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S36 | UBAPMT | Prioritised PMT/vPvM substances in the REACH registration database
<p><strong>Prioritised PMT/vPvM substances in the REACH registration database</strong></p> <p>This is the 2022 update (first update) of the UBA list of prioritised persistent, mobile and toxic/very persistent and very mobile (PMT/vPvM) substances in the REACH registration database. All substances are registered under REACH (EC No 1907/2006) and meet the <a href="https://www.umweltbundesamt.de/publikationen/protecting-the-sources-of-our-drinking-water-the">PMT/vPvM criteria as proposed by UBA in 2019</a>. Compared to the first version from 2019, this 2022 update of the UBA list adds new substances and improved the PMT/vPvM assessment. This UBA list is published as UBA TEXTE xxx /2022. It is indicated if a substance would also meet the less stringent PMT/vPvM criteria as published by the European Commission (EC) in September 2021, which are currently under discussion for inclusion in the Classification, Labelling and Packaging (<a href="https://echa.europa.eu/guidance-documents/guidance-on-clp">CLP</a>) regulation (EC No 1272/2008).</p> <p><em>Reference: </em>Hans Peter H Arp, Sarah E Hale, Ivo Schliebner and Michael Neumann (2022). Prioritised PMT/vPvM substances in the REACH registration database, Texte | XXX/2022, edited by Michael Neumann and Ivo Schliebner, IV 2.3 Chemicals, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN: 1862-4804 xxx pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) Project No. (FKZ) 3719 65 408 0 and Report No. (to be announced)</p> <p><em>Previous version:</em></p> <p>The first version of this UBA list from 2019 was published as a <a href="https://www.umweltbundesamt.de/publikationen/reach-improvement-of-guidance-methods-for-the">technical note (UBA TEXTE 126/2019)</a>.</p> <p><em>Reference: </em>Hans Peter H Arp and Sarah E Hale (2019). REACH: Improvement of guidance and methods for the identification and assessment of PMT/vPvM substances, Texte | 126/2019, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN:1862-4804, 131 pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety Project No. (FKZ) 3716 67 416 0 and Report No. FB000142/ENG.</p> <p>This collection is associated with list S36 UBAPMT on the NORMAN Suspect List Exchange (<a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a>).</p>
Food and Agriculture Biomass Input–Output (FABIO) database
<p>This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry. </p> <p>The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers <strong>191 countries</strong> + RoW, <strong>118 processes</strong> and <strong>125 commodities</strong> (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on <a href="https://github.com/fineprint-global/fabio">GitHub</a>. For more information please refer to <a href="https://fabio.fineprint.global">https://fabio.fineprint.global</a>.</p> <p>The database consists of the following main components, in compressed .rds format:</p> <ul> <li>Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.</li> <li>Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.</li> <li>X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.</li> <li>L: the Leontief inverse, computed as (I – A)<sup>-1</sup>, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).</li> <li>E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).</li> <li>mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).</li> <li>mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).</li> </ul> <p>A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS</p> <p>Except of <em>X.rds</em>, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes <em>value </em>and <em>mass </em>only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see <a href="http://doi.org/10.1021/acs.est.9b03554">Bruckner et al. 2019</a>). Product, process and country classifications can be found in the file <em>fabio_classifications.xlsx</em>.</p> <p>Footprint results are not contained in the database but can be calculated, e.g. by using this script: <a href="https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R">https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R</a></p> <p> </p> <p><strong><em>How to cite: </em></strong></p> <p>To cite FABIO work please refer to this paper:</p> <p>Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. <em>Environmental Science & Technology</em> 53(19), 11302–11312. DOI: <a href="https://doi.org/10.1021/acs.est.9b03554">10.1021/acs.est.9b03554</a></p> <p> </p> <p><em><strong>License:</strong></em></p> <p>This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at <a href="mailto:martin.bruckner@wu.ac.at">martin.bruckner@wu.ac.at</a>.</p> <p> </p> <p><strong><em>Known issues:</em></strong></p> <p>The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.</p>
Passive gas plume database for metrics comparison
