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1,936 results for “environmental data”
LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe – files
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (<a href="https://doi.org/10.5194/essd-13-4529-2021">https://doi.org/10.5194/essd-13-4529-2021</a>).</p> <p>LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution.<br> LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (<a href="https://doi.org/10.5194/hess-21-5293-2017">https://doi.org/10.5194/hess-21-5293-2017</a>), Chile (<a href="https://doi.org/10.5194/hess-22-5817-2018">https://doi.org/10.5194/hess-22-5817-2018</a>), Brazil (<a href="https://doi.org/10.5194/essd-12-2075-2020">https://doi.org/10.5194/essd-12-2075-2020</a>), Great Britain (<a href="https://doi.org/10.5194/essd-12-2459-2020">https://doi.org/10.5194/essd-12-2459-2020</a>) and Australia (<a href="https://doi.org/10.5194/essd-13-3847-2021">https://doi.org/10.5194/essd-13-3847-2021</a>), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.</p> <p>We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.</p> <p><strong>Note: </strong>It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that <strong>NAs</strong> are indicated by the number<strong> -999</strong> in the <strong>runoff time series</strong>.</p> <p><strong>Disclaimer:</strong> We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (<a href="https://www.gkd.bayern.de/en/rivers/discharge/tables">https://www.gkd.bayern.de/en/rivers/discharge/tables</a>) and Baden-Württemberg (<a href="https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer">https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer</a>). Runoff data from the Czech Republic may not be used to set up operational warning systems (<a href="https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf">https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf</a>).</p> <p><strong>License: </strong>This work is licensed with CC BY-SA 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Additional references: </strong>We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying <a href="https://doi.org/10.5194/essd-13-4529-2021">paper</a>.</p> <p><strong>Supplements: </strong>We have created additional files after publication (therefore non peer-reviewed):<br> 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge.<br> 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes.<br> If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at</p>
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Environmental data associated to particular health events example dataset
<p>The data represents and example output for environmental data (i.e. climate and pollution) linked with individual events through <strong>location</strong> and <strong>time</strong>. The linkage is the result of a semantic query that integrates environmental data <strong>within an area relevant to the event</strong> and selects a <strong>period of data before the event</strong>.</p> <p>The resulting event-environmental linked data contains:</p> <ul> <li>The data for analysis as a data table (.csv) and graph (.ttl)</li> <li>The metadata describing the linkage process and the data (.csv and .ttl)</li> <li>The interactive report to explore the (meta)data (.html)</li> </ul> <p>The graph files are ready to be shared and published as Findable, Accessible, Interoperable and Reusable (FAIR) data, including the necessary information to be reused by other researchers in different contexts.</p>
EVIDENT H2020– Environmental data for Sweden cities Dataset
<p>EVIDENT H2020- Environmental data for Swedish Cities Dataset</p> <p>Environmental data from 615 cities in Sweden</p> <p>Weather, in combination with residential characteristics and electricity consumption, might be useful to consider in association with other datasets. In the instance of EVIDENT, they will be examined in combination with electricity consumption to establish the correlation with weather and to examine if weather conditions contribute and should be considered for policy development.</p> <p>The data have been collected from 18.10.2021 to 4.05.2023 and refer to 615 Swedish cities. The collection has been carried out with agents created and by calling in API. In the file "Sweden_Cities_Avg_DaySect.xlsx", all cities have averaged from all measures. Also, the day has been divided into 3 sections and the averages apply to each section of the day.