHz_IGMmean", "N2O_26kHz_IGMmean" contain the DCS interferogram averaged over 15s of acquisition for Gas measurements, performed with different DeltaFrep as described in the main text of the paper.<br>"Blank_1o6kHz_IGMs", "Blank_5o1kHz_IGMs", "Blank_26kHz_IGMs" contain all the phase corrected DCS interferograms acquired over 100ms, used to calculate the average SNR shown in Figure 4.<br>"RIN_DFG" contain the raw data acquired from the detector for RIN calculations, with an average voltage values of 25mV.</p> <p><br>The scripts for processing the Raw Data and retrieve the data as shown in the paper are home made.</p> <p> </p> <p> </p>
Increased transparency in accounting conventions could benefit climate policy
<p>Datasets of emissions from 1750-2020 and spreadsheet of calculations supporting the manuscript: Wedderburn-Bisshop, G. 2025 <span>Increased transparency in accounting conventions could benefit climate policy</span></p> <p> </p> <h1>Abstract</h1> <p>Greenhouse gas accounting conventions were first devised in the 1990’s to assess and compare emissions. Several assumptions were made when devising these conventions that remain in practice, however recent advances offer potentially more consistent and inclusive accounting of greenhouse gases. We apply these advances, namely: gross accounting of CO<sub>2</sub> sources; linking land use emissions with sectors; using Effective Radiative Forcing (ERF) rather than Global Warming Potentials (GWPs) to compare emissions; including both heating and cooling emissions, and including loss of additional sink capacity (LASC). We compare these results with conventional accounting and find that this approach boosts perceived carbon emissions from deforestation, and finds agriculture, the most extensive land user, to be the leading emissions sector and to have caused 60% (32%-87%) of ERF change since 1750. We also find that fossil fuels are responsible for 17% of ERF, a reduced contribution due to masking from cooling co-emissions. We test the validity of this accounting and find it useful for determining sector responsibility for present-day warming and for framing policy responses, while recognising the dangers of assigning value to cooling emissions, due to health impacts and future warming.</p>
Raw data for Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications
<p>In the following the raw data lying the foundation of the paper “Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications” (Lopez-Garcia et al.) published in Solar Rapid Research Letters, DOI: 10.1002/solr.202100909 (2021), are described. They were obtained under the funding provided by the European Union H2020 Framework Programme under Grant Agreement no. 826002 (Tech4Win) and by the Mater-One (Refs. PID 2020-116719RB-C42 and PID 2020-116719RB-C41) and SCALED (Ref. PID 2019-109215RB-C4) projects funded by the Spanish MCIN/AEI/10.13039/5011000110033.</p> <p>UV–vis measurements were acquired with a dual-beam spectrophotometer setup (Perkin Elmer Lambda L35) in transmittance mode (light source and detector normal to sample’s surface (i.e., 0<sup>o</sup>)) and in reflectance mode (with an Integrating sphere) scanning from 300 to 800 nm.</p> <p>J–V measurements under illumination were carried out using a homemade setup consisting on a AAA solar simulator calibrated using a NREL-certified Si reference solar cell (Abet Technologies, Model 15150). Electrical measurements were carried out with a source-measure unit (Keithley 2400) in four-wire sense mode, controlled by the software Tracer (ReRa solutions) using a IEEE 488 GPIB Instrument Control Device (National Instruments GPIB-USB-HS).</p>
TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images
<p>We present TransProteus, a dataset, for predicting the 3D structure and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in many fields and depends heavily on vision. This work supplies a new procedurally generated dataset consisting of 50k images of liquids and solid objects inside transparent containers. The image annotations include 3D models and material properties (color/transparency/roughness...) for the vessel and its content. The synthetic (CGI) part of the dataset was procedurally generated using 13k different objects, 500 different environments (HDRI), and 1450 material textures (PBR) combined with simulated liquids and procedurally generated vessels. In addition, we supply 104 real-world images of objects inside transparent vessels with depth maps of both the vessel and its content.