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417 results for “federal”
mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download:<br> • Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units)<br> • Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands )<br> • Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types)<br> • associated reports (PDF; statistical and additional information on the classifiaction results and workflow)<br> The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided: <br> • Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands, 1x for classification of substrate types)<br> • Color Coding table for the visualization of the classifiaction units (.xlsx)</p>
A Geospatial Source Selector for Federated GeoSPARQL Querying - Datasets
<p>This dataset comprises of a set of individual RDF files. It contains 3 data layers that cover Austria (namely Administrative data, Snow cover data, and Crop-type data), and each layer is also divided geospatially. Thus, each RDF file contains only one thematic layer and refers to a specific area.</p> <p>This data can be used in the context of experimenting with federated query processors. Thus, if we deploy each RDF file in a separate GeoSPARQL endpoint, we will have a resulting federation of 34 GeoSPARQL source endpoints. The objective of this scenario is to evaluate the effectiveness of a source selection mechanism of a federation engine, and, in particular, if the source selector is aware of the geospatial nature of the source endpoints.</p>
Waterloo, Ontario Federal and Provincial Voting Intention and Vaccine Hesitancy
<p>This it the initial release of federal and provincial voting intention in Waterloo Region in the spring of 2022, commissioned by the Laurier Institute for the Study of Public Opinion and Policy.</p>
Outputs of the paper "Blockchain-enabled Server-less Federated Learning"
<p>This repository contains the different outputs generated for the paper "Blockchain-enabled Server-less Federated Learning", submitted to Computer Networks (COMNET) journal. Two types of outputs are provided:</p> <ol> <li><strong>Blockchain queue simulator (output_queue_simulator):</strong> results of the simulations done in the batch-service queue simulator (https://github.com/fwilhelmi/batch_service_queue_simulator) to characterize the queue latency of blockchain applications.</li> <li><strong>Tensorflow (output_tensorflow):</strong> results of the simulations done in Tensorflow Federated (TFF), resulting from the application of different models to the federated EMNIST dataset.</li> </ol> <p>Each folder also includes the scripts used to execute the corresponding simulations. For more details, see the repository in https://github.com/fwilhelmi/blockchain_enabled_federated_learning</p>
Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions
<p>This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model.</p> <p>The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions.</p> <p>The two files, <em>SWA9505V2_Gsim_PMA_WRES.mat</em> and <em>SWA9505V2_Gsim_PMA_WMP.mat</em>, represent model outputs from the two hydropower models, WRES and WMP respectively.</p> <p>Each file contains 6 variables:</p> <p>1) “Models”: the 10 global climate models (GCMs).</p> <p>2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015).</p> <p>3) “PMA_G_mn_6605”: 1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH.</p> <p>4) “PMA_G_mn_1150”: Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation.</p> <p>5) “PMA_G_yr_6605”: 1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH.</p> <p>6) “PMA_G_yr_1150”: Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation.</p> <p>The following journal paper details the method in creating the dataset:</p> <p><strong>Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants</strong></p> <p><strong>Zhou et al. (2022) Preparing for submission to Environmental Research Letters.</strong></p>
The #BTW17 Twitter Dataset - Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag
