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1,061 results for “projection data”
The Jefferson Project 2020 weather data from seven surface weather stations on Lake George, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had seven weather monitoring stations around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidiity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, and WX_Glenburnie. The stations have a sensor payload that include some combination of the following sensors: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, and N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which has undergone data correction and downsampling to an hourly frequency.
Densities and cover data for intertidal organisms from an LTREB project in the Gulf of Maine, USA, from 1996 to 2023.
Experimental clearings in macroalgal (Ascophyllum nodosum) stands were made in 1996 to determine if mussel beds and macroalgal stands on protected intertidal shores in New England represent alternative community states. Uncleared control plots and four sizes of circular clearings (1m, 2m, 4m and 8m in diameter), which mimicked ice scour events, were established in A. nodosum stands at 12 sites on Swan’s Island, Maine, USA. The purpose of these datasets is to provide access to data on densities and percentage cover in the 60 experimental plots from 1996 to 2023. Earlier versions of the data prior to 2007 can be found in Ecological Archives ( E087-047 and E089-032). The current EDI version includes corrections of errors in the versions in Ecological Archives. Data include densities of mussels (Mytilus edulis), an herbivorous limpet (Testudinalia testudinalis), herbivorous snails (Littorina littorea, Littorina obtusata), a predatory snail (Nucella lapillus), a barnacle (Semibalanus balanoides), and fucoid algae (Ascophyllum nodosum and Fucus vesiculosus), and percentage cover by mussels, barnacles, fucoids and other sessile organisms. Research was funding by NSF's LTREB program.
Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Jobos Bay NERR, April 2024 - December 2024
The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six mangrove forest locations in Jobos Bay National Estuarine Research Reserve (JBNERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Maria (September 2017) were identified and selected for hydrologic monitoring coincident with sites sampled for structural metrics in 2022. One reference site, defined as a site that was observed to be recovering following the hurricane, was selected in black mangrove forest. Two of the six sampling locations were selected to monitor effects of human encroachment on the western boundary of the reserve. These two sites were not coincident with structural sampling plots established in 2022. This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.
Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Rookery Bay NERR, April 2024 - December 2024
The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six black mangrove forest locations in Rookery Bay National Esturarine Research Reserve (NERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Irma (September 2017) were identified and selected for hydrologic monitoring. The design consists of three sites in mainland/interior black mangroves and three sites on ocean-facing islands, all of which are located on the east side of Hurricane Irma eyewall. In each group, two of the sites selected were considered sites of minimal recovery whereas one site was selected as a reference (location of recovering mangroves). This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.
2017 hydrologic, water quality, and soil quality data from The Jefferson Projects 8 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake’s food web and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had eight tributary monitoring stations around the lake collecting data on water quality, soil quality and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_ShelvingRock and TS_West. The stations have a sensor payload that may include some or all of the following sensors: EXO2 Multi-parameter sonde, CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter with five 3.0 MHz transducers, Argonaut-SL Doppler current meter, WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and down sampling to an hourly frequency.