<p>Plume database used for the evaluation of different metrics that are presented in the submitted paper "New plume comparison metrics for the inversion of passive gases emissions". The synthetical CO2 plumes presented in the NetCDF file entitled "Synthetical_CO2_plume_database.nc" are the results of chemical transport model simulations described in the preprint available here <a href="https://amt.copernicus.org/preprints/amt-2022-48/">https://amt.copernicus.org/preprints/amt-2022-48/</a>.</p>
WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets
<p>The WS22 database provides a collection of molecular datasets that explores a broad configurational space of flexible organic molecules with varying sizes and complexity. It includes several chemical properties calculated with a quantum chemical (QM) method. Complementary to the structured datasets, this repository also provides the molecular geometries for the equilibrium structures together with the corresponding output of the QM frequency calculations. Details about the methodology, content, and structure of the WS22 datasets are provided in the README file included in this repository.</p>
Small-scale fisheries adaptations understudied in climate change hotspots - database
<p>Using a systematic review approach, we identified a global dataset of 301 reported adaptation responses of small-scale fishers to climate change. The adaptations were extracted from academic publications and grey literature (reports and Ph.D. theses) published from 2008 to 2020. The database provides coordinates and/or location, climate change hazard identified as motivating the response, small-scale fisher adaptation response, and any other stressor related to the response.</p>
The Audio Database of Hatoma Example Sentences
<p>This is a set of sound files of Hatoma Language, Southern Ryukyuan, spoken on Hatoma island, Okinawa.</p> <p>The database has 37611 sentences in Hatoma, included in Hatoma-Japanese Dictionary.</p> <p>See the audio database of Hatoma lexicon (Southern Ryukyuan) for a set of lexicons. https://doi.org/10.5281/zenodo.4560935</p>
DISASTER database on hydro-geomorphologic disasters in Portugal
<p>In the last century, Portugal was affected by several natural disasters of hydrogeomorphologic origin that often caused high levels of destruction. However, data on past events related to floods and landslides were scattered. The DISASTER project created a consistent and validated hydro-geomorphologic database for Portugal, by creating, disseminating and exploiting a GIS database on disastrous floods and landslides for the period 1865–2010, further updated until 2020.</p> <p>Data collection was steered by the concept of disaster used within the DISASTER project. Therefore, any hydro-geomorphological case is stored in the database if the occurrence led to fatalities or injuries, and missing, evacuated or displaced people, independently of the number of people affected.</p> <p>The sources of information are 16 national, regional and local newspapers that implied the analysis of 145,344 individual newspapers. The hydro-geomorphologic occurrences were stored in a database containing two major parts: the characteristics of the hydro-geomorphologic case and the corresponding damages. We provide the main results of the DISASTER database for the public.</p> <p>Further details about the data collection and exploitation can be found in the following paper: </p> <p>Zêzere, J.L., Pereira, S., Tavares, A.O. <em>et al.</em> DISASTER: a GIS database on hydro-geomorphologic disasters in Portugal. <em>Natural Hazards</em> <strong>72</strong>, 503–532 (2014). https://doi.org/10.1007/s11069-013-1018-y</p> <p>We provide a shapefile with the location of the hydro-geomorphological hazard for the period 1865-2020 for mainland Portugal and additional details about the hydrogeomorphological hazard type and subtype, date of occcurrence, year, month, day, hour, georeferencing quality, source, source date, source type, page in the source, number of human damages (fatalities, injured, evaciated, displaced and missing people), district, municipality and parish.</p> <p>Also, we provide a word document with the database codes description.</p>
Database of optical parameters for the simulation of perovskite/silicon solar cells
<p>This dataset contains a set of representative optical parameters, i.e. the wavelength dependent complex refractive index (n+ik), for common materials used in perovskite-silicon tandems. In particular: SnO2, Spiro-MEOTAD, CH3NH3PbI2, a-Si:H, undoped c-Si, MgF.<br> The data are stored in the ASCII file “<em>nk_MaterialParameters.txt</em>”, where for each material, we report three data columns: wavelength (in µm), refractive index <em>n</em> and extinction coefficient <em>k</em>.</p>
Zeolite Templated Carbon Materials - DFTB Structural Database