</p> <p>Note that there are two files here:</p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z</a></p> <p>and</p> <p><br> <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z </a>, contain subset of the virtual CGI data set.</p> <p>https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z</p> <p><a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TransProteus_RealSense_RealPhotos.7z">TransProteus_RealSense_RealPhotos.7z </a>: Contain real-world photos scanned with real sense with depth map of both the vessel and its content</p> <p>See ReadMe file in side the downloaded files for more details</p> <p>The full dataset (>100gb) can be found here:</p> <p><a href="https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV">https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV</a></p> <p>https://<a href="http://icedrive.net/1/6cZbP5dkNG">icedrive.net/1/6cZbP5dkNG</a></p> <p>See: <a href="https://arxiv.org/pdf/2109.07577.pdf"> https://arxiv.org/pdf/2109.07577.pdf</a> for more details</p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">**This dataset is complementary to LabPics dataset with 8k real images of materials in vessels in chemistry labs, medical labs, and other settings. The LabPics dataset can be downloaded from here:</a></strong></p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">https://zenodo.org/record/4736111#.YVOAx3tE1H4</a></strong></p> <p> </p> <p><strong>************************************************************************************</strong></p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z </a>and <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z</a></p> <p>The two folders contain relatively similar data styles.<br> The data in No_Shift contain images that were generated with no camera shift in the camera paramters. If you try to predict 3d model from an image as a depth map, this is easier to use (Otherwise, you need to adapt the image using the shift). For all other purposes, both folders are the same, and you can use either or both. In addition, a real image dataset for testing is given in the RealSense file.</p> <p> </p> <p> </p> <p> </p>
Fig. 6 in Tracking transparent monogenean parasites on fish from infection to maturity
Fig. 6. Mean parasite counts of Neobenedenia sp. infecting the head (A), body (B) and fins (C) of Lates calcarifer over time. 'a', 'b' and 'c' = differences between pairs of means determined using Tukey's HSD test.
Fig. 4 in Tracking transparent monogenean parasites on fish from infection to maturity
Fig. 4. Neobenedenia sp. mean infection success on Lates calcarifer over time. 'a', 'b' and 'c' = differences between pairs of means determined using Tukey's HSD test, p <0.05.
Fig. 5 in Tracking transparent monogenean parasites on fish from infection to maturity
Fig. 5. Neobenedenia sp. distribution on the body surface of Lates calcarifer over time. A kernel spatial point analysis was used to estimate the number of parasites/unit of measure2. Dhat values show the rank of the data within 99 simulations of randomly distributed points. Complete spatial randomness is rejected with values between 90 and 100.
Fig. 3 in Tracking transparent monogenean parasites on fish from infection to maturity
Fig. 3. Live fluorescent Neobenedenia sp. attached to Lates calcarifer over time. Parasites observed attached to fish following 15 min (A), 30 min (B), 2 h (C), 48 h (D), 96 h (E) and 16 d (F) post-infection. Arrow shows the haptor of Neobenedenia sp. A slightly higher exposure was used when photographing parasites at 16 days post-infection to account for faded fluorescence. Scale bar = 100 Mm.
Fig. 1 in Tracking transparent monogenean parasites on fish from infection to maturity
Fig. 1. Lates calcarifer microhabitat terminology (A) and body surface regions (B) used for statistical analysis. af = anal fin; cf = caudal fin; cp = caudal peduncle; dhf = dorsal hard fin; dsf = dorsal soft fin; e = eye; h = head; m = mandible; mb = middle body; op = operculum; plf = pelvic fin; ptf = pectoral fin; ub = upper body; vb = ventral body. B = body; F = fins; H = head. Terminology is based on Helfman et al. (2009) and Roberts and Ellis (2012).
Fig. 2 in Tracking transparent monogenean parasites on fish from infection to maturity
Fig. 2. Live fluorescent Neobenedenia sp. juveniles attached beneath the scales of Lates calcarifer (A, B) and attached to the surface of the fish scales (C). Parasites are 1 h old (A, B) and 2 h old (C). Scale bar = 100 Mm.