Arctic Grayling length, weight and tag data from Arctic LTER Streams project, Toolik Filed Station Alaska, 1985 to 2018
Since 1983, the Streams Project at the Toolik Field Station has monitored physical, chemical, and biological parameters in a 5-km, fourth-order reach of the Kuparuk River near its intersection with the Dalton Highway and the Trans-Alaska Pipeline. In 1989, similar studies were begun on a 3.5-km, third-order reach of a second stream, Oksrukuyik Creek. Fish were collected on each river. Station locations, representing kilomter values certain distances from original phosphorus dripper (see method) were noted. 1985 to 2012 long-term tagging file for Arctic Grayling (Thymallus arcticus) on the Kuparuk River. All grayling adults and juveniles captured during the field season are measured, weighed, tagged and released. Grayling were tagged originally with a colored tag with a number. In 1993, researchers started pit tagging the grayling. These pit tags can be read with an antenna to track the migration of the grayling throughout the Kuparuk River system. Arctic grayling young-of-the-year (YOY) were caught multiple times during each summer and measured and weighed as well. This file combines the data from the following data sets: Dataset ID Short name 10325 1985-2012_Kuparuk_Grayling_Tags 10327 1986-2012_Kuparuk_YOY 10329 1989-2011_Oksrukuyik_Grayling_Tags 10330 1989-2012_Oksrukuyik_YOY
Cascade project at North Temperate Lakes LTER - Daily data for key variables in whole lake experiments on early warnings of critical transitions, Paul and Peter Lakes, 2008-2011
Peter Lake's food web was altered by adding largemouth bass at a slow rate while monitoring key food web constituents including littoral minnow abundance indexed as catch per trap per hour, zooplankton biomass, and concentration of chlorophyll a. Paul Lake was manipulated and the same variables were measured there. In Peter Lake, we expected littoral catch of minnows to first increase as minnows moved into the littoral zone due to the threat of bass predation and then decrease due to bass predation. We expected zooplankton biomass to increase as minnows moved into the littoral zone. We expected chlorophyll to decrease due to increased grazing by zooplankton. We expected that variance and autocorrelation of chlorophyll would increase as the food web passed a critical transition. We expected that the time series in Paul Lake would represent the normal variability of an unmanipulated lake
Landscape Position Project at North Temperate Lakes LTER: Fish Growth and Mercury Contaminant Data 1998 - 1999
As part of the Landscape Position Project, yellow perch were collected for mercury and isotope analysis by a combination of angling, beach seining, vertical gill net, fyke net and electrofishing in the summers of 1998 and 1999. A total of 86 yellow perch from 25 lakes were analyzed. Scales were used to determine age and length at ages 1 to 3 years. The nitrogen stable isotope signature indicates the relative food-web position of the fish relative to cladocerans collected from the same lake. The N_SIGNATURE value divided by 3.2 gives trophic position relative to cladoceran Sampling Frequency: one survey on each lake in late June through late July of 1998 or 1999 Number of sites: 25
Test data for the transverse Mercator projection
<p>This is a set of 287000 geographic points together with their coordinates in the transverse Mercator projection. The WGS84 ellipsoid (equatorial radius <em>a</em> = 6378137 m, flattening <em>f</em> = 1/298.257223563) is used, with central meridian 0°, central scale factor 0.9996 (the UTM value), false easting = false northing = 0 m.</p> <p>Each line of the test set gives 6 space delimited numbers</p> <ul> <li>latitude, φ (degrees, exact)</li> <li>longitude, λ (degrees, exact — see below)</li> <li>easting (meters, accurate to 0.1 pm)</li> <li>northing (meters, accurate to 0.1 pm)</li> <li>meridian convergence (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>scale (accurate to 10<sup>−20</sup>)</li> </ul> <p>These are computed using high-precision calculations using the exact formulas for the projection, see Lee (1976). The latitude and longitude are all multiples of 10<sup>−12</sup> deg and should be regarded as exact, except that λ = 82.63627282416406551° should be interpreted as exactly (1 − <em>e</em>) 90°, where <em>e</em> is the eccentricity given by <em>e</em><sup>2</sup> = <em>f</em> (2 − <em>f</em> ).</p> <p>The contents of the file are as follows:</p> <ul> <li>250000 entries randomly distributed in φ ∈ [0°, 90°], λ ∈ [0°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 0°</li> <li>1000 entries randomly distributed on φ = 0°, λ ∈ [0°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 90°</li> <li>1000 entries close to φ = 90° with λ ∈ [0°, 90°]</li> <li>1000 entries close to φ = 0°, λ = 0° with φ ≥ 0°, λ ≥ 0°</li> <li>1000 entries close to φ = 0°, λ = 90° with φ ≥ 0°, λ ≤ 90°</li> <li>2000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° with φ ≥ 0°</li> <li>25000 entries randomly distributed in φ ∈ [−89°, 0°], λ ∈ [(1 − <em>e</em>) 90°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = 90°</li> <li>1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = (1 − <em>e</em>) 90°</li> <li>1000 entries close to φ = 0°, λ = 90° (φ < 0°, λ ≤ 90°)</li> <li>1000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° (φ < 0°, λ ≤ (1 − <em>e</em>) 90°)</li> </ul> <p>The entries for φ < 0° and λ ∈ [(1 − <em>e</em>) 90°, 90°] use the “extended” domain for the transverse Mercator projection explained in Sec. 5 of Karney (2011). The first 258000 entries have φ ≥ 0° and are suitable for testing implementations following the standard convention.</p>