</p> <p>In each city, on average, there are 6 measurements per day. The source dataset is "swedish_cities_environmental.csv.". example</p> <table> <tbody> <tr> <td>country</td> <td>city</td> <td>temperature</td> <td>feels_like</td> <td>temp_min</td> <td>temp_max</td> <td>pressure</td> <td>humidity</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>wind_speed</td> <td>wind_deg</td> <td>sunrise</td> <td>sunset</td> <td>weather_description</td> </tr> </tbody> </table> <p>There are 2 more datasets, "swedish cities environmental_tranformDay.csv" and "swedish cities environmental_week.csv", and refer to transformations made in the original dataset.<br> The first file is about the day analysis, where the day has been divided into 3 sections and depending on the time of the measurement, a new column has been created in the Day section and can take values 0,1,2. In addition, there is the column day_hours which is the duration of the day in seconds from sunrise to sunset. Finally, there is pressure, humidity and wind speed. In the second file, the column weekday has been added and relates to the day of the week (e.g. Monday), and the daily analysis has been removed.</p> <p>More information can be found on the public deliverables of the EVIDENT project <a href="https://evident-h2020.eu/deliverables/">https://evident-h2020.eu/deliverables/</a>. More specifically, the experiment's theoretical framework and motivation are described in are described in deliverable D1.2 <a href="https://evident-h2020.eu/wp-content/uploads/2021/12/EVIDENT_D1.2_Assessing_behavioural_biases_and_financial_literacy.pdf">Assessing behavioural biases and financial literacy</a> and deliverable <strong>D1.3</strong> <a href="https://evident-h2020.eu/wp-content/uploads/2022/03/EVIDENT_D1.3_Specification_of_Big_Data_Analytics.pdf">Specifications of Big Data Analytics</a>, in section 4 while the final design is reported in <strong>D3.2</strong> <a href="http://evident-h2020.eu/wp-content/uploads/2023/01/EVIDENT_D3.2_Implementation-of-preparatory-actions-for-RCT-surveys-and-serious-game.pdf">Implementation of preparatory actions for RCT, surveys and serious game</a>.</p>
Observational constraints of fire, environmental and anthropogenic on pantropical tree cover - Data
<p>Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes.</p> <p>Land surface properties:</p> <ul> <li><strong>TreeCover </strong>- Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from <sup>1</sup>, regridded as per <sup>2</sup>.</li> <li><strong>urban </strong>cover from the History Database of the Global Environment, Version 3.1 (HYDE) <sup>3,4</sup></li> <li><strong>crop </strong>cover (from HYDE)</li> </ul> <ul> <li><strong>pas - Pasture </strong>Cover (from HYDE)<strong>PopDen </strong>(population density from HYDE)</li> <li><strong>BurntArea_xxxxx </strong>- Burnt area with xxxx denoting different products, provided by fireMIP <sup>5–7</sup>: <ul> <li>GFED_four: Global Fire Emissions Database, Version 4 (GFED4) <sup>8</sup></li> <li>GFED_four_s: Global Fire Emissions Database, Version 4.1, including small fires (GFEDv4.1) <sup>9</sup></li> <li>MCD_forty_five: MCD45 <sup>10</sup></li> <li>Meris: Fire_CCI4.0 <sup>11</sup></li> <li>MODIS: Fire_CCI5.1 <sup>12</sup></li> </ul> </li> </ul> <p>Climate:</p> <ul> <li><strong>MAP_xxx </strong>- Mean annual precipitation where xxx denotes data source: <ul> <li><strong>CMORPH </strong><sup>13,14</sup></li> <li><strong>CRU </strong>from version 4.03 of the Climatic Research Unit Time Series high-resolution gridded dataset (CRU TS v4.01) <sup>15</sup></li> <li><strong>GPCC: </strong><sup>16</sup></li> <li><strong>MSWEP: </strong><sup>17</sup></li> </ul> </li> <li><strong>MAT </strong>- Mean annual temperature from CRU)</li> <li><strong>MConc_xxx </strong>– Mean annual concentration of rainfall as defined by <sup>18</sup>, where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MADD_xxx</strong>- Mean annual fractional dry days from CRU - i.e. seasonality of rainfall), where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MDDM_xxx </strong>– Mean fractional dry days of the driest month.</li> <li><strong>MADM_xxx – </strong>Mean annual precipitation of the driest month<strong>.</strong></li> <li><strong>MTWM </strong>- Mean Maximum Temperature of the warmest month from CRU</li> <li><strong>MTCM </strong>- Mean minimum temperature of the coldest month from CRU</li> <li><strong>SW1 </strong>- direct downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>SW2 </strong>- diffuse downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>MaxWind </strong>(Mean Max Windspeed from CRU-(National Centers for Environmental Prediction <sup>15</sup></li> </ul> <p>‘output_summary’ contains framework output. There are several directories for different experiments, each containing a netcdf file. Along with standard latitude and longitude,each file contains ‘model_level_number’ dimension, with each layer representing the 1, 5, 10, 25, 50, 75, 90, 95 and 99% quantiles of the model posterior. The folder represents the experiment:</p> <ul> <li>Control – standard full model reconstruction</li> <li>noHumans – without human influence (from crop, pasture, population density or urban influence)</li> <li>noMortality – without disturbance stress (burnt area, wind, heat stress, rainfall seasonality</li> <li>noMAP – without mean annual precip influence.