Figure 4. After merging, overview is more transparent. Tens of persons were merged together into clusters in order to clarify the visualization. Firms and persons are recognized based on their icons.-Browsing Semantic Data in Slovakia
<p>The usefulness of such visualization has its key points regarding connections. Thanks to SBR browsing module, we were able to get 22 firm records for “Váhostav” query. Between any 2 companies, connections may be (and often are) not bidirectional, so, in order to navigate through connections, we have refined all 22 records. Although, even being filtered, graph is still complex. And it is possible to further navigate and search for outgoing connections, for example firm “MERLIN TRADE, a.s.” on Fig.4 contains item on “Ján Kato”, which is already included in our graph and connected to “VÁHOSTAV&SK&DEVELOPEMENT” on bottom left side and “VÁHOSTAV&SK, a.s.” in the center. Edge coloring and drawing is helpful with overlapped edges. For methods of visualization, including coloring, we refer to studies of H. Omote and K. Sugiyama (2006), and I. Herman, G. Melanon, and M. S. Marshall (2000) or our study on graph clutter filtering and connectivity distance (Mojzis & Laclavik, 2014).</p>
IEEE ISTAS'13 Symposium - Investigating Transparency
<p>In the leadup to the 2013 IEEE International Symposium on Technology and Society (ISTAS): Social Implications of Wearable Computing and Augmediated Reality in Everyday Life which had a theme of 'smartworld' the concept of identity awareness of research data, principally tracking research data and attributing digital object identifiers was a hot topic. Alexander Hayes in this video engages and explores with a range of interested researchers in how to make 'real' this concept, calling on these participants in a live streamed event to respond given the social and ethical implications or 'long tail' effect of these technologies. This livestream session was originally published at https://youtu.be/3AXjxIeSUZI</p>
High spatiotemporal resolution free surface detection using cost-effective video equipment and computer vision techniques in nearly stationary flow along a transparent wall in the laboratory
<p>The identification of the air-water interface in free surface flows traditionally involves intrusive techniques or costly equipment. Non-intrusive alternatives, such as computer vision, are emerging as highly effective substitutes or supplements for more invasive techniques in laboratory measurements, thanks to their straightforward implementation and cost efficiency. This research specifically delves in the conjunction of various naive techniques, exploring their collective precision in detecting the air-water interface along transparent walls in laboratory. A detection technique based on the double gradient of the image is applied and thoroughly examined. The study progresses through multiple refinement stages, culminating in a method that is both cost effective and easy to implement. This methodology allows for large-scale, high resolution measurements (200 mm × 1800 frames per video at a 0.25 mm, 50 Hz resolution), offering both spatial and temporal measurements by adeptly detecting the free surface along transparent walls.</p>
Delphi Study Data: Rounds 1 and 2 for Enhancing Efficiency and Transparency in Ghana's Rental Housing Market
<p><span>The dataset includes responses from two rounds of a Delphi study conducted among experts in the real estate sector, policymakers, and government officials in Ghana. The data captures both quantitative and qualitative insights on factors influencing rental housing decisions.</span></p> <ul> <li><span>Round 1</span><span>: Initial ratings of factors such as rental cost, proximity to work, landlord responsiveness, and availability of amenities.</span></li> <li><span>Round 2</span><span>: Pairwise comparisons of the top factors identified in Round 1, used for AHP analysis to determine their relative importance.</span></li> </ul>
Raw data for UV-Selective Optically Transparent Zn(O,S)-Based Solar Cells