Historical Weather, Load, Wind, and Solar Data for the Salt River Project
<p>We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:</p><ol><li>"Time_UTC"; Coordinated Universal Time (UTC); Time of day.</li><li>"T2"; Fahrenheit; 2-m air temperature.</li><li>"Q2"; kg/kg; 2-m water vapor mixing ratio.</li><li>"SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface.</li><li>"GLW"; W/m^2; Downwelling longwave radiative flux at the surface.</li><li>"WSPD"; m/s; 10-m wind speed.</li><li>"Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes.</li><li>"Load"; MWh; Simulated hourly demand for electricity.</li><li>"Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database.</li><li>"Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ.</li><li>"Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.</li></ol>
Topographic data and orthomosaics of the Noordwest Natuurkern project
<p>This data set contains digital elevation models and orthomosaics of the Noordwest Natuurkern region near Bloemendaal aan Zee, the Netherlands. High-frequency wind and rain data are also provided. The data descriptor paper can be found <a href="https://doi.org/10.3390/data9020037" target="_blank" rel="noopener">here</a>, and provides full details of data collection and processing, file format, file naming, and so on. The point clouds from which the elevation models were computed, are not available in this data base but can be obtained from the data creator upon reasonable request.</p> <p>v2.1.0: 41 digital elevation models (20080510 - 20241112; 1x1 m) and 27 orthomosaics (20130501 - 20241112; 1x1 and 0.05x0.05 m) - meta data file - wind and rain data - updated from v2.0.0 on January 8, 2025 (see ChangeLog.txt)</p> <p> </p>
Data from the project "Nationalutgåva av de äldre geometriska kartorna", 1630–1655
<p>The project for the oldest geometrical maps, which took place between 2001 and 2010, aimed to make the content of these maps accessible to both the scientific community and the public.</p> <p>The large-scale maps were created during the period 1630–1655. The largest collection is held at the Swedish National Archives, within the Land Survey's archives. Additional maps are scattered across other public archives and private ownerships. The total number of map images is approximately 12,000.</p> <p>The project involved a comprehensive inventory of the archives, leading to a significant number of new discoveries. The original maps were scanned by the National Archives' photo unit. Each map image was printed as a scaled copy on archival paper. Following this, all text on the maps was interpreted, transcribed, systematized, and registered within a dedicated database. Additionally, the content on the maps regarding farms and settlements were located and spatially registered as point objects within a GIS application.</p> <p>The "National Edition of the Oldest Geometrical Maps" project was undertaken on behalf of Vitterhetsakademiens kommitté för historiska kartor. Funding was provided by the Royal Swedish Academy of Letters, History and Antiquities and Riksbankens Jubileumsfond (principal investigator: Clas Tollin). Additional funding was provided by the Swedish Research Council to process and publish the database online. The project collaborated with the Institute for Language and Folklore, the Swedish Land Survey (Lantmäteriet), and the Swedish National Archives.</p>
New maps of global geologic provinces and tectonic plates: global tectonics data and QGIS project file
<p>The global tectonics data compilation is a set of raster and vector data that are useful for investigating tectonics past and present. The datasets are useful on their own or can be used in GIS software, which includes the QGIS project file for convenience. The datasets include our new models for tectonic plate boundaries and deformation zones, geologic provinces and orogens. Additional datasets include earthquake and volcano locations, geochronology, topography, magnetics, gravity, and seismic velocity.</p> <p>The global tectonics collection is suitable for research and educational purposes.</p>
Field data obtained from the implementation of the Field Protocols (1, 2, and 3) from WP3 in B-GOOD Project
