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102 results for “agency”
WSC 2007 - 2012 Yahara Watershed surface water quality policies and practices created and implemented by public agencies
This dataset was created June 2012 - August 2013 to contribute to research under the Water Sustainability and Climate project. Interventions collected are those land-based policies and practices written and implemented by public agencies. Policies were implemented in Wisconsin's Yahara Watershed the period 2007-2012. They aim to improve surface water quality through nutrient (phosphorus and nitrogen) and sediment reduction. Interventions included in the mapping must have spatially-explicit, publicly available data through personal communication or website.
2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.
Lake water quality is known to be affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology conceptual framework using a random forest algorithm on large, national-scale, spatially explicit dataset, the United States Environmental Protection Agency 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity).
Gaviota Fire Perimeter (Santa Barbara County, CA), June 9, 2004 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Gaviota Fire burned from 2004-06-05 to 2004-06-12, 15 miles west of Santa Barbara, Santa Barbara County. Approximately 7440 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2004-06-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Tea Fire Perimeter (Santa Barbara County, CA), November 15, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Tea Fire burned from 2008-11-13 to 2008-11-17, Montecito, Cold Springs Creek and Hot Springs Road, Santa Barbara County. Approximately 1940 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-11-15, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Jesusita Fire Perimeter (Santa Barbara County, CA), May 10, 2009 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Jesusita Fire burned from 2008-05-05 to 2008-05-18, Northwest of Mission Canyon and Santa Barbara City, Santa Barbara County. Approximately 8733 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-05-10, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Lake mask and distance to land dataset of 2024 lakes for the European Space Agency Climate Change Initiative Lakes v2
<p>This dataset contains the distance to land and the lake identifiers as a global netcdf file for all the water pixels at 1km (1/120 deg) lat/lon resolution of 2024 lakes distributed globally. It contains also the list of lakes as a csv file with information such as the lake center as defined in [1], and the coordinate of a box to easily locate the like in the global netcd file. The mask excludes islands on lakes and it has been derived from the GloboLakes high resolution limnology dataset [2]. The dateset have been further harmonized with the lake maximum extent lake polygons by PML [3]. The lake list with the plot of the mask and the polygons is available as a html file accessible also from the lake website at the University of Reading: http://www.laketemp.net/home_CCI/LMPolygons.php</p> <p>This dataset accompanies the <strong>ESA CCI Lakes v2 dataset</strong> [4].</p> <p> </p> <p>[1] Carrea, L.; Embury, O.; Merchant, C.J. (2015): High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates - Geoscience Data Journal, 2 (2). pp. 83-97. ISSN 2049-6060 doi: https://doi.org/10.1002/gdj3.32</p> <p>[2] Carrea, L.; Embury, O.; Merchant, C.J. (2015): GloboLakes: high-resolution global limnology dataset v1. Centre for Environmental Data Analysis. doi:10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb. <a href="http://dx.doi.org/10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb">http://dx.doi.org/10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb</a></p> <p>[3] Simis, S.; Mata, A.; Selmes, N.; Carrea, L. (2021) Lake polygons dataset accompanying Calimnos v1.4.0 and ESA CCI Lakes Climate Research Data Package v2.0. zenodo https://doi.org/10.5281/zenodo.4899250</p> <p>[4] Carrea, L.; Crétaux, J.-F.; Liu, X.; Wu, Y.; Bergé-Nguyen, M.; Calmettes, B.; Duguay, C.; Jiang, D.; Merchant, C.J.; Mueller, D.; Selmes, N.; Simis, S.; Spyrakos, E.; Stelzer, K.; Warren, M.; Yesou, H.; Zhang, D. (2022): ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.1. NERC EDS Centre for Environmental Data Analysis <a href="https://catalogue.ceda.ac.uk/uuid/03c935c6890c4b2ebf4aae4d84cd9472">https://catalogue.ceda.ac.uk/uuid/03c935c6890c4b2ebf4aae4d84cd9472</a></p>