<p>The German Bundestag elections are the most important democratic elections of Germany. This dataset comprises Twitter interactions related with German politicians of the most important political parties over several months in the (pre-)phase of the German election campaigns in 2017. The Twitter accounts of 364 politicians (that is approximately half of the German parliament, the German Bundestag) were followed for almost half a year. The collected data comprise of about 10 GB of Twitter raw data generated by more than 120.000 active Twitter users generating more than 1.200.000 tweets during the pre- and hot-phase of the election campaigns for the 19th German Bundestag. <br> The dataset can be used to study how political parties, their followers and supporters make use of social media channels like Twitter in the context of political election campaigns and what kind of content is shared.</p> <p>The following files contain relevant context information:</p> <ul> <li><strong>crawled-pages.json</strong> contains the URLs of the official party faction websites of the 18th German Bundestag that were crawled to identify the Twitter screennames of German politicians of all Bundestag factions. Because the <em>Alternative für Deutschland (AfD)</em> and the <em>Freie Demokratische Partei (FDP)</em> were not part of the 18th German Bundestag (but it was likely that they will enter the 19th German Bundestag) other official websites were selected to crawl for relevant and representative politicians for these both parties (in case of the <em>AfD</em> this was the website of the directorate of the <em>AfD</em> federal party and the list of members of the European Parliament, in case of the <em>FDP</em> this was the website of the executive committee of the <em>FDP</em> federal party of Germany).</li> <li><strong>followed-accounts.json</strong> contains the (manually checked and edited) crawling result of 327 Twitter screennames of politicians that have been observed via the Twitter streaming API to collect this dataset.</li> </ul>
Рис. 1. Карта-схема литоральных Экспедиций Института биологии морЯ (Национального научного центра морской биологии им. А.В. Жирмунского ДВО РАН) и Дальневосточного государственого университета (Дальневосточного федерального университета) (по: Ivanova, Tsurpalo [2012], с дополнениЯми). Fig. 1. A schematic map of intertidal expeditions performed by the Institute of Marine Biology (A.V. Zhirmunsky National Scientific Centre of Marine Biology FEB RAS) and Far Eastern State University (Far Eastern Federal University) (after Ivanova, Tsurpalo [2012], with additions). in Bivalve mollusks of the intertidal zone of the Far Eastern seas of Russia
Рис. 1. Карта-схема литоральных Экспедиций Института биологии морЯ (Национального научного центра морской биологии им. А.В. Жирмунского ДВО РАН) и Дальневосточного государственого университета (Дальневосточного федерального университета) (по: Ivanova, Tsurpalo [2012], с дополнениЯми). Fig. 1. A schematic map of intertidal expeditions performed by the Institute of Marine Biology (A.V. Zhirmunsky National Scientific Centre of Marine Biology FEB RAS) and Far Eastern State University (Far Eastern Federal University) (after Ivanova, Tsurpalo [2012], with additions).
Рис. 1. Вьюн НикоΛьского M. nikolskyi Vasil'eva, 2001 (пойменный воΔоем реки БурΛа, Панкрушихинский район АΛтайского края) in Vasil'Eva, 2001 A As New Specie In The Fish Fauna Of Altai Krai (West Siberia, The Russian Federation)
Рис. 1. Вьюн НикоΛьского M. nikolskyi Vasil'eva, 2001 (пойменный воΔоем реки БурΛа, Панкрушихинский район АΛтайского края)
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets
<div> <div> <div> <div> <p>This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis.</p> <h3>Datasets</h3> <p>We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations:</p> <ol> <li> <p><strong>Cell Line (CL)</strong>:</p> <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> <p><strong>Human Dendritic Cells (HDC)</strong>:</p> <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> <p><strong>Human Pancreas (HP)</strong>:</p> <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> <p><strong>Mouse Brain (MB)</strong>:</p> <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> <p><strong>Mouse Cell Atlas (MCA)</strong>:</p> <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> <p><strong>Mouse Hematopoietic Stem and Progenitor Cells (MHSPC)</strong>:</p> <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> <p><strong>Mouse Retina (MR)</strong>:</p> <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> <p><strong>PBMC (human Peripheral Blood Mononuclear Cell)</strong>:</p> <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> <p><strong>Usage Notes</strong>: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file.</p> <p><strong>Keywords</strong>: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics</p> <p><strong>Contact</strong>: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a></p> <p><strong>License</strong>: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Fig. 1 in Vasil'Eva, 2001 A As New Specie In The Fish Fauna Of Altai Krai (West Siberia, The Russian Federation)