<p>In the following the raw data lying the foundation of the paper “UV-Selective Optically Transparent Zn(O,S)-Based Solar Cells” (Lopez-Garcia et al.) published in Solar Rapid Research Letters Vol. 4, 2000470, 2020, are described. They were obtained under the funding provided by the European Union H2020 Framework Programme under Grant Agreement no. 826002 (Tech4Win) and by the Basque Country PI2018-08 (PISCES).</p> <p>UV–vis measurements were acquired with a dual-beam spectrophotometer setup (Perkin Elmer Lambda L35) in transmittance mode (light source and detector normal to sample’s surface (i.e., 0<sup>o</sup>)) scanning from 300 to 800 nm.</p> <p>Raman spectroscopy was performed with a FHR640 Horiba Jobin–Yvon spectrometer coupled to a Raman probe developed at Institut de Recerca en Energia de Catalunya (IREC) and a cryogenically cooled charge coupled device detector. Measurements were carried out in backscattering configuration and with a 325 nm UV laser as the excitation wavelength. An excitation power density of about 25W/cm<sup>2</sup> was used to inhibit thermal effects on the samples. The Raman shift was calibrated using a Si monocrystal reference and adjusting the Raman shift for the main Si band at 520 cm<sup>-1</sup>.</p> <p>FE-SEM images were acquired with a ZEISS Auriga Series system. The images were acquired at 5 kV, aperture of 20 μm, and working distance of around 4mm with the InLens detector.</p> <p>J–V measurements under illumination were carried out using a homemade setup consisting on a 150W xenon broadband arc Lamp (Thorlabs SLS401) calibrated using a NREL-certified Si reference solar cell (Abet Technologies, Model 15150). Electrical measurements were carried out with a source-measure unit (Keithley 2400) in four-wire sense mode, controlled by the software Tracer (ReRa solutions) using a IEEE 488 GPIB Instrument Control Device (National Instruments GPIB-USB-HS).</p> <p>EQE curves were obtained using a spectral response system (Bentham PVE300) calibrated with a Si photodiode.</p>
Fig. 3 in Relationships between water transparency and abundance of Cynodontidae species in the Bananal floodplain, Mato Grosso, Brazil
Fig. 3. Cluster dendrogram based on relative abundance in biomass of Cynodontidae species and water transparency from 15 sampling sites in the Bananal floodplain, Mato Grosso, Brazil (UPGMA algorithm, Euclidian distance). See Table 1 for site codes.
Fig. 1 in Relationships between water transparency and abundance of Cynodontidae species in the Bananal floodplain, Mato Grosso, Brazil
Fig. 1. Partial map of South America showing the location of the study area in the Bananal floodplain. The detailed map depicts the sampling sites (circles) in the rios Araguaia and Mortes basins. See Table 1 for site codes.
Fig. 2 in Relationships between water transparency and abundance of Cynodontidae species in the Bananal floodplain, Mato Grosso, Brazil
Fig. 2. Linear regression between relative abundance in biomass (a) and number of individuals (b) of Cynodontidae species and water transparency from 15 sampling sites in the Bananal floodplain, Mato Grosso, Brazil.
Astroclimate measurements on several points over Eastern hemisphere in 2-mm and 3-millimeter atmospheric transparency windows using tipping radiometer
<p>We are presenting results of the atmospheric propagation observations which our research group has been conducting since 2012. Over this time, we have gathered statistical data of atmospheric propagation over a number of sites which possibly can be used for radio astronomical observations in the millimetre and sub-millimetre bandwidths. The Zenith opacity was studied by the atmospheric dip method in the 3 mm and 2 mm atmospheric windows. The dataset is recommended to use for estimation of atmospheric propagation for the purpose of radio astronomy and telecommunications.</p> <p>The “tau-meter” (named MIAP-2) allows us to estimate an integral absorption by using the atmospheric dip method. The hardware includes a radiometric system comprising two self-contained radiometers operating in two different bands of 84-99 GHz (λ ~ 3 mm) and 132-148 GHz (λ ~ 2 mm), a rotary support, a control, and a firmware system. The radiometers work in modulation mode within 36 Hz modulation frequency. Floating mirror allows radiometer to scan the sky in the interval of 0 – 88.5 elevation angles (6 total). The output record contains the voltages of synchronous detector for each angle in 2 wavebands. The voltage is proportional to brightness temperature of the sky in corresponding waveband. (It has negative value for technical reasons.)</p> <p>The dataset contains raw data observed by radiometer: Local date and time, a several detector voltages on different elevation angles for 2 wavebands, as well as service information header. The first 6 columns contain optical depth calculated by old method and it’s no more used in data processing since the new algorithm has been invented in 2018.</p>
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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.