</li> <li>noNoneMAT – without mean annual temperature influence.</li> <li>noFire – tree cover without the influence of fire</li> <li>noDrought – without the influence of rainfall distribution</li> <li>noTasMort – without mortality from heat stress</li> <li>noWind – without influence from max. windspeed</li> <li>noPas – without exclusion from pasture</li> <li>noCrop – without exclusion from crop</li> <li>noPop – without reduction from population density</li> <li>noUrban – without exclusion from urban</li> <li>firePlus1pc – tree cover with burnt area was 1% higher.</li> </ul> <p> </p> <p><strong>References</strong></p> <p> </p> <p>1. Dimiceli, C. & Others. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). Preprint at (2015).</p> <p>2. Kelley, D. I. <em>et al.</em> How contemporary bioclimatic and human controls change global fire regimes. <em>Nat. Clim. Chang.</em> <strong>9</strong>, 690–696 (2019).</p> <p>3. Klein Goldewijk, K., Goldewijk, K. K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. <em>Glob. Ecol. Biogeogr.</em> <strong>20</strong>, 73–86 (2010).</p> <p>4. Hurtt, G. C. <em>et al.</em> Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. <em>Climatic Change</em> vol. 109 117–161 Preprint at https://doi.org/10.1007/s10584-011-0153-2 (2011).</p> <p>5. Hantson, S., Arneth, A., Harrison, S. P. & Kelley, D. I. The status and challenge of global fire modelling. (2016).</p> <p>6. Hantson, S. <em>et al.</em> Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. <em>Geoscientific Model Development</em> vol. 13 3299–3318 Preprint at https://doi.org/10.5194/gmd-13-3299-2020 (2020).</p> <p>7. Rabin, S. S., Melton, J. R. & Lasslop, G. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions. <em>Geoscientific Model</em> (2017).</p> <p>8. Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). <em>J. Geophys. Res. Biogeosci.</em> <strong>118</strong>, 317–328 (2013).</p> <p>9. van der Werf, G. R. <em>et al.</em> Global fire emissions estimates during 1997–2016. <em>Earth Syst. Sci. Data</em> <strong>9</strong>, 697–720 (2017).</p> <p>10. Roy, D. P., Boschetti, L., Justice, C. O. & Ju, J. The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product. <em>Remote Sensing of Environment</em> vol. 112 3690–3707 Preprint at https://doi.org/10.1016/j.rse.2008.05.013 (2008).</p> <p>11. Alonso-Canas, I. & Chuvieco, E. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. <em>Remote Sens. Environ.</em> <strong>163</strong>, 140–152 (2015).</p> <p>12. Chuvieco, E. <em>et al.</em> Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. <em>Earth System Science Data</em> vol. 10 2015–2031 Preprint at https://doi.org/10.5194/essd-10-2015-2018 (2018).</p> <p>13. Joyce, R. J., Janowiak, J. E., Arkin, P. A. & Xie, P. CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution. <em>J. Hydrometeorol.</em> <strong>5</strong>, 487–503 (2004).</p> <p>14. Marthews, T. R., Blyth, E. M., Martínez-de la Torre, A. & Veldkamp, T. I. E. A global-scale evaluation of extreme event uncertainty in the eartH2Observe project. <em>Hydrol. Earth Syst. Sci.</em> <strong>24</strong>, 75–92 (2020).</p> <p>15. Harris, I. C. & Jones, P. D. CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018). (2019) doi:10.5285/10D3E3640F004C578403419AAC167D82.</p> <p>16. Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Full Data Monthly Product Version 2018 at 0.5◦: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historical Data. <em>Deutscher Wetterdienst: Offenbach am Main, Germany</em> (2018).</p> <p>17. Beck, H. E., Van Dijk, A. & Levizzani, V. MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. <em>Hydrol. Earth Syst. Sci.</em> (2017).</p> <p>18. Kelley, D. I., Harrison, S. P., Wang, H. & Simard, M. A comprehensive benchmarking system for evaluating global vegetation models. (2013).</p>
Environmental data from Neversink River, White Clay Creek, and Rio Tempisquito watersheds
This dataset presents dissolved stream water chemistry and other environmental variables collected from Neversink River in NY, USA, White Clay Creek in PA, USA, and Rio Tempisquito in Costa Rica.
Sacramento-San Joaquin Bay-Delta Continuous (15 Minute) water quality monitoring data collected by the Continuous Environmental Monitoring Program, DWR, 2005- ongoing.