<p>Zeolite-templated carbon (ZTC) is a unique porous carbonaceous material in that its structure is ordered at the nanometre scale, enabling a representative periodic description at the atomistic level. A structural library for ZTC of varying compositions was created using density functional tight binding (DFTB) potentials parameterized for materials science applications (matsci-0-3). We provide here quantum chemical-refined structures of models with CH, CHO, CHON, CHOB, and CHOBN compositions with various degrees of heteroatom substitution. The "initial ZTC structure" files correspond to the initial model used in our work that was developed using molecular mechanics, empirical force fields. These structural models comprise the characteristic morphological features of highly porous carbon materials, such as open-blade surfaces, edges, saddles, and closed-strut formations, spanning a range of curvatures and characteristic sizes. The optimized structures in CIF and native DFTB file formats are organized in the "stationary structure" file based on the optimization pathways that lead to the stationary structures.</p> <p>Secondly, we carried out alternating compression and expansion of the CHO model unit cell to determine the lowest energy structure as well as to obtain the bulk modulus. The file "bulk modulus" contains two data sets that describe the deformational energy landscape of pure faujasite zeolite, Na-substituted zeolite, and the ZTC model structure.</p> <p>The file "analysis tools" is a representative compilation of utilities for file format conversion, fractional vs. Cartesian crystal coordinates, and structural analysis spreadsheets.</p> <p>The agreement between experimental measurements and the computational model is remarkable that demonstrates the power of approximate density functional theory as a cost-effective computational tool with chemical accuracy for the investigation of structure/property relationships in real-world carbon-based solids.</p>
Multi-LEX: a database of multi-word frequencies (French files)
<p>Written word frequency is a key variable used in many psycholinguistic studies and is central in explaining visual word recognition. Indeed, methodological advances on single word frequency estimates have helped to uncover novel language-related cognitive processes, fostering new ideas and studies. In an attempt to support and promote research on a related emerging topic, visual multi-word recognition, we extracted from the exhaustive Google Ngram datasets a selection of millions of multi-word sequences and computed their associated frequency estimate. Such sequences are presented with Part-of-Speech information for each individual word. An online behavioral investigation making use of the French 4-gram lexicon in a grammatical decision task was carried out. The results show an item-level frequency effect of word sequences. Moreover, the proposed datasets were found useful during the stimulus selection phase, allowing more precise control of the multi-word characteristics.</p>
Multi-LEX: a database of multi-word frequencies (English files)
<p>Written word frequency is a key variable used in many psycholinguistic studies and is central in explaining visual word recognition. Indeed, methodological advances on single word frequency estimates have helped to uncover novel language-related cognitive processes, fostering new ideas and studies. In an attempt to support and promote research on a related emerging topic, visual multi-word recognition, we extracted from the exhaustive Google Ngram datasets a selection of millions of multi-word sequences and computed their associated frequency estimate. Such sequences are presented with Part-of-Speech information for each individual word. An online behavioral investigation making use of the French 4-gram lexicon in a grammatical decision task was carried out. The results show an item-level frequency effect of word sequences. Moreover, the proposed datasets were found useful during the stimulus selection phase, allowing more precise control of the multi-word characteristics.</p>
Database of weeds in cultivation fields of France and UK, with ecological and biogeographical information
<p>The database includes a list of 1577 weed plant taxa found in cultivated fields of France and UK, along with basic ecological and biogeographical information.<br> The database is a CSV file in which the columns are separated with comma, and the decimal sign is ".".<br> It can be imported in R with the command "tax.discoweed <- read.csv("tax.discoweed_18Dec2017_zenodo.csv", header=T, sep=",", dec=".", stringsAsFactors = F)"</p> <p>Taxonomic information is based on TaxRef v10 (Gargominy et al. 2016),<br> - 'taxref10.CD_REF' = code of the accepted name of the taxon in TaxRef,<br> - 'binome.discoweed' = corresponding latine name,<br> - 'family' = family name (following APG III),<br> - 'taxo' = taxonomic rank of the taxon, either 'binome' (species level) or 'infra' (infraspecific level),<br> - 'binome.discoweed.noinfra' = latine name of the superior taxon at species level (different from 'binome.discoweed' for infrataxa),<br> - 'taxref10.CD_REF.noinfra' = code of the accepted name of the superior taxon at species level.