Fig. 1. Misgurnus nikolskyi Vasil'eva, 2001 (the floodplain water body of the the Burla River, Pankrushikhinsky district of Altai Krai)
Dataset: Capitol Federal Financial, Inc. (CFFN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Kentucky First Federal Bancorp (KFFB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Home Federal Bancorp, Inc. of Louisiana (HFBL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Рис. 1. Карта-схема заказника «УΑыΛь» с указанием станций отбора проб зообентоса (с сайта ФГБУ «ЗаповеΑное Приамурье». URL: http://www.zapovedamur.ru/zakaznik_udyl) Fig. 1. Map-scheme of the Udyl Nature Reserve with indication of zoobenthos sampling stations (from website of the Federal State Budgetary Institution "Zapovednoe Priamurye. Available at: http://www.zapovedamur.ru/zakaznik_udyl) in Quantitative zoobenthos characteristics of the Udyl Lake basin (Udyl Nature Reserve, Khabarovsky Region)
Рис. 1. Карта-схема заказника «УΑыΛь» с указанием станций отбора проб зообентоса (с сайта ФГБУ «ЗаповеΑное Приамурье». URL: http://www.zapovedamur.ru/zakaznik_udyl) Fig. 1. Map-scheme of the Udyl Nature Reserve with indication of zoobenthos sampling stations (from website of the Federal State Budgetary Institution "Zapovednoe Priamurye. Available at: http://www.zapovedamur.ru/zakaznik_udyl)
Australian Federal Legislation - Principal acts in force
<p>This dataset is a corpus with 884 documents in legal Australian English. These documents are part of the Australian Federal Legislation principal acts in force as of 29 December 2018.</p> <p>The documents are offered in two forms: (1) as pure txt documents (2) as RDF turlte documents following the ELI schema (European Legislation Identifier ontology).</p>
Fig. 3. 2 in Spores of Paenibacillus larvae, Ascosphaera apis, Nosema ceranae and Nosema apis in bee products supervised by the Brazilian Federal Inspection Service
Fig. 3. 2% agarose gel stained with SYBR Safe of the multiplex PCR products from royal jelly samples (1–10) obtained from markets of the state of São Paulo, Brazil. M, ® molecular marker 100 pb (Invitrogen); C+, positive control for N. ceranae (218 pb), N. apis (321 pb), A. apis (485 pb) and P. larvae (700 pb); C−, negative control.
Fig. 4. 2 in Spores of Paenibacillus larvae, Ascosphaera apis, Nosema ceranae and Nosema apis in bee products supervised by the Brazilian Federal Inspection Service
Fig. 4. 2% agarose gel stained with SYBR Safe of the multiplex PCR products from honey samples (1–17) obtained from markets of the state of São Paulo, Brazil. M, ® molecular marker 100 pb (Invitrogen); C+, positive control for N. ceranae (218 pb), N. apis (321 pb), A. apis (485 pb) and P. larvae (700 pb); C−, negative control.
Fig. 5. 2 in Spores of Paenibacillus larvae, Ascosphaera apis, Nosema ceranae and Nosema apis in bee products supervised by the Brazilian Federal Inspection Service
Fig. 5. 2% agarose gel stained with SYBR Safe of the multiplex PCR products from pollen samples (1–10) obtained from markets of the state of São Paulo, Brazil. M, ® molecular marker 100 pb (Invitrogen); C+, positive control for N. ceranae (218 pb), N. apis (321 pb), A. apis (485 pb) and P. larvae (700 pb); C−, negative control.
Figures 1−4 in New species of Aethalopteryx Schoorl, 1990 (Lepidoptera, Cossidae, Zeuzerinae) from Federal Democratic Republic of Ethiopia
Figures 1−4. Adult specimens, male genitalia and habitat of Aethalopteryx strohlei Yakovlev sp. nov.: 1. Male, holotype; 2. Female, paratype; 3. Male genitalia (slide MSW 2015/41 Coss) 4. Habitat (photo by M. Ströhle).
Figure 15 in Wittoecia - a new genus of Zeuzerinae (Lepidoptera, Cossidae) from the Federal Democratic Republic of Ethiopia
Figure 15. Habitat of Wittoecia brehmi (Yakovlev & Witt, 2016) comb. n. (photo by Harald Sulak).
ScienceDex guides
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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.