<p>Dataset of the field data obtained from the implementation of the field protocols 1, 2, and 3, developed under B-GOOD (Giving Beekeeping Guidance by cOmputatiOnal-assisted Decision making, Grant agreement No. 81762) project in WP3.</p> <p>The primary goal of field data collection was to gather data that could be used for the validation of floral resources maps and models developed across the various tasks within WP3, and also to fill specific information gaps during the development of the phenological model. The fieldwork was conducted in three countries (Portugal, Belgium, and the United Kingdom) using three field protocols developed under B-GOOD and described in Milestone MS15.</p> <p>These protocols are part of Tasks 3.1 and 3.3 and serve various purposes. The primary goal of Field Protocol 1: "<em>Assessment of plant species composition on key landscape elements/habitats important for bees</em>" was to determine the species composition in selected key plant communities (<em>i.e.</em> landscape elements or habitats) important for bees. Field Protocol 1 was used to determine the plant species composition of specific ALMaSS landscape elements and to confirm/validate the plant composition of some BIOEUNIS habitat types, developed in Task 3.1. The main goal of Field Protocol 2: "<em>Assessment of Phenology of Floral Resources for Bees</em>" was to determine the flower phenology of targeted plant species to construct flowering phenological curves for targeted plant species. Field Protocol 2 was used to validate the floral resource models developed in Task 3.2 (see Section 2.4). The primary goal of Field Protocol 3: "<em>Floral resources evaluation (detailed method)</em>" was to conduct a detailed evaluation of the floral resources in each landscape window with B-GOOD mini-apiaries to map resource availability. Field protocol 3 was divided into two parts: Part 1 - “<em>Assessment and quantification of floral resources</em>”, aiming to determine the species composition, species cover, and flower abundance; and Part 2 - “<em>Flowering species characterization</em>”, aiming to quantify the number of flowers per individual plant, and the nectar and pollen production of target plant species. Field Protocol 3 was also used to make a detailed assessment of plant species composition and plant resources at the landscape level, as well as to fill the gaps in knowledge about pollen and nectar production of some target plant species. The field data collected by the implementations of the field protocols could be categorized into three main groups: Plant Species Composition (Field Protocol 1 and Field Protocol 3: Part 1), Phenology of Floral Resources (Field Protocol 2), and Flowering Species Characterization (Field Protocol 3: Part 2).</p> <p>Field protocols have been implemented in Portugal, the United Kingdom, and Belgium. Field protocols 2 and 3 were fully implemented in the three countries. However, field protocol 1 was not implemented as a stand-alone field protocol in Portugal and the United Kingdom due to logistical and time constraints primarily caused by the COVID pandemic. However, this does not hamper our ability to obtain landscape-specific plant composition data because the information gathered in this protocol can be derived entirely from the implementation of the first part of Field Protocol 3. As a result, for Portugal and the United Kingdom, Field Protocol 1 data was derived from the first part of Field Protocol 3. The full dataset gathered by the implementation of these three protocols is available here.</p> <p>The files “field-data-protocol-1-be.xlsx”, “field-data-protocol-1-pt.xlsx” and “field-data-protocol-1-uk.xlsx” have the field data obtained from the implementation of Field Protocol 1: "Assessment of plant species composition" in Belgium, Portugal, and the UK, respectively.</p> <p>The files “field-data-protocol-2-be.xlsx”, “field-data-protocol-2-pt.xlsx” and “field-data-protocol-2-uk.xlsx” have the field data obtained from the implementation of Field Protocol 2: "Assessment of Phenology of Floral Resources" in Belgium, Portugal, and the UK, respectively.</p> <p>The files “field-data-protocol-3-part-1-be.xlsx”, “field-data-protocol-3-part-1-pt.xlsx “and “field-data-protocol-3-part-1-uk.xlsx” have the field data obtained from the implementation of Field Protocol 3 - Part 1 “Assessment and quantification of floral resources” in Belgium, Portugal, and the UK, respectively.</p> <p>The file “field-data-protocol-3-part-2-be-pt-uk.xlsx” have the field data obtained from the implementation of Field Protocol 3 - Part 2 - “Flowering species characterization” in Belgium, Portugal, and the UK.</p> <p>For further details, see Alves da Silva et al. 2020. Field protocols for the assessment of Floral Resources. Milestone MS15 EU Horizon 2020 B-GOOD Project. GA No. 817622 and Ziółkowska et al 2022. Floral Resource Models Validation Deliverable D3.4 EU Horizon 2020 B-GOOD Project, GA No. 817622.</p>
Project Tycho Level 2 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1888-2014
Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point.<p></p><p>Project Tycho level 2 version 1.1.0 data include data counts that have been filtered from the raw data to render standardized data that can be used immediately for analysis. All level 2 data were originally reported in a consistent format and have not been transformed into a standard format by Project Tycho staff, except for smallpox records that included repeated counts for the same location and week, but sometimes with different numbers. These duplicate smallpox records have been averaged into one count for each location and week. Level 2 data include counts for a wide variety of diseases and locations for varying time periods. Because we removed data in an inconsistent format from level 2 data, counts may be missing for certain diseases, locations, or years. For the most complete collection of standardized data, we encourage users to use Project Tycho version 2.0 datasets.</p><p>More detailed methods and additional information about the origin of Projec Tycho level 2 version 1.1.0 data can be found in our original publication in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a></p><p>Level 2 version 1.1.0 data is represented in a CSV file with 11 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>country: a two digit country abbreviation, only including "US" in version 1.1.0</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported, in all capitals</li><li>event: an indicator representing the disease outcome reported, including "CASES" or "DEATHS"</li><li>number: the reported number of cases or deaths</li><li>from_date: the start date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>to_date: the end date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>url: the URL of the source document from which the count was obtained</li></ul><p></p>
Project Tycho Level 1 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1916-2011
<p>Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point. Project Tycho level 1 data include data counts that have been standardized for a specific, published, analysis. Standardization of level 1 data included representing various types of data counts into a common format and excluding data counts that are not required for the intended analysis. In addition, external data such as population data may have been integrated with disease data to derive rates or for other applications.</p><p>Version 1.0.0 of level 1 data includes counts at the state level for smallpox, polio, measles, mumps, rubella, hepatitis A, and whooping cough and at the city level for diphtheria. The time period of data varies per disease somewhere between 1916 and 2011. This version includes cases as well as incidence rates per 100,000 population based on historical population estimates. These data have been used by investigators at the University of Pittsburgh to estimate the impact of vaccination programs in the United States, published in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a>. See this paper for additional methods and detail about the origin of level 1 version 1.0.0 data.</p><p>Level 1 version 1.0.0 data is represented in a CSV file with 7 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported: HEPATITIS A, MEASLES, MUMPS, PERTUSSIS, POLIO, RUBELLA, SMALLPOX, or DIPHTHERIA</li><li>cases: the number of cases reported for the specified disease, epidemiological week, and location</li><li>incidence_per_100000: the number of cases per 100,000 people, computed using historical population counts for cities and states as reported by the US Census Bureau</li></ul><p></p>
CENTAUR project laboratory testing data
<p>This dataset contains results from testing carried out at a laboratory facility at the University of Sheffield (UK) as part of the <a href="https://www.sheffield.ac.uk/centaur">CENTAUR project</a>. CENTAUR is an EC funded Horizon 2020 Innovation Action. The project has developed a system to reduce flood risk in urban areas by utilising existing available storage capacity in urban drainage networks through the use of a gate installed in an existing manhole. The gate is controlled by Fuzzy Logic, using data from level sensors.</p> <p>The laboratory facility is described in the 'CENTAUR_Lab_facility.pdf '. Further details of the sensors and logging system are provided in 'Data_File_Column_Descriptions.csv'.</p> <p>The file 'Test_Record.csv' describes all tests carried out. This dataset contains 83 csv data files in for days when good data was collected, these are zipped into 'DataFiles.zip'. Each csv file within the .zip contains the test results for one day, the files are named with the date of testing in the format yymmdd. The csv data files do not include column headers, but a full description of the data in each column is provided in 'Data_File_Column_Descriptions.csv'. The csv files contain data from all sensors, but the time period of the data from each sensor (or sensor set) and timesteps are not the same, hence for each sensor / sensor set there is a separate time column. The sampling interval for the level sensors is given in column 26 of 'Test_Record.csv', this will be correct for the test period, but outside the tests the interval was often increased and this may be seen in the data files. The gate / FCD sampling interval is the same as the Fuzzy Logic interval in column 27 of 'Test_Record.csv', although the position is only reported when the gate / FCD is active - i.e. not fully open. At the end of a test the FCD will return to the fully open position (100%), but this final datapoint is not recorded. The flow rate and downstream valve position sampling interval are given in column 12 of 'Test_Record.csv'.</p> <p>Test numbers and fuzzy logic version ids are simplified for the journal paper 'Demonstrating a Fuzzy Logic algorithm for real-time flow control in a full-scale laboratory environment' which is currently under review with the Urban Water Journal. A correlation between the information in the paper and in 'Test_Record.csv' can be found in 'Paper_Test_Numbers.csv'.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641931.</p>
