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564 results for “June”
Perceptions of heat and air pollution among older adults experiencing homelessness in Phoenix, Arizona (USA) (June 2024)
This dataset consists of survey responses from 40 older adults experiencing homelessness in Phoenix, Arizona (USA), assessing the perceptions of environmental hazards—specifically heat and air pollution—and attitudes toward coping resources and behaviors. The survey includes 51 questions co-created with community members across five categories: demographics and behavior, movement/transportation, climate perceptions, resource availability, and local knowledge mapping. Surveys were conducted indoors at a local service provider over two days in June 2024, when outdoor temperatures reached 42 degrees C and 45 degrees C. The dataset offers insights into potential public service reforms to mitigate heat and air pollution risks among Arizona’s unhoused population. The survey was approved by the Institutional Review Board of Arizona State University (IRB approval number: STUDY00018399).
Movements of aquatic predators within the Shark River estuary (FCE LTER), Everglades National Park, South Florida, USA, June 2007 - ongoing
In South Florida, the allocation of freshwater resources is a constant source of debate. Stakeholders competing for freshwater include agriculture, rapidly growing urban populations, and the natural environment with its associated ecosystem services. Among these services, one of the most valuable is the provisioning of coastal recreational fisheries, which generates roughly $8 billion annually in angler expenditures in Florida alone. Yet, the interplay between freshwater allocation and the sustainability of these coastal fisheries remains poorly understood. One pathway of influence is through the availability of resources and food. Seasonal rainfall and freshwater management drive pulses of freshwater marsh prey into estuaries, creating short-lived but abundant foraging opportunities. Previous research has shown that these prey pulses occur primarily in the inland reaches of the estuary, providing resources for recreationally and ecologically important consumers such as the Common Snook (Centropomus undecimalis), Florida Largemouth Bass (Micropterus salmoides), Red Drum (Sciaenops ocellatus), Atlantic Tarpon (Megalops atlanticus), Bull Shark (Carcharhinus leucas), and American Alligator (Alligator mississippiensis). However, it is unclear how far these species move to exploit this subsidy, or whether such pulses increase reproductive output and long-term population stability. Further, sea level rise is changing how economically and ecologically important taxa use estuarine environments. To address these questions, we use acoustic telemetry to track the multi-year (2007–present) movements of key estuarine taxa, including Common Snook, Florida Largemouth Bass, American Alligator, and Bull Shark, within the Shark River Estuary of Everglades National Park. This multi-species approach expands our focus from freshwater and estuarine predators to include apex predators that link freshwater, estuarine, and marine ecosystems. From a science perspective, our research provides
Molecular composition of dissolved organic matter from Lake Mendota from June – November 2017, analyzed by Fourier-transform ion cyclotron resonance mass spectrometry
Dissolved organic matter (DOM) is a complex mixture of organic compounds found in all natural waters. Its composition affects its reactivity towards numerous processes. Its composition is a function of both its source (e.g., allochthonous or autochthonous) as well as the extent of environmental processing it has undergone (e.g., chemical or biological degradation). Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) allows for the characterization of dissolved organic matter at the molecular level. The water sample was collected near the NTL-LTER research buoy on Lake Mendota. Formula assignments were made to raw mass to charge ratios detected in the mass spectrum using a custom processing script and resulting in a list of chemical formulas making up the DOM sample.
Ultraviolet-visible spectroscopy absorbances for dissolved organic matter from Lake Mendota from June – November 2017
Dissolved organic matter (DOM) is a complex mixture of organic compounds found in all natural waters. Its composition affects its reactivity towards numerous processes. Its composition is a function of both its source (e.g., allochthonous or autochthonous) as well as the extent of environmental processing it has undergone (e.g., chemical or biological degradation). Ultraviolet-visible (UV-vis) spectroscopy is an analytical technique commonly used to assess the composition of dissolved organic matter in water samples. Here, we present spectra from Lake Mendota samples collected from June - November in 2017 at the surface of Lake Mendota as well as at specific depths within the water column. All samples were collected near the NTL-LTER research buoy. Absorbance values are listed for wavelengths 200 - 800 nm for each sample.