German ZIP codes, Kreisschlüssel (Administration Unit), Kreis, Inhabitant per ZIP, City Names, responsible Arbeitsagentur (Social Agency
<p>This dataset from 2019 contains all German ZIP codes, city names associated with it, Kreisschlüssel (Administration Unit ID) Kreis, (Administration Unit), Bundesland (State), Inhabitants, responsible Arbeitsagentur (Social Agency). Note that especially the PLZ ZIP Codes and the responsible Arbeitsagentur change from time to time due to administrative reasons.</p>
Child mortality dataset (from the UN Inter-agency Group for Child Mortality Estimation database). June 2019
<p>This dataset compromises all country data included in the UN Inter-agency Group for Child Mortality Estimation (IGME) database (<a href="https://childmortality.org/data">https://childmortality.org/data</a>, downloaded June 2019).</p> <p>It includes:</p> <p><strong>Reference area: </strong>name of the country</p> <p><strong>Indicator:</strong> child mortality indicator (neonatal mortality, infant mortality, under-5 mortality and mortality rate age 5 to 14)</p> <p><strong>Sex: </strong>sex of the child (male, female and total)</p> <p><strong>Series name:</strong> name of survey/census/VR [note: UN IGME estimates, i.e. not source data, are identified as "UN IGME estimate" in this field]</p> <p><strong>Series year: </strong>year of survey/census/VR series</p> <p><strong>Observation value: </strong>value of indicator from survey/census/VR</p> <p><strong>Observation status:</strong> indicates whether the data point is included or excluded for estimation [status of "normal" indicates UN IGME estimate, i.e. not source data]</p> <p><strong>Series Category:</strong> category of survey/census/VR, and can be:</p> <ul> <li>DHS [Demographic and Health Survey]</li> <li>MIS [Malaria Indicator Survey]</li> <li>AIS [AIDS Indicator Survey]</li> <li>Interim DHS</li> <li>Special DHS</li> <li>NDHS [National DHS]</li> <li>WFS [World Fertility Survey]</li> <li>MICS [Multiple Indicator Cluster Survey]</li> <li>NMICS [National MICS]</li> <li>RHS [Reproductive Health Survey]</li> <li>PAP [Pan Arab Project for Child or Pan Arab Project for Family Health or Gulf Famly Health Survey]</li> <li>LSMS [Living Standard Measurement Survey]</li> <li>Panel [Dual record, multiround/follow-up survey and longitudinal/panel survey]</li> <li>Census</li> <li>VR [Vital Registration]</li> <li>SVR [Sample Vital Registration]</li> <li>Others [e.g. Life Tables]</li> </ul> <p><strong>Series type: </strong>the type of calculation method used to derive the indicator value (direct, indirect, household deaths, life table and vital records)</p> <p><strong>Standard error: </strong>sampling standard error of the observation value</p> <p><strong>Series method: </strong>data collection method, and can be:</p> <ul> <li>Survey/census with Full Birth Histories</li> <li>Survey/census with Summary Birth Histories</li> <li>Survey/census with Household death</li> <li>Vital Registration</li> <li>Other</li> </ul> <p><strong>Lower and upper bound:</strong> the lower and upper bounds of 90% uncertainty interval of UN IGME estimates (for estimates only, i.e., not source data).</p> <p>The dataset is used in the following paper:</p> <p><em>Ezbakhe, F. and Pérez-Foguet, A. (2019) Levels and trends in child mortality: a compositional approach. Demographic Research (Under Review)</em></p>
Attribution of intentional agency towards robots reduces one's own sense of agency.
