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536 results for “Office”
Historical Animal Observation Records by Bavarian Forestry Offices (1845)
<p>In 1845, under the scientific direction of Andreas Wagner, the Bavarian government recorded the occurrence of 44 selected vertebrate species across the entire country. To this end, Wagner had a survey questionnaire sent to all 119 forestry offices in the state. The foresters' responses were now systematically recorded and analyzed for the first time. This data set represents the result of this survey. Among other things, it contains 5,467 geo-coded animal observation data.</p> <p>The data is the result of an interdisciplinary collaboration between scientists from the Chair of Computational Humanities at the University of Passau, the Directorate General of the Bavarian State Archives Munich, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, the Center for Biodiversity Informatics and Collection Data Integration at the Botanical Garden Berlin, and the NFDI4Biodiversity consortium.</p>
UMA-Offices
<p>The UMA-Offices dataset was collected in our facilities at the University of Málaga. It consist of 25 scenarios captured through an RGB-D sensor mounted on a robot. More info here: <a href="https://mapir.isa.uma.es/mapirwebsite/?p=2407" target="_blank" rel="noopener">https://mapir.isa.uma.es/mapirwebsite/?p=2407</a></p> <p>If you use the dataset, please cite it as:</p> <blockquote> <pre>@INPROCEEDINGS{Ruiz-Sarmiento-KBS-2015, author = {Ruiz-Sarmiento, J. R. and Galindo, Cipriano and Gonz{\'{a}}lez-Jim{\'{e}}nez, Javier}, title = {Exploiting Semantic Knowledge for Robot Object Recognition}, journal = {Knowledge-Based Systems}, volume = {86}, year = {2015}, doi = {doi:10.1016/j.knosys.2015.05.032}, pages = {131--142}, } @inproceedings{fernandez2013fast, title = {Fast place recognition with plane-based maps}, author = {Fern{\'a}ndez-Moral, Eduardo and Mayol-Cuevas, W and Ar{\'e}valo, Vicente and Gonz{\'a}lez-Jim{\'e}nez, J}, booktitle = {Robotics and Automation (ICRA), 2013 IEEE International Conference on}, pages = {2719--2724}, year = {2013}, organization = {IEEE}</pre> </blockquote>
Sacramento-San Joaquin Bay-Delta Continuous (15 minute) Water Quality Monitoring: South Delta Region, collected by the North Central Region Office, DWR, 1999 – ongoing
The Department of Water Resources (DWR) Water Quality Evaluation Section (WQES) provides technical expertise and program support for regulatory compliance, water operations, emergency response, and environmental restoration. Wireless telemetry is used to transmit real-time provisional data to the California Data Exchange Center (CDEC), making the data publicly available. The published dataset is quality controlled and quality assured providing detailed information at 15-minute intervals from 18 monitoring stations, using Xylem’s YSI EXO2 and YSI 6600 multiparameter sondes to document individual water quality measurements of multiple water quality parameters. The dataset informs operations for the California State Water Project and supports water quality monitoring required by Water Right Decision D-1641, the Delta Compliance Program, the South Delta Temporary Barriers and the South Delta Improvement Program. It is important to note that the start dates and subsequent equipment upgrades vary between stations and equipment leading to discrepancies in the dataset’s date ranges.