Mapping of a Mid-depth Salinity Maximum Intrusion south of New England in June 2021
<div> <div> <p>This dataset contains data from a process-oriented research cruise aboard the R/V Neil Armstrong from June 18th to July 2nd. The goal of this cruise was to map the three-dimensional structure of a mid-depth salinity maximum intrusion of warm salinity slope water extending onto the continental shelf south of New England. This was done through the use of Autonomous Underwater Vehicles (two REMUS 100 vehicles and one Tethys class AUV (Long Range AUV or LRAUV)), a towed Rockland Scientific Vertical Microstructure Profiler (VMP 250), and ship-board CTD and ADCP measurements. More details about the processing, data coverage, and usage can be found in the accompanying manuscript. This cruise took place on the shelf waters south of Cape Cod, MA, extending to the shelf break, with all of the data collected between 40°N to 41°N and 71.5°W to 70°W. Attached is a data map showing the location of all data included within this dataset. </p> </div> <div> <p> </p> </div> <div> <p>File Descriptions: </p> </div> <div> <p><strong>Datamap.jpg </strong></p> </div> <div> <p>A map of the locations of all data included within this dataset. </p> </div> <div> <p> </p> </div> <div> <p><strong>CTD_summer2021.mat </strong></p> </div> <div> <p>This file contains profiles from the ship-board CTD (SeaBird 911+). Raw data was processed and gridded into 1 decibar bins using standard procedures in Seasave V 7.26.7.121 (Look at cnv file header for details about processing). This file is organized as a structure, with each variable in the data being a different field called by dot notation and each row with the structure being a different CTD profile. Biooptical variables are not quality-controlled. </p> </div> <div> <ul> <li> <p>CTD.time: the time of each profile in the MATLAB datetime format (from the processed SeaBird header file) in GMT </p> </li> <li> <p>CTD.lon: degrees longitude of the profile (from the processed SeaBird header file) </p> </li> <li> <p>CTD.lat: degrees latitude of the profile (from the processed SeaBird header file) </p> </li> <li> <p>CTD.pres: the pressure in decibar at each location of the profile </p> </li> <li> <p>CTD.sal: the seawater practical salinity in psu </p> </li> <li> <p>CTD.temp: the seawater in-situ temperature in °C </p> </li> <li> <p>CTD.flor: seawater fluorescence in mg/ m3 </p> </li> <li> <p>CTD.depth: depth at each location within the profile in meters </p> </li> <li> <p>CTD.density: sigmatheta (the potential seawater density with respect to a reference pressure of 0 db) in kg.m3 minus 1,000kg/m3 </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p><strong>CTD_Darter_MMMdd.mat and CTD_Edgar_MMMdd.mat </strong></p> </div> </div> <div> <div> <p>These files contain the data from the REMUS 100 missions, with Darter and Edgar being the two different REMUS 100 vehicles. </p> </div> <div> <ul> <li> <p>Conductivity: conductivity in mS/cm </p> </li> <li> <p>Depth: depth in meters </p> </li> <li> <p>Latitude: degrees latitude </p> </li> <li> <p>Longitude: degrees longitude </p> </li> <li> <p>Mission_number: the number of the REMUS mission </p> </li> <li> <p>Mission_time: time during the mission in seconds since midnight in GMT </p> </li> <li> <p>Salinity: the seawater practical salinity in psu </p> </li> <li> <p>Sound_speed: the sound speed in m/s </p> </li> <li> <p>Temperature: the seawater temperature in °C </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p><strong>LRAUV_20210623T194917.mat and LRAUV_20210624T145829.mat </strong></p> </div> <div> <p>These files contain data from the Tethys Class LRAUV (Long Range AUV) missions. Each file contains 10 structure variables. </p> </div> </div> <div> <div> <ul> <li> <p>CTD_Seabird: structure containing the bin median temperature in °C and salinity in PSU. </p> </li> <li> <p>depth: the depth at each data point in meters. </p> </li> <li> <p>fix_residual_percent_distance_traveled: underwater dead-reckoned navigation error (based on GPS fix when on surface) as a percentage of distance traveled </p> </li> <li> <p>latitude: Latitude at each data point (not corrected for vehicle drift in underwater current) </p> </li> <li> <p>latitude_fix: latitude of GPS fix (vehicle surfaced) </p> </li> <li> <p>longitude: Longitude at each data point (not corrected for vehicle drift in underwater current) </p> </li> <li> <p>longitude_fix: longitude of GPS fix (vehicle surfaced) </p> </li> <li> <p>platform_battery_charge: The battery charge in ampere-hour </p> </li> <li> <p>time_fix: time in seconds since January 1, 1970 (epoch time) </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p><strong>VMPtransact_YYYYMMdd.mat</strong></p> <p>Vertical