</p> <p>The presence of each taxon in one or several of the following data sources is reported:<br> - Species list from a reference flora (observations in cultivated fields over the long term, without sampling protocol),<br> * 'jauzein' = national and comprehensive flora in France (Jauzein 1995),<br> - Species lists from plot-based inventories in cultivated fields,<br> * 'za' = regional survey in 'Zone Atelier Plaine & Val de Sèvre' in SW France (Gaba et al. 2010),<br> * 'biovigilance' = national survey of cultivated fields in France (Biovigilance, Fried et al. 2008),<br> * 'fse' = Farm Scale Evaluations in England and Scotland, UK (Perry, Rothery, Clark et al., 2003),<br> * 'farmbio' = Farm4Bio survey, farms in south east and south west of England, UK (Holland et al., 2013)<br> - Reference list of segetal species (species specialist of arable fields),<br> * 'cambacedes' = reference list in France (Cambacedes et al. 2002)</p> <p>Life form information is extracted from Julve (2014) and provided in the column 'lifeform'.<br> The classification follows a simplified Raunkiaer classification (therophyte, hemicryptophyte, geophyte, phanerophyte-chamaephyte and liana). Regularly biannual plants are included in hemicryptophytes, while plants that can be both annual and biannual are assigned to therophytes.</p> <p>Biogeographic zones are also extracted from Julve (2014) and provided in the column 'biogeo'.<br> The main categories are 'atlantic', 'circumboreal', 'cosmopolitan, 'Eurasian', 'European', 'holarctic', 'introduced', 'Mediterranean', 'orophyte' and 'subtropical'.<br> In some cases, a precision is included within brackets after the category name. For instance, 'introduced(North America)' indicates that the taxon is introduced from North America.<br> In addition, some taxa are local endemics ('Aquitanian', 'Catalan', 'Corsican', 'corso-sard', 'ligure', 'Provencal').<br> A single taxon is classified 'arctic-alpine'.</p> <p>Red list status of weed taxa is derived for France and UK:<br> - 'red.FR' is the status following the assessment of the French National Museum of Natural History (2012),<br> - 'red.UK' is based on the Red List of vascular plants of Cheffings and Farrell (2005), last updated in 2006.<br> The categories are coded following the IUCN nomenclature.</p> <p>A habitat index is provided in column 'module', derived from a network-based analysis of plant communities in open herbaceous vegetation in France (Divgrass database, Violle et al. 2015, Carboni et al. 2016).<br> The main habitat categories of weeds are coded following the Divgrass classification,<br> - 1 = Dry calcareous grasslands<br> - 3 = Mesic grasslands<br> - 5 = Ruderal and trampled grasslands<br> - 9 = Mesophilous and nitrophilous fringes (hedgerows, forest edges...)<br> Taxa belonging to other habitats in Divgrass are coded 99, while the taxa absent from Divgrass have a 'NA' value.</p> <p>Two indexes of ecological specialization are provided based on the frequency of weed taxa in different habitats of the Divgrass database.<br> The indexes are network-based metrics proposed by Guimera and Amaral (2005),<br> - c = coefficient of participation, i.e., the propensity of taxa to be present in diverse habitats, from 0 (specialist, present in a single habitat) to 1 (generalist equally represented in all habitats),<br> - z = within-module degree, i.e., a standardized measure of the frequency of a taxon in its habitat; it is negatve when the taxon is less frequent than average in this habitat, and positive otherwise; the index scales as a number of standard deviations from the mean.</p>
Database of Ultrasonic Transducer Radiation Characteristics
<p>Database of measurements of different properties of ultrasonic transducers of different types.</p> <ul> <li>On-axis frequency response measurements of multiple copies of the same transducer model, measured between 20 kHz and 160 kHz in an anechoic space. </li> <li>Directivity patterns of multiple copies of the same transducer model, measured for every degree in an anechoic space. Calculated for every 10 Hz between 20 kHz and 160 kHz.</li> <li>Frequency response measurements for every degree for a single transducer of each type, measured between 20 kHz and 160 kHz in an anechoic space.</li> </ul> <p>The datasets are stored in HDF5 files created using h5py. An example Jupyter notebook is included to show how the plots were created.</p> <p> </p> <p>The transducers measured are:</p> <ul> <li>Murata MA40S4S</li> <li>Camdenboss CDT40K1007T</li> <li>Multicomp MCUST10P40B07RO</li> <li>Multicomp 400PT16P</li> <li>Multicomp MCUSD14A40S09RS</li> <li>Multicomp MCUSD14A48S09RS</li> <li>Multicomp MCUSD14A58S09RS</li> </ul>
Global database of Coastal Characteristics (GCC)