<p>### Attribution of intentional agency towards robots reduces one’s own sense of agency ###</p> <p>The data presented here are reported in Ciardo, Beyer, De Tommaso & Wykowska (accepted). Attribution of intentional agency towards robots reduces one’s own sense of agency. Cognition.</p> <p>Please refer to that paper for context and method. </p> <p>Files descriptions:<br> Raw Data.csv: Raw data of the three experiments. Please read the .txt file for variables definition.<br> Exp3_Data_Goodspeed.csv: Goodspeed questionnaire data of Experimet 3.<br> Listof Variables:..txt file with definition of variables and labels.</p>
Content Analysis of Canada's Science Based Departments and Agencies Open Science Action Plans
<p>Dataset and codebook for a content analysis of Science-based departments and agencies open science action plans in response to the Government of Canada's Roadmap for Open Science. </p>
Gap Fire Perimeter (Santa Barbara County, CA), July 9, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Gap Fire burned from 2008-07-01 to 2008-07-28, Lizard's Mouth area of Los Padres National Forest, Santa Barbara County. Approximately 9544 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-07-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
ERT data collected at the Corona volcano (Lanzarote, Canary Islands) during the European Space Agency (ESA) testing campaign PANGAEA-X 2017
<p>This dataset contains the ERT (Electrical Resistivity Tomography) data collected between 22 and 23 November 2017 at the Corona volcano (Lanzarote, Canary Islands, Fig. 1) for the detection of lava tubes and the stratigraphic investigation of planetary volcanic analogues. This geophysical survey was carried out within the European Space Agency (ESA) testing campaign PANGAEA-X 2017 (Bessone et al., 2018), aimed at integrating astronaut training-data collection, documentation, analogue field geology procedures with remote sensing and in situ geophysical methods. </p> <p>Two ERT profiles were acquired in NE-SW and NNE-SSW orientations (Fig. 1). These were located roughly orthogonal to the Corona lava tube system and as far as possible on top of the main lava tube axes. The longer profile, profile D, is 470 m in length and was obtained using 48 electrodes spaced 10 m apart. The profile orientation is from SW to NE (electrode 1 to 48). The profile was acquired to detect lava tubes in test site D (sub-area south) where the exact location of a lava tube was known thanks to a LiDAR TLS (Terrestrial Laser Scan) subsurface survey (Santagata et al., 2018). A shorter profile, profile E, is 235 m long and was obtained using 48 electrodes 5 m apart. The profile orientation is from SSW to NNE (electrode 1 to 48). This profile was acquired in test site E (sub-area north) to provide a more detailed investigation of the potential existence of inaccessible sections of the tube whose location could be indicated by the evidence of closely-spaced aligned collapse structures.</p> <p>Each profile was collected using measure sequences compounded by 276 Wenner-Schlumberger array quadrupoles which ensure high vertical resolution and signal amplitude and 328 dipole-dipole array quadrupoles which provide enhanced lateral resolution. A fully automatic multi-electrode resistivity meter SYSCAL Jr Switch-48 by IRIS Instruments (400 V max output voltage, 1200 mA max output current, 100 W max output power, <a href="http://www.iris-instruments.com/syscal-juniorsw.html">http://www.iris-instruments.com/syscal-juniorsw.html</a>), was used for data collection.</p> <p>At most of the measurement points, it was necessary to drill the basalt using a hand drilling machine in order to place the tips of the electrodes into the ground at a depth of approximately 40 cm. The electrodes also needed to kept moist to reduce contact resistance between the electrode and the ground. A large amount of water (up to 2 liters per point) was needed for profile D, situated in an area above the lava tubes with very porous dry soil cover.</p> <p>The dataset is presented as a spreadsheet format which has the "space" as separator and the ".txt" extension. The structure of such a file is the following one:</p> <p>#, El array, Spa1/4, Rho, Dev, M, Sp, Vp, In, Time, Spa5/12, M1/20</p> <p>- #: Data point number</p> <p>- El array: Electrode array</p> <p>- Spa. 1/4: four spacing parameters (corresponding to the electrode array – in m)</p> <p>- Rho: resistivity value (in Ohm.m)</p> <p>- Dev: standard deviation (quality factor, in %)</p> <p>- M: global chargeability value (induced polarization parameter (in mV/V – "=0" if only-resistivity data))</p> <p>- Sp: spontaneous polarization (measured just before the injection, in mV)</p> <p>- Vp: measured primary voltage (in mV)</p> <p>- In: injected current intensity (in mA)</p> <p>- Time: injection time (pulse duration, in s)</p> <p>- Spa. 5/8: other spacing parameters (in m)</p> <p>- Spa. 9/12: electrode elevation (in m)</p> <p>- M1/M20: partial chargeability values (induced polarization window (in mV/V – "=0" if only-resistivity data))</p> <p> </p> <p>Acknowledgements</p> <p>The authors are grateful to ESA and all PANGAEA-X 2017 staff, particularly Loredana Bessone, Matthias Maurer, Herve Stevenin and Igor Drozdovskiy for their participation in data collection during some of the experiments and to the MilesBeyond Team, particularly Francesco Maria Sauro for his logistical support. Regional and local remote sensing data were obtained by the Spanish Instituto Geográfico Nacional (https://www.ign.es) and Gobierno de Canarias (https://www.grafcan.es, <a href="https://opendata.sitcan.es/">https://opendata.sitcan.es</a>).