Microstructure Profiler (Rockland Scientific VMP 250) </p> </div> <div> <p>These files contain the processed data for each Vertical Microstructure Profiler (Rockland Scientific VMP 250) transect, consisting of multiple profiles. Data has been gridded on a 1 decibar equidistant grid using standard procedures in Rockland Scientific’s processing software. Note: Bio-optical variables and dissipation rates have not been quality-controlled. </p> </div> <div> <ul> <li> <p>Time: Time in MATLAB datenum format (days since 0000-00-00 00:00:00) in GMT </p> </li> <li> <p>z: Pressure in decibar </p> </li> <li> <p>T: in-situ temperature in degC </p> </li> <li> <p>cnd: conductivity in mS/cm </p> </li> <li> <p>Chl: Chlorophyll from fluorescence in mg/ m3 </p> </li> <li> <p>turb: Turbidity in NTU </p> </li> <li> <p>eps: dissipation rate inferred from microstructure shear in m^2/s^3. (Note: Dissipation estimates come from standard fitting of microstructure data within a 1 decibar bin to a turbulence spectrum within Rockland Scientific’s standard processing. The dissipation data in the provided files has not been quality-controlled. </p> </li> </ul> </div> <div> <p> VMP-data was georeferenced by comparing the time stamps of VMP and processed ADCP files. </p> </div> <div> <p> </p> </div> <div> <p><strong>ADCP_ar50_wh300.mat </strong></p> </div> <div> <p>This file contains the data from the shipboard ADCP (Teledyne WH300 kHz). ADCP data was processed aboard using standard procedures in UHDAS/CODAS (University of Hawaii Technical Services Program, servicing UNOLS vessels (<a href="https://currents.soest.hawaii.edu/docs/adcp_doc/index.html" target="_blank" rel="noreferrer noopener">https://currents.soest.hawaii.edu/docs/adcp_doc/index.html). Vertical bin size is 2 m. </a>u: zonal (positive towards east) velocity component in m/s </p> <ul> <li> <p>v: meridional (positive towards north) component in m/s </p> </li> </ul> </div> </div> <div> <div> <ul> <li> <p>txy: time, longitude, and latitude of the velocity profiles. Time is in decimal days, with noon of Jan 1 being 0.5 decimal days and noon of January 20th being 19.5 decimal days of the reference year. For another example, 6am on June 18, 2021, is decimal day 168.25. All times are in GMT. </p> </li> <li> <p>refyear: The reference year from which the decimal days are calculated. </p> </li> <li> <p>depth: vertical coordinate of the velocity bin center </p> </li> <li> <p>pgood: percent good, a quality parameter showing the fraction of good pings within an ensemble average. </p> </li> <li> <p>spd_u: zonal ship speed in m/s </p> </li> <li> <p>spd_v: meridional ship speed in m/s </p> </li> <li> <p>tr_temp: ADCP transducer temperature in deg C </p> </li> <li> <p>amp: backscatter amplitude in relative units </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> </div>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 35 known metabolites(all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in one Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and one organism part (annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable STATO terms. The measurements over these metabolites, which were made in 2 distinct experiments, were extracted from: a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018 a supplementary material table available as a pdf from 'Biosynthesis of monoterpene scent compounds in roses' by Magnard et al, Science 03 Jul 2015 identified by the following doi: https://doi.org/10.1126/science.aab0696. This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR)and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.It is associated to the following project: https://github.com/proccaserra/rose2018ng-notebook with all the necessaryinformation, executable code and tutorials in the form of Jupyter notebooks.</p>
Salt River Wetlands denitrification rate, dissimilatory nitrate reduction to ammonium rate, dissolved organic carbon concentration in June 2016 as well as soil porosity and bulk density
Raw and derived data used to calculate denitrification and dissimilatory nitrate to ammonium (DNRA) from push-pull experiments with added isotopically labelled nitrate. Experiments were conducted in 2016 in the Salt River Accidental Wetlands in three different patch types: Unvegetated, dominated by Ludwigia peploides, and dominated by Typha species (T. domingensis and T. latifolia). Data include start and end of incubation concentration of nitrate, ammonium, atom percent 15N in ammonium, dissolved organic carbon, excess mass 29-N2, and excess mass 30-N2. Soil data was collected from the same patch types including soil moisture, porosity, and bulk density.