Raw Data of Pilot Plant Runs for CONSENS Project (Case Study 1)
<p>In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and <em>o</em>-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum. </p> <p>The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17.</p> <p>In the following the contents of the files are explained.</p> <p><strong>NIR_data_AQ15_raw.zip: </strong>Contains Bruker binary files (0-Files) of NIR spectrometer at AQ15 (Location is located subsequently to NMR spectrometer)</p> <p><strong>NMR_spectra_raw.zip: </strong>Contains Spinsolve files (Binarys of FID and Spectrum, DX-Files) of NMR spectrometer. The use of DX-files files is not recommended.</p> <p><strong>PCS_data_csv.zip</strong>: Contains csv-files of the process control system (PCS) including data of mass flow controlers (*_Bilanz.csv), filling level (*_FillLe.csv), pressures (*_pres), temperatures (*_Temp), position of valves (*_Valves). Relevant labels are: BP13 = LiHMDS storage tank, BP12 = aniline storage tank, BP12 = <em>o</em>-FNB storage tank, CM003 and CM004 = tubular reactors, T0041 and T005 = Temperature at reactor exits, P009 and P003 = Pressure at reactor inlets, P006 = Pressure at reactor exits.</p> <p><strong>housing_data_NMR.csv</strong>: Contrains data of NMR enclosure of all four days. Each columns from left to right represent timestamps, bypass pressure (bar), bypass temperature (°C), Gasalarm (logical), bypass actual flowrate (g min<sup>-1</sup>), bypass flowrate setpoint (g min<sup>-1</sup>), bypass density (kg m<sup>3</sup>)</p> <p><strong>matlab_variables_explanation.xlsx</strong>: Explanation of variables used in matlab structure "data_validation_run".</p> <p><strong>data_validation_run.mat</strong>: Matlab structure containing most relevant process data including NMR results, NIR results, housing data of NMR, and process control system data.</p>
Ranking data for the eo-Delphi project
<p>The eo-Delphi project (https://osf.io/8f3aj/) created consensus for a core set of outcomes for future studies evaluating the effects of oral corticosteroid therapy in chronic obstructive pulmonary disease (COPD) patients stratified by eosinophil levels. The dataset presented here reports individual ranking scores for proposed outcomes.</p>
Data from a three-phase Delphi study used to investigate Knowledge Infrastructure for Research Data in Norway, KIRDN_Data; PhD project
<p>A modified three-phase Delphi study was used to explore the knowledge infrastructure for research data in Norway. The study includes different stakeholders involved in research data sharing. A Delphi study is characterised by the use of an expert panel to elicit opinions on a shared reality from different perspectives. Data collection is performed in several rounds with the intention of reaching consensus or solving an issue. </p> <p>A group of 24 experts took part in the study. The group consisted of policy-makers, representatives of national service providers, and researchers and research support staff from four Norwegian universities. The participants were invited based on their involvement in the development of policies, infrastructure or data-related research support. The research support staff were recruited to include representatives from different research support services at the universities, including libraries, research offices and IT departments. While the researchers were selected from based on their receival of EU funding with requirements of data management plans.</p> <p>Data were collected in three phases. The first phase, the ‘exploration phase’, was conducted using open interviews lasting approximately one hour in January/February 2018. The purpose of this phase was to obtain an initial overview of the panel members opinions’ on issues regarding research data management.</p> <p>In the second phase, the ‘evaluation phase’, conducted in August/September 2018, participants answered a survey containing nine questions on topics such as data stewardship, DMPs, ethical aspects of data sharing and core functions in a research data infrastructure. The survey was designed to further explore issues and tensions uncovered in the first interviews. Several of the questions were formulated as statements that the participants were asked to agree or disagree upon. </p> <p>The third, ‘concluding phase’ was conducted using interviews in March/April 2019. These interviews lasted approximately 30 minutes and were based on results from the questionnaire as well as the first interview. Participants were asked whether they had thoughts on the preliminary findings of the study. </p> <p>Based on requests from some of the participants, the questions were sent to all participants prior to the data collection, in all three phases. The participants were also sent the transcripts from the interviews and were asked for permission to share the complete material or parts of the data material to which they contributed. </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.