</p> <p> </p> <p>References</p> <p>Bessone, L., et al., 2018, Testing technologies and operational concepts for field geology exploration of the Moon and beyond: the ESA PANGAEA-X campaign, Geophysical Research Abstract, #EGU2018-4013.</p> <p>Santagata, T., Sauro, F., Massironi, M., Pozzobon, R., Del Vecchio, U., Lazzaroni, M., Damiano, N., Tonello, M., Tomasi, I., Martínez-Frìas, J. and Mateo Medero, E., 2018. Subsurface laser scanning and photogrammetry in the Corona Lava Tube System, Lanzarote, Spain, EGU General Assembly 2018, pp. EGU2018-5290.</p>
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Datasets and Supporting Materials for the MALIN-ANR 2019 Competition (French national research agency)
<p>This "ZENODO deposit" provides a multiple sensor dataset collected by the CyborgLOC team during the intermediate competition of the Challenge MALIN (<em>MA</em><em>îtrise</em><em> de la </em><em>L</em><em>ocalisation </em><em>IN</em><em>door</em>), which is a competition for indoor/outdoor real-time positioning. The sensors, including a GNSS receiver Ublox NEO-M8N, a Realsense D435i stereo camera, three Xsens MTi-300 and one PERSY (<strong>PE</strong>destrian <strong>R</strong>eference <strong>SY</strong>stem), are mounted on different parts of the subject’s body. The PERSY is a foot-mounted positioning device with a tri-axial accelerometer, a tri-axial gyroscope, a tri-axial magnetometer as well as a GNSS receiver Ublox M8T. The two scenarios are designed in a training center of firefighters CFIS (Fire and Rescue Training Center) in Blois, France to simulate the situation of firefighters during interventions. With total distances around 2 km for each scenario, the travelled trajectories passed through challenging environments including indoor, outdoor, urban canyon. The indoor part contains different stair levels, from the underground up to the 6th floor. The travel modes are vehicles and pedestrians. Several classical activities of firefighters are realized such as walking, running, stair-climbing, side-walking, crawling, passing above/below obstacles, carrying a stretcher, ladder climbing, etc. High accurate ground truth of stationary points and enclosing volumes are provided by the organizers of the competition, i.e., the French Ministry of Defense (DGA: Direction Générale de l’Armement). Provided with raw data, they allow the evaluation of the positioning performances.</p> <p>To facilitate the use of our dataset under Rosbag format, a toolkit of python scripts named <em>MALIN Data Processing Tools</em> is provided on GitHub (<a href="https://github.com/4g-group/malin_data_processing_tools">https://github.com/4g-group/malin_data_processing_tools</a>). It allows merging Rosbags, converting Rosbag files to CSV files as well as republishing camera’s topics as decompressed data. Details about these processing tools could be found in the Readme file on the Github page. </p>
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NOAA NCCOS Assessment: Agency priorities for mapping coral reef ecosystems in American Samoa, 2023-06-06 to 2023-08-07
<p>Description:</p><p>NOAA's Coral Reef Conservation Program (CRCP) has identified a need for priority locations based on emerging management requirements in shallow coral reef areas (up to 40 meters depth) surrounding American Samoa. The priorities provided by participating agencies will inform research and monitoring activities, address current and future management needs, and maximize opportunities to leverage and complement existing regional efforts.</p><p>To meet this need, NOAA's National Centers for Coastal Ocean Science (NCCOS) developed a systematic, quantitative approach and online GIS application to gather seafloor mapping priorities from researchers and coral reef managers. Participants placed virtual coins into a grid overlaid on the project area to express the location of their mapping priorities. They also used pull-down menus to indicate specific mapping data needs and the rationale for their selections. Participants' inputs were compiled and analyzed to identify high priority areas along with their justifications and requirements. A total of nine participant groups entered their mapping priorities into the online tool. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in American Samoa.</p><p>Purpose:</p><p>The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coast of the American Samoa. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection.</p><p>Methods:</p><p>An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the prioritization process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online prioritization tool the study area was divided into 160 hexagonal grid cells 2.6 km2 in size. Existing relevant spatial datasets (e.g., bathymetry layers, Sanctuary Protection Areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. Each participant was provided with 50 virtual coins to place into grid cells that they wished to prioritize. They were instructed to place more coins in grid cells that were higher priorities. A maximum of 5 coins could be placed into an individual grid cell. Respondents also reported why these locations were important by selecting a minimum of one, and a maximum of two, management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also reported requirements of data were needed in priority cells. A minimum of one, to a maximum of two choices were selected from the following list: delineations of large topographic features, delineations of hard vs. soft bottom, models of habitat suitability for key taxa or communities, delineations of substrate type (e.g. sand, mud, coral, rock), models of presence/absence or density of corals, identification of coral species and their local environments, documentation of individual specimen condition. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefile contains the 2.6 km2 grid cells used in this prioritization and their associated coin values overall, as well as by management use, data product, and mapping methodology. Other summary values include the number of participants, number of participating groups, number of management uses, and number of data requirements. Additionally, coins for microscale (identification of coral species and their local environments and documentation of individual specimen condition), mesoscale (delineations of substrate type, models of presence/absence/density of corals), and regional (delineations of topographic features, delineations of hard vs. soft bottom, models of habitat suitability) requirements were summarized. Also included is a ranking of each grid cell based on the total number of coins, management uses, and participating groups allocating coins in the respective cell. For a complete description of the process and analysis see: Hile et al. 2023, in prep.</p>
Public Attitudes Towards the European Space Agency
<p>The data inherent in this dataset were collected between 11 February 2020 to 1 March 2020 as part of a public survey of German residents. The survey looked into attitudes and (hypothetical) behaviours related to the European Space Agency and European space activities. Convenience sampling and snowball sampling were employed over email, messengers and social media.</p> <p>This is a multiple imputation dataset, including an imputation variable, a weighting variable, as well as composite variables where applicable.</p> <p>Additional files include a generic codebook for the dataset and a print version of the online survey for a rough overview of the survey design. The survey content in the latter is in German.</p>
NOAA NCCOS Assessment: Agency priorities for mapping coral reef ecosystems in Puerto Rico and the U.S. Virgin Islands, 2021-11-03 to 2022-01-14
<p>Description:</p> <p>The National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process, and online application (Buja and Christensen 2019) to identify mapping needs along the Puerto Rico and U.S. Virgin Island (USVI) coasts to support shallow coral reef management by NOAA’s Coral Reef Conservation Program (CRCP). Participants from local, federal, academic, and other institutions (sixteen in Puerto Rico, eighteen in USVI), entered their priorities in an online participatory Geographic Information System (pGIS). Participants used virtual coins to denote their priorities in 2.6 km<sup>2</sup> hexagonal grid cells overlaid on the study area, individually for Puerto Rico and USVI. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important, what data types were needed, and data collection methodologies using a pre-set list of options. Results were compiled, summarized, and mapped to identify high priority areas, reasons for those priorities, and information needs. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in Puerto Rico and USVI.</p> <p> </p> <p>Purpose:</p> <p>The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coasts of Puerto Rico and USVI. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection.</p> <p> </p> <p>Methods:</p> <p>An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the pGIS process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online pGIS, the Puerto Rico study area was divided into 2007 hexagonal grid cells 2.6 km2 in size. The USVI study area was divided into 644 hexagonal grid cells 2.6 km2 in size. Existing relevant spatial datasets (e.g., bathymetry, Sanctuary Protection Areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. The pGIS was used by 16 participants in Puerto Rico and 18 participants in USVI to convey their recommendations. Each Puerto Rico participant was provided with 600 virtual coins to place into grid cells that they wished to prioritize. Each USVI participant was provided with 200 coins. They were instructed to place more coins in grid cells that were higher priorities. A maximum of 60 coins could be placed into an individual grid cell in Puerto Rico by each respondent, and a maximum of 20 coins could be place into an individual grid cell in USVI. Respondents also reported why these locations were important by selecting a minimum of one, and a maximum of two, management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also reported requirements of data were needed in priority cells. A minimum of one, to a maximum of two choices were selected from the following list: delineations of large topographic features, delineations of hard vs. soft bottom, models of habitat suitability for key taxa or communities, delineations of substrate type (e.g. sand, mud, coral, rock), models of presence/absence or density of corals, identification of coral species and their local environments, documentation of individual specimen condition. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefile contains the 2.6 km2 grid cells used in this prioritization and their associated coin values overall, as well as by management use, data product, and mapping methodology. Other summary values include the number of participants, number of participating groups, number of management uses, and number of data requirements. Additionally, coins for microscale (identification of coral species and their local environments and documentation of individual specimen condition), mesoscale (delineations of substrate type, models of presence/absence/density of corals), and regional (delineations of topographic features, delineations of hard vs. soft bottom, models of habitat suitability) requirements were summarized. Also included is a ranking of each grid cell based on the total number of coins, management uses, and participating groups allocating coins in the respective cell. For a complete description of the process and analysis see: Kraus et al. 2022, in prep.</p> <p> </p>
NOAA NCCOS Assessment: Agency priorities for mapping coral reef ecosystems in Hawaiʻi, 2022-07-08 to 2022-08-01
<p>Description</p> <p>NOAA's Coral Reef Conservation Program (CRCP) has identified a need for priority locations based on emerging management requirements in shallow coral reef areas (up to 40 meters) surrounding the main Hawaiian Islands. The priorities provided by participating agencies will inform research and monitoring activities, address current and future management needs, and maximize opportunities to leverage and complement existing regional efforts.</p> <p>To meet this need, NOAA’s National Centers for Coastal Ocean Science (NCCOS) developed a systematic, quantitative approach and online GIS application to gather seafloor mapping priorities from researchers and coral reef managers. Participants placed virtual coins into a grid overlaid on the project area to express the location of their mapping priorities. They also used pull-down menus to indicate specific mapping data needs and the rationale for their selections. Participants’ inputs were compiled and analyzed to identify high priority areas along with their justifications and requirements. A total of 17 participant groups entered their mapping priorities into the online tool. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in Hawaiʻi.</p> <p>Purpose:</p> <p>The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coasts of the main Hawaiian Islands. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection.</p> <p>Methods:</p> <p>An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the prioritization process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online prioritization tool, the study area was divided into 1786 hexagonal grid cells 2.6 km<sup>2</sup> in size. Existing relevant spatial datasets (<em>e.g.</em>, bathymetry, protected areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. Each participant was provided with 540 virtual coins to place into grid cells to denote their mapping needs. They were instructed to place more coins in grid cells that were higher priority. A maximum of 54 coins could be placed into an individual grid cell by each respondent. Participants also selected from a drop-down list of predefined management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also selected what map product requirements were needed in priority cells by selecting a minimum of one, to a maximum of two choices from the following list: delineations of large topographic features, delineations of hard vs. soft bottom, models of habitat suitability for key taxa or communities, delineations of substrate type (e.g. sand, mud, coral, rock), models of presence/absence or density of corals, identification of coral species and their local environments, documentation of individual specimen condition. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefile contains the 2.6 km<sup>2</sup> grid cells used in this prioritization and their associated coin values overall, as well as by management use and map product requirement. Other summary values include the number of participants, number of participating groups, number of management uses, and number of map product requirements. Additionally, coins for microscale (identification of coral species and their local environments and documentation of individual specimen condition), mesoscale (delineations of substrate type, models of presence/absence/density of corals), and regional (delineations of topographic features, delineations of hard vs. soft bottom, models of habitat suitability) requirements were summarized. Also included is a ranking of each grid cell based on the total number of coins, management uses, and participating groups allocating coins in the respective cell. For a complete description of the process and analysis see: Kraus et al. 2023, in prep.</p> <p> </p>