<p>This dataset present a Global database of Coastal Characteristics (GCC) with 80 indicators spanning the</p> <ul> <li>geophysical,</li> <li>hydrometeorological and</li> <li>socioeconomic</li> </ul> <p>environment, at a high alongshore resolution of 1 km and provided at ~730,000 points along the global ice-free coastline. The latest freely available global datasets and a global high-resolution transect system are used to derive these indicators.</p> <p>The geophysical indicators include coastal slopes and elevation maxima, land-use, presence of vegetation or sandy beaches.The hydro-meteorological indicators involve water level, wave conditions and meteorological conditions (rain and temperature). Additionally, the socioeconomic indices are related to population, GDP and presence of critical infrastructure (roads, railways, ports and airports).</p> <p>The indicators are provided in three comma-separated values (CSV) files, one for each group:</p> <ul> <li>GCC_geophysical.csv</li> <li>GCC_hydrometeorological.csv</li> <li>GCC_socioeconomic.csv</li> </ul> <p>Information for each individual indicator, including its name, long name (description), units and type, are provided in the meta_data.yml file.</p>
UK Low Carbon Technology Database (UKLCTD)
<p><strong>UK Low Carbon Technology Database (UKLCTD)</strong></p> <p>Version used for revised paper submitted to Nature Energy: Sheridan Few, Predrag Djapic, Gpran Strbac, Jenny Nelson, Chiara Candelise, "A geographically disaggregated approach to integrate low-carbon technologies across local electricity networks"</p> <p><strong>Overview</strong></p> <p><br>This repository contains:</p> <p>(1) The United Kingdom Low Carbon Technology Database (UKLCTD), a collection of real geographically disaggregated data on current deployment of small scale photovoltaics (PV), heat pumps (HPs), electric vehicles (EVs), network inrastructure, domestic and nondomestic meter density, electricity demand, and rurality at an LSOA / Scottish Data Zone level. (UKLCTD.csv)</p> <p>(2) Scenarios for future deployment of PV, HPs, EVs, and battery storage upto 2050 at an LSOA level based upon current data, National Grid's Future Energy Scenarios (FES) and UKPN, NPG, and WPD's Distribution Future Energy Scenarios (DFES). (UKLCTD_Scenarios_DFES_base_[date].csv, 2050 file has PV deployment capped at two per meter)</p> <p>(3) Raw data from which each of the above are generated, and R scripts used to generate the above databases from raw data. Links to sources of raw data are included in scripts to facilitate upadates to this framework as new data becomes available. (UKLCTD.zip)</p> <p><br><strong>Usage</strong></p> <p>R scripts in the zip file have a short comment at the start describing their function. Before running, 'root_path' variable will need to be updated in each script to reflect the path these files are kept in on your local repository.</p> <p>The data may be explored using the following script:</p> <p>- Import_UKLCTD.R</p> <p>To generate the UKLCTD and scenarios from scratch, scripts are intended to be run in this order (names mostly self explanatory)</p> <p>- Generate_UKLCTD.R<br>- Add_substations_to_UKLCTD.R<br>- Add_Scottish_rurality_to_UKLCTD.R<br>- Generate_NG_scenarios.R<br>- Add_DFES_scenarios_w_plot.R<br>- Cap_Deployment.R</p> <p><br>Each of these scripts generates data used by subsequent scripts. These are broken down into stages and commented as far as possible.</p> <p><strong>Data Structure</strong></p> <p>Data: All raw data is in "Input_Data". This data can be updated as new information becomes available (input data files, sheets, and cells referred to in the above scripts will likely need to be updated accordingly). Data produced by these scripts in "Intermediate Data" and "Output Data" folders depending on whether it is used by subsequent scripts. Plots are generated in the "Plots" folder</p> <p><strong>Attribution</strong></p> <p>If this framework has been useful, please cite the following papers outlining our methodology:</p> <p>Few, S., Djapic, P., Strbac, G., Nelson J., Candelise C. A geographically disaggregated approach to integrate low-carbon technologies across local electricity networks. <em>Nat Energy</em> (2024). <a href="https://doi.org/10.1038/s41560-024-01542-6" target="_blank" rel="noopener">https://doi.org/10.1038/s41560-024-01542-6</a></p> <p>Few, S., Djapic, P., Strbac, G., Nelson J., Candelise C. Assessing Local Costs and Impacts of Distributed Solar PV Using High Resolution Data from across Great Britain. <em>Renewable Energy</em> 162 (2020) 1140–50. <a href="https://doi.org/10.1016/j.renene.2020.08.025" target="_blank" rel="noopener">https://doi.org/10.1016/j.renene.2020.08.025</a></p> <p> </p>
Database - Bridge clogging and debris - July 2021 flood