Field Evidence of Carbon and Nitrogen Stabilization through Mineral Associated Organic Matter Formation in Coastal Wetland Soils from Apalachicola, Florida, collected in June, 2022.
This data set was used to observe the role of Mineral Associated Organic Matter Formation (MAOM) on biogeochemical soil properties in three coastal wetlands in Apalachicola, Florida. One wetland was restored using beneficial dredged sediment, increasing the soil's inorganic matter content. Soil samples were collected in June 2022 from this wetland and two nearby reference wetlands: one with high organic matter and the other with higher inorganic matter content. The samples were analyzed at the University of Central Florida for biogeochemical properties to determine which properties were most related to MAOM pools.
Composited land surface temperature of the greater Phoenix, Arizona, USA metropolitan area and surrounding Sonoran desert derived from cloud-free, summer (June, July, and August) Landsat imagery: 1985-2020
This project calculates land surface temperature (LST) from remotely sensed imagery. The intent is to extend the previous version of the LST data for the CAP LTER study area in central Arizona, USA to include 2020 and update the products so that they are based on a composite of images from each year (all available cloud-free acquisitions from June, July, and August) in the analysis to reduce the potential for outlier images or pixels to impact analyses. The aim is to make updated LST data accessible to stakeholders and researchers studying the greater Phoenix, Arizona, USA metropolitan area. LST is calculated from cloud-free Landsat 5 and 8 imagery (30m resolution) from summer months (June, July, and August) in 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. All images are cropped to the CAP LTER study area boundary.
Surface Water Quality Monitoring Data collected in South Florida Coastal Waters (FCE LTER), Florida, USA, June 1989-ongoing
The Southeast Environmental Research Center at Florida International University operates a network of 331 fixed sampling sites distributed throughout the estuarine and coastal ecosystems of south Florida. The purpose of this network is to address concerns in regional water quality which cross and overlap separate political boundaries. Funding has come from different sources with individual programs being added as funding became available. Biscayne Bay, Florida Bay, Whitewater Bay, Ten Thousand Islands, Rookery Bay, Estero Bay, and Pine Island Sound are sampled monthly while the Florida Keys National Marine Sanctuary (FKNMS) and the southwest shelf are sampled quarterly. Variables currently being measured include surface and bottom temperature, salinity, dissolved oxygen, nitrate, nitrite, ammonium, total nitrogen, total organic nitrogen, total phosphorus, soluble reactive phosphorus, total organic carbon, total silicate, chlorophyll a, alkaline phosphatase activity, turbidity, and light extinction. The purpose of this network is to address concerns in regional water quality which cross and overlap separate political boundaries. One of the products is a quasi-synoptic big picture of nutrient and phytoplankton biomass distributions over the South Florida Coastal Waters. The SERC network will, in time, provide us with the data necessary to determine whether conditions within the estuaries and sanctuary are improving or declining.
Fish community data obtained from Antillean-Z fish trap deployment in the Eastern Gulf of Shark Bay, Australia from June 2013 to August 2013
This dataset consists of capture data of teleost communities caught in Antillean-Z fish traps deployed in 2013. Also included are anciliary environmental data as well as trap deployment information. This dataset will be used to determine changes in teleost community structure and abundance in response to an extreme temperature event and associated decline in seagrass cover in Shark Bay, Western Australia.
Periphyton Nutritonal Data across the freshwater Everglades (FCE): June 2016-Feb 2017
Fatty Acid profiles were obtained for various periphyton sources (food for consumers) accross the freshwater Everglades in June-July 2016, the peak of the wet season, and in January 2017, the peak of the dry season. These data include specific fatty acid markers for each periphyton type (floating mat, benthic mat, epiphyton, and/or filamentous green) collected. Nutritional measurements for various periphyton sources (food for consumers) were also obtained during the same intervals. These measurements include periphyton type (floating mat, benthic mat, epiphyton, and/or filamentous green), periphyton availability (volume, organic content and mineral content), stoichiometric data (TP, C:N, C:P, and N:P), and macronutrient composition (protein, carbohydrate, lipid). These measurements form the basis for the data discussed in the dissertation chapter, "Freshwater herbivores dominate over omnivores in suboptimal habitats because of their ability to exploit low quality resources" by Jessica L Sanchez and Joel C Trexler.