A Dataset of UN Agencies' Public Communication about Climate Change on Twitter
<p>The present dataset contains the Twitter communication of eight international organizations (IOs) in different policy areas that are known to be central in communicating about climate change. The IOs are comparable in their communication, all being parts of the United Nations (UN). The IOs under consideration are:</p> <ul> <li>Food and Agriculture Organization (FAO),</li> <li>Office for the Coordination of Humanitarian Affairs (UNOCHA),</li> <li>UN Development Programme (UNDP),</li> <li>UN Office for Disaster Risk Reduction (UNDRR),</li> <li>UN Environmental Program (UNEP),</li> <li>UN International Children’s Emergency Fund (UNICEF),</li> <li>UN High Commissioner for Refugees (UNHCR),</li> <li>World Health Organization (WHO).</li> </ul> <p>The tweets were downloaded and parsed via the Twitter Academic Research API (<a href="https://developer.twitter.com/en/products/twitter-api/academic-research">link</a>). In total, the dataset contains 222,191 tweet IDs of the tweets posted by the above 8 UN organizations from their official accounts. This number represents the total number of tweets posted by these selected UN organizations since the beginning of their tweeting history until the end of 2019. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p>The dataset consists of two parts:</p> <ul> <li>Unlabeled tweet IDs of the considered IOs (8 txt-files),</li> <li>Labeled dataset of tweet IDs with labels indicating whether tweets are about climate change or not (1 csv-file).</li> </ul> <p><strong>Unlabeled tweet IDs</strong></p> <p>The corresponding 8 txt-files contain tweet IDs of the corresponding tweets posted by the UN organizations. The files are summarised in Table 1 below. </p> <p> </p> <table align="center"> <caption><strong>Table 1</strong>. Summary of the collected dataset files.</caption> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Organization</strong></td> <td><strong>Account</strong></td> <td><strong>Start date</strong></td> <td><strong>End date</strong></td> <td><strong>Tweet IDs</strong></td> </tr> <tr> <td><strong>tweet_ids_FAO_2009_2019.txt</strong></td> <td>FAO</td> <td>@FAO</td> <td>Jan. 2009</td> <td>Dec. 2019</td> <td>28,630</td> </tr> <tr> <td> <p><strong>tweet_ids_UNDP_2009_2019.txt</strong></p> </td> <td>UNDP</td> <td>@UNDP</td> <td>Jul. 2009</td> <td>Dec. 2019</td> <td>47,960</td> </tr> <tr> <td> <p><strong>tweet_ids_UNDRR_2009_2019.txt</strong></p> </td> <td>UNDRR</td> <td>@UNDRR</td> <td>Oct. 2010</td> <td>Dec. 2019</td> <td>9,735</td> </tr> <tr> <td> <p><strong>tweet_ids_UNEP_2009_2019.txt</strong></p> </td> <td>UNEP</td> <td>@UNEP</td> <td>May 2009</td> <td>Dec. 2019</td> <td>21,615</td> </tr> <tr> <td> <p><strong>tweet_ids_Refugees_2008_2019.txt</strong></p> </td> <td>UNHCR</td> <td>@Refugees</td> <td>Jun. 2008</td> <td>Dec. 2019</td> <td>42,882</td> </tr> <tr> <td> <p><strong>ttweet_ids_UNICEF_2009_2019.txt</strong></p> </td> <td>UNICEF</td> <td>@UNICEF</td> <td>Jul. 2009</td> <td>Nov. 2019</td> <td>34,288</td> </tr> <tr> <td> <p><strong>tweet_ids_UNOCHA_2011_2019.txt</strong></p> </td> <td>UNOCHA</td> <td>@UNOCHA</td> <td>Jul. 2011</td> <td>Jul. 2019</td> <td>12,521</td> </tr> <tr> <td> <p><strong>tweet_ids_WHO_2008_2019.txt</strong></p> </td> <td>WHO</td> <td>@WHO</td> <td>May 2008</td> <td>Dec. 2019</td> <td>24,560</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td><strong>Total</strong></td> <td>222,191</td> </tr> </tbody> </table> <p>The dataset contains only tweet IDs to ensure compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The tweet IDs need to be hydrated to be used. For hydrating the present dataset, the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link</a>) may be used; see a step-by-step tutorial on how to use Hydrator (<a href="http://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">link</a>).</p> <p><strong>Labeled dataset related to climate change</strong></p> <p>This is a subset of the entire dataset described above. Namely, 5,750 tweets are randomly selected from the entire dataset and labeled manually as either "climate change-related" or "not climate change-related". The dataset is available in the file <strong>dataset_UN_climate_change_labeled.csv</strong> and is summarised in Table 2 below. </p> <table align="center"> <caption><strong>Table 2</strong>. Summary of the labeled dataset.</caption> <tbody> <tr> <td><strong>Organization</strong></td> <td><strong>Tweets</strong></td> </tr> <tr> <td>FAO</td> <td>753</td> </tr> <tr> <td>UNDP</td> <td>1,199</td> </tr> <tr> <td>UNDRR</td> <td>256</td> </tr> <tr> <td>UNEP</td> <td>540</td> </tr> <tr> <td>UNHCR</td> <td>1,114</td> </tr> <tr> <td>UNICEF</td> <td>910</td> </tr> <tr> <td>UNOCHA</td> <td>366</td> </tr> <tr> <td>WHO</td> <td>612</td> </tr> <tr> <td><strong>Total</strong></td> <td>5,750</td> </tr> </tbody> </table> <p> </p> <p> </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.