<p><span>This dataset documents 71 floating debris accumulations at bridges following an extreme hydrological event that hit Belgium and Germany in July 2021. Data were collected from various sources including public authorities’ documents, public online database, post event pictures and field visits. The dataset covers bridge geometry, flood conditions and debris accumulation. In particular, it systematically details deposits dimensions and classifies deposits components, which contain a significant portion of man-made objects, in addition to driftwood. </span></p> <p><span>The dataset is stored in a single CSV file, with semicolon separator. The file contains 72 lines and 63 columns. First line contains the label of the columns parameters. Each of the 71 following lines contains the data of one bridge and corresponding accumulation. </span></p> <p><span>A data descriptor is under review in Nature Scientfic Data: <br>Erpicum S., Poppema D., Burghardt L., Benet L., Wüthrich D., Klopries E., Dewals B., (submitted) A dataset of floating debris accumulation at bridges after July 2021 Flood in Germany and Belgium, Nature Scientific Data<br></span></p>
Organic Matter Database (OMD)
<p>Agricultural, fisheries, forestry and agro-processing activities produce large quantities of residues, by-products and waste materials every year. However, data on such residues and by-products are not readily available. We present a global organic matter database (OMD) of residues and by-products from agriculture, fisheries, forestry and related industries. The OMD is the first of its kind consolidating quantities of residues and by-products from agriculture, fisheries, forestry and allied industries globally. Residue datasets were estimated from the FAOSTAT and FishStatJ databases using prescribed equations and conversion factors.</p>
SADIE II Database
<p><strong>HRIRs</strong> were measured for each subject for a regular latitude-longitude distribution (15° elevation and variable azimuthal resolution) and for at least 12 key Ambisonic loudspeaker configurations: Octahedron (x3 orientations), Cube, Bi-Rectangle (x3 orientations), Icosehedron, 7-Design, 26pt Lebedev Grid, Pentakis Icosedodecahedron and 50pt Lebedev Grid. They were taken using Genelec 8010s at a radius of 1.2m from the center of the subject's head.<br><strong>BRIRs</strong> were measured for each subject for a 50pt Lebedev Grid Configuration. They were taken using Genelec 8030/40s at a radius of 1.5m from the centre of the subject's head.<br><strong>Headphone Responces</strong> were measured for beyerdynamic DT990 headphones.<br><br>Audio data is available in 44.1K - 16bit, 48K - 24bit and 96K - 24bit .wav and AES 69 SOFA format. Measurements are labeled by azimuth and elevation in degrees accurate to 1 decimal place. Azimuth is measured anti-clockwise (to the left) about the horizontal plane. 0° marks directly in front of the subject. Elevation is measured above (positive) and below (negative) the horizontal plane. These data sets are specifically supported the Ambix Ambisonic decoder configuration files found <a href="https://www.york.ac.uk/sadie-project/ambidec.html">here</a>. By default, the config. files were designed to load measurements at 48KHz.<br><br>All data was recorded and developed at The Audio Lab, Department of Electronic Engineering, University of York, UK by Cal Armstrong, Lewis Thresh and Gavin Kearney. For any further details please <a href="https://www.york.ac.uk/sadie-project/contact.html">get in touch</a>.<br><br>The associated journal paper is published Open Access: A Perceptual Evaluation of Individual and Non-Individual HRTFs: A Case Study of the SADIE II Database. DOI: <a href="https://www.mdpi.com/2076-3417/8/11/2029" target="_blank" rel="noopener">10.3390/app8112029</a></p>
Triangle of Biomedicine Framework to Analyze the Citations' Impact on Categories Dissemination in the PubMed Database
<p>This is the data and the most relevant script of the paper 'Triangle of Biomedicine Framework to Analyze the Citations’ Impact on Categories Dissemination in the PubMed Database'.</p>
H2020 ENODISE: UTWE Configuration A Experimental Databases with Mitigation
<p>This database contains the datasets for the aeroacoustic experiments conducted at the University of Twente on the H2020 ENODISE project, Task 6.1 (experimental databases with mitigation), Configuration A1 - Wall mounted.</p> <p><br>Configuration A1 consists of a propeller ingesting a zero-pressure-gradient boundary layer. Far-field acoustics are measured using two microphone arrays, to assess directivity and sound pressure levels. Different treatments are used at the flat plate underneath the propeller to attenuate the noise reflected by the plate. Two main strategies are analyzed: tuned quarter-wavelength resonator arrays - to reduce tonal noise - and a broadband noise absorber (metal foam). The quarter-wavelength resonator arrays are tested in different geometrical distributions. </p> <p><br>That forms part of deliverables D6.4, D6.5, and D6.6 - see reports for further information regarding measurement techniques. Included are descriptive READMEs. The data is in the HDF5 format and comprises mean, spectral, and other processed data types in engineering units. CAD files are also added, to properly describe the setup and its components.</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.