A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring
<p>A dataset containing measurement data for six office rooms in Aalborg Denmark.<br>All the measurements have been resampled to 5 minute resolution<br>The measurements consists of:</p> <ul> <li>BMS data for the rooms</li> <li>Occupancy for the rooms (from cameras)</li> <li>BMS data for the AHU supplying the rooms</li> <li>BMS data for the Heating system supplying the rooms</li> </ul> <p>Changes from v2<br>It was found that the pressure difference measurements across the exhaust fan was faulty and the following variables have therefore been removed:</p> <ul> <li>Ventilation:Fan__air_flow__exhaust</li> <li>Ventilation:Fan__pressure_difference__exhaust</li> </ul> <p>More data has been added, now increasing the dataset to span the rest of 2023. To better handle the changes between standard time and daylight-saving time the column named "timestamp" has been adjusted so the datetime format now follows the ISO 8601 format YYYY-MM-DDThh:mm:ss+hhmm. the +hhmm changes between 0100 (Danish standard time) and 0200 (Danish daylight-saving time).</p> <p> </p>
Documentary sources of case studies on the issues a data protection officer faces on a daily basis
<p>The dataset contains the text of the documents that are sources of evidence used in [1] and [2] to distill our reference scenarios according to the methodology suggested by Yin in [3].</p> <p>The dataset is composed of 95 unique document texts spanning the period 2005-2022. This dataset makes available a corpus of documentary sources useful for outlining case studies related to scenarios in which the DPO finds himself operating in the performance of his daily activities.</p> <p>The language used in the corpus is mainly Italian, but some documents are in English and French. For the reader's benefit, we provide an English translation of the title of each document.</p> <p>The documentary sources are of many types (for example, court decisions, supervisory authorities' decisions, job advertisements, and newspaper articles), provided by different bodies (such as supervisor authorities, data controllers, European Union institutions, private companies, courts, public authorities, research organizations, newspapers, and public administrations), and redacted from distinct professional roles (for example, data protection officers, general managers, university rectors, collegiate bodies, judges, and journalists).</p> <p>The documentary sources were collected from 31 different bodies. Most of the documents in the corpus (a total of 83 documents) have been transformed into Rich Text Format (RTF), while the other documents (a total of 12) are in PDF format. All the documents have been manually read and verified.<br> The dataset is helpful as a starting point for a case studies analysis on the daily issues a data protection officer face. Details on the methodology can be found in the accompanying papers.</p> <p>The available files are as follows:</p> <ul> <li><strong>documents-texts.zip</strong> --> contain a directory of .rtf files (in some cases .pdf files) with the text of documents used as sources for the case studies. Each file has been renamed with its SHA1 hash so that it can be easily recognized.</li> <li><strong>documents-metadata.csv</strong> --> Contains a CSV file with the metadata for each document used as a source for the case studies.</li> </ul> <p>This dataset is the original one used in the publication [1] and the preprint containing the additional material [2].</p> <p>[1] F. Ciclosi and F. Massacci, "The Data Protection Officer: A Ubiquitous Role That No One Really Knows" in IEEE Security & Privacy, vol. 21, no. 01, pp. 66-77, 2023, doi: 10.1109/MSEC.2022.3222115, url: https://doi.ieeecomputersociety.org/10.1109/MSEC.2022.3222115.</p> <p>[2] F. Ciclosi and F. Massacci, "The Data Protection Officer, an ubiquitous role nobody really knows." arXiv preprint arXiv:2212.07712, 2022.</p> <p>[3] R. K. Yin, Case study research and applications. Sage, 2018.</p>
Outputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the outputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in the urban sensors section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p> </p>
Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of single sample data file for 1.5 m temperature as part of the Met Office contribution to the COVID 19 modelling effort.</p> <p>The full dataset was available for download from the Met Office Azure (https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/). The full dataset was available for download under the terms of non-commercial purposes.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p><strong>Note this data should be used only for non-commercial purposes.</strong></p>
The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3
<p>Supporting data for figures in GMD draft paper: The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3</p>
Office dataset - UdG
<p>This document contains sensor data collected from an office environment. The office is located in Girona, Spain, and has a surface of 72 m<sup>2 </sup>.The dataset contains 10 min sample data of energy consumption, temperature (internal and external), humidity, precipitation, wind speed, noise, and state of doors and windows</p>
Energy consumption data from an office building, waterpark and warehouse in Slovenia and energy production data from a PV Plan (900KW)
<p>The first dataset included 9-month hourly energy data from a waterpark, a warehouse and high-rise office buildings. The second dataset includes 10-year hourly energy production data from a PV plant (900KW). Both datasets refer to Ljubljana, Slovenia. </p>
Met Office Unified Model outputs for simulations of Proxima Centauri b and TRAPPIST-1e