Water Quality Data (Rainfall-driven autosampler) from the Shark River Slough, Everglades National Park (FCE LTER), Florida, USA, June 2003 - ongoing
Water quality samples are being collected using ISCO autosamplers at all freshwater sites: SRS1a (not active), SRS1c (not active), SRS1d, SRS2, and SRS3. Rain level actuators are used at the sites to trigger water sampling after rain events exceed a given threshold of duration and/or intensity. As currently programmed, when a rain event at a site exceeds the threshold of 2.5 cm per hour, the autosampler at that site collects a 1000mL sample 30 minutes later. The samples are retrieved from the site every 3-4 weeks and analyzed for total phosphorus (TP), total nitrogen (TN), and salinity. Salinity values were not taken consistently from 2000 to mid-2017; those values were replaced by -9999 in the data. See also Shark River Slough precipitation data package (knb-lter-fce.1092) and Shark River Slough extensive water quality data (knb-lter-fce.1072) on the FCE LTER website's data catalog or in the EDI repository.
Survey of high marsh plant structure and biomass for Spartina, Juncus, Borrichia and Batis specimens on Sapelo Island, Georgia during May to June 2015
From May 29th to June 27th of 2015, plant specimens of Spartina, Juncus, Borrichia, and Batis were extracted from Georgia Coastal Ecosystems Study Site 6 and brought to the UGA Marine Institute research center for structural and biomass measurements. The focus of the structural measurements were to capture the top-down horizontal length and branching angles of rhizomes connecting the above-ground plant shoots. The height of plant shoots were also measured, along with the dry-weight mass of all measured plant components. The purpose of these measurements was to provide data that will be the basis for parameter values used in the Virtual Prairie agent-based model for clonal plant growth and competition. Note that a web-based index of plant photographs from this study are available at http://gce-lter.marsci.uga.edu/public/datasets/ancillary/PLT-GCET-1508/.
Average monthly sea ice coverage for various PAL LTER sub-regions West of the Antarctic Peninsula derived from passive microwave satellite data, 1978 - June 2024.
Monthly sea ice coverage derived from passive microwave satellite measurements and extracted for various PAL LTER subregions. Several different sea-ice metrics are provided including (1) monthly sea-ice extent, sea-ice area, and open-water area (km^2) extracted for the greater WAP region (from the AP to 80W) and for 3 PAL LTER grid regions: the original PAL grid (000-900 lines), the PAL 'DSR' grid (200-600 lines), and the PAL 'new' grid (-200 to 600 lines); and (2) monthly sea-ice concentration (%) extracted for small PAL subregions, including the nominal penguin foraging areas (~200km by ~200km) southwest of King George Island (KGI), Anvers, Avian and Charcot islands, as well as for Marguerite Bay (~140km by ~140km) (inland of the area defined for Avian).
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.
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Frictionless Tabular Data Package for GC-MS data from the 'Rose Genome' article published in Nature genetics, June, 2018
<p>This dataset, in the form of a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data was extracted from a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018. This supplementary material table was deposited to Zenodo and is identified by the following doi: <a href="https://doi.org/10.5281/zenodo.2598799">https://doi.org/10.5281/zenodo.2598799</a></p> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data were extracted from:</p> <ul> <li>a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018</li> <li>a supplementary material table available as a pdf from "Biosynthesis of monoterpene scent compounds in roses" by Magnard et al, Science 03 Jul 2015 identified by the following doi: <a href="https://doi.org/10.1126/science.aab0696">https://doi.org/10.1126/science.aab0696</a></li> </ul> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p> <p> </p>
Frictionless Tabular data package for GC-MS data from Rose Genome article published in Nature genetics, June, 2018
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxId) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The data was extracted from a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018. This dataset is used to demonstrate how to make data Findeable, Accessible, Discoverable and Interoperable(FAIR) and how Tabular Data Package representations can be easily mobilized for re-analysis and data science. It is associated to the following project available from github at: https://github.com/proccaserra/rose2018ng-notebook with all necessary information and Jupyter notebooks.</p>
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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.