<p>Five datasets generated by the Met Office Unified Model configured for exoplanets Proxima Centauri b and TRAPPIST-1e. </p> <p>1. Control Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere using observed planetary data (radius, stellar constant) as model parameters</p> <p>2. Warm Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>3. Control Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere using observed planetary data</p> <p>4. Warm Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>5. Dry Trap: A simulation of TRAPPIST-1e with a dry atmosphere</p> <p>The data is used in Cohen et al. (2023). ""Traveling planetary-scale waves cause cloud variability on tidally locked aquaplanets." Submitted to The Planetary Science Journal.</p> <p>Abstract:</p> <p>"Cloud cover at the planetary limb of water-rich Earth-like planets is likely to weaken chemical<br> signatures in transmission spectra, impeding attempts to characterize these atmospheres. However,<br> based on observations of Earth and solar system worlds, exoplanets with atmospheres should have both<br> short-term weather and long-term climate variability, implying that cloud cover may be less during<br> some observing periods. We identify and describe a mechanism driving periodic clear sky events at<br> the terminators in simulations of tidally locked Earth-like planets. A feedback between dayside cloud<br> radiative effects, incoming stellar radiation and heating, and the dynamical state of the atmosphere,<br> especially the zonal wavenumber-1 Rossby wave identified in past work on tidally locked planets, leads<br> to oscillations in Rossby wave phase speeds and in the position of Rossby gyres and results in advection<br> of clouds to or away from the planet’s eastern terminator. We study this oscillation in simulations of<br> Proxima Centauri b, TRAPPIST 1-e, and rapidly rotating versions of these worlds located at the inner<br> edge of their stars’ habitable zones. We simulate time series of the transit depths of the 1.4 μm water<br> feature and 2.7 μm carbon dioxide feature. The impact of atmospheric variability on the transmission<br> spectra is sensitive to the structure of the dayside cloud cover and the location of the Rossby gyres,<br> but none of our simulations have variability significant enough to be detectable with current methods."</p>
ES16 - Office - Salamanca (Spain)
<p>Data files for building: ES16 - Office - Salamanca (Spain)</p><p>Languages: Spanish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
ES15 - Office - Valladolid (Spain)
<p>Data files for building: ES15 - Office - Valladolid (Spain)</p><p>Languages: Spanish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
ES14 - Office - Zamora (Spain)
<p>Data files for building: ES14 - Office - Zamora (Spain)</p><p>Languages: Spanish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
ES04 - Office - León (Spain)
<p>Data files for building: ES04 - Office - León (Spain)</p><p>Languages: Spanish, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Santa Barbara Caltrans Office (SBCaltrans335)
Precipitation was collected by the Santa Barbara County Flood Control District at Santa Barbara Caltrans Office (SBCaltrans335) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc
SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Carpinteria US Forest Service Office (CarpinteriaUSFS383)
Precipitation was collected by the Santa Barbara County Flood Control District at Carpinteria US Forest Service Office (CarpinteriaUSFS383) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc
Patterns in bird and pollinator occupancy and richness in a mosaic of urban office parks across scales and seasons
<p>Urbanization is a leading cause of global biodiversity loss, yet cities can provide resources required by many species throughout the year. In recognition of this, cities around the world are adopting strategies to increase biodiversity. These efforts would benefit from a robust understanding of how natural and enhanced features in urbanized areas influence various taxa. We explored seasonal and spatial patterns in occupancy and taxonomic richness of birds and pollinators among office parks in Santa Clara County, California, USA, where natural features and commercial landscaping have generated variation in conditions across scales. We surveyed birds and insect pollinators, estimated multi-species occupancy and species richness, and found that spatial scale, season, and urban sensitivity were all important for understanding how communities occupied sites. Features at the landscape- and local-scale (i.e., distance to streams or baylands and tree canopy, shrub, or impervious cover, respectively) were the strongest predictors of avian occupancy in all seasons. The pollinator richness index was influenced by local tree canopy and impervious cover in spring, and distance to baylands in early and late summer. We predicted relative contributions of different spatial scales to annual bird species richness by assigning values to simulated sites representing "good" and "poor" quality, based on influential covariates returned by models. Shifting from poor to good quality conditions locally increased annual avian richness by up to 6.8 species with no predicted effect of the quality of the neighborhood. Conversely, sites of poor local- and neighborhood-scale quality in good quality landscapes were predicted to harbor 11.5 more species than sites of good local- and neighborhood-scale quality in poor quality landscapes. Finally, more urban sensitive bird species were gained at good quality sites relative to urban tolerant species, suggesting that urban natural features at the local- and landscape-scales disproportionately benefited them.</p>
GR16 - Office - Paros (Greece)
<p>Data files for building: GR16 - Office - Paros (Greece)</p><p>Languages: Greek, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
DK10 - Multi-apartment building & Office - Herning (Denmark)
<p>Data files for building: DK10 - Multi-apartment building & Office - Herning (Denmark)</p> <p>Languages: Danish, English</p> <p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p> <p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p> <p>The files are organised in the following folders (note that not all files are always provided):</p> <ol> <li>Main data and Results, with: <ol> <li>Neutral data inventory.</li> <li>Neutral results report.</li> <li>Original EPC certificate.</li> </ol> </li> <li>Energy Consumption Data, with: <ol> <li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li> </ol> </li> <li>Drawings <ol> <li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li> </ol> </li> <li>Other Data <ol> <li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li> </ol> </li> <li>Dynamic Model <ol> <li>Data to run a dynamic model of the building, if available.</li> </ol> </li> </ol> <p>The files have been redacted to exclude confidential information. </p>
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
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DANDI Archive for NWB datasets
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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.