The Continuous Environmental Monitoring Program (CEMP) plays an instrumental role in overseeing real-time water quality in the Sacramento-San Joaquin Delta (the Delta) and Suisun Bay. The program harnesses wireless telemetry to transmit crucial data to the California Data Exchange Center (CDEC), making high-resolution environmental data pertaining to the Delta and Suisun Bay publicly accessible. The extensive dataset captures information at 15-minute intervals from 15 monitoring stations, utilizing YSI 6600 and YSI EXO sondes to obtain standalone water quality measurements. This extensive dataset informs the operations of the California State Water Project, ensuring it adheres to mandated water quality standards set by Water Right Decision 1641. This data compilation incorporates all information since the transition to YSI multiparameter sondes in 2005. It is important to note that the commencement dates and subsequent upgrades vary between stations, leading to slight discrepancies in the dataset's date ranges. Since its inception in the mid-1980s, CEMP has progressively expanded its monitoring capabilities, consistently augmenting the number of monitoring locations and the array of water quality parameters assessed. Its commitment to utilizing the most advanced water quality monitoring technology reaffirms its position as an environmental monitoring leader in the Delta and Suisun Bay. Today, the program oversees 15 water quality stations that reliably capture data every 15 minutes, each day of the year, transmitting this data in real-time. The core tenents of CEMP: • to obtain consistent and accurate data in real-time at established monitoring stations • to provide data necessary to achieve compliance with salinity, flow, and dissolved oxygen standards • to perform data analyses for further understanding of estuarine ecology • to report information to other government agencies, as well as the public, for the purpose of management and conservation of the upper San F
Data from "Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient"
Synthesized fish, benthic invertebrate, and water quality dataset used for analysis in: Rogers, T., S. Bashevkin, C. Burdi, D. Colombano, P. Dudley, B. Mahardja, L. Mitchell, S. Perry, and P. Saffarinia. 2022. Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient. preprint, EcoEvoRxiv. https://doi.org/10.32942/X2MK5Z
Environmental, community and trait data of small water bodies in Zijin Mountain, Nanjing, Jiangsu, China, 2022
Small water bodies (SWBs) are vulnerable to drought and play a vital role in the conservation of aquatic biodiversity. Currently climate change is intensifying the seasonal drought of SWBs in monsoonal east Asia. However, little is known about the response of benthic macroinvertebrates of small ponds and streams that simultaneously suffer from climate-induced extreme drought. This study aimed to explore the taxonomic and functional response of macroinvertebrates in ponds and streams, either respectively or jointly, to extreme summer drought. We calculated taxonomic and functional diversity indices of communities in 11 streams and 12 ponds across three seasons: spring, summer and winter in 2022. We performed a permutational multivariate analysis of variance, Moran’s eigenvector maps, Moran Spectral Randomization based variation partitioning and convex hull analysis of trait space to examine temporal compositional and functional, as well as trait changes, and the contributions of environmental and spatial factors in shaping communities. The responses of taxonomic and functional diversity in ponds and streams were contrasting during the summer drought. Ponds showed increased taxonomic richness (TR), functional richness (FRic), functional richness (FRed) and trait space volume, while streams experienced decreased TR, FRic and trait space volume but increased FRed. The taxonomic and functional increases of ponds were driven by an influx of generalist taxa from streams, while the increase of FRed in streams resulted from the loss of species with strong dispersal and lentic adaptation traits. Dispersal played a more significant role than environmental filtering in shaping community structure during the drought, especially for streams lacking hydrological connectivity. This study provides the first insights into the complex response of macroinvertebrates to summer drought of SWBs in east Asia monsoonal region. Our results underscore the refuge effect of ponds during summer
Course Materials for Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730)
In today's world, understanding environmental data and making informed decisions based on it is crucial for addressing complex environmental challenges. Yale School of the Environment's Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730) course serves as an introduction to the integration of environmental data using R programming language, coupled with machine learning techniques. This dataset contains a zip file with all the data files used in this course, along with a README that has the metadata for those files.
Gross methane production and consumption estimated for intact soil cores from agricultural plots including environmental covariates and example raw isotope pool dilution data
This study was performed to determine how different soil moistures, soil sources, and agricultural practices affected the gross CH4 fluxes (i.e., rates of methanogenesis) of soils. We extracted intact soil cores from two agricultural sites in the USA in row crop plots under conventional, no-till, and organic management. We then took them to the lab, manipulated their moisture levels, incubated them at room temperature for 22 weeks, and measured gas fluxes at weeks 6 and 21. We developed and utilized a new form of CH4 isotope pool dilution (IPD) to estimate gross CH4 production and consumption fluxes. This new method can measure IPD in a bag headspace that loses volume over time due to sampling. We fit the IPD model to the data and extracted gross CH4 production (P) and consumption (K) constants. These along with calculated fluxes and covariates measured (e.g., moisture, inorganic N) are reported in the main data table.
Stationary camera observations, set, and environmental data from Shark Bay Marine Park, Western Australia from July 2011 to June 2012
This dataset provides information on stationary video cameras set within the study area from 2011 to 2012, including animals viewed along with relevent environmental and camera data. These data provide insight into teleost communities that utilize various habitats within Shark Bay.
Fish trap catch, set, and environmental data from Shark Bay Marine Park, Western Australia from May 2010 to July 2012
This dataset provides information on fish traps set within the study area from 2010 to 2012, including animals caught, relevent environmental and trap data, animal specific measurements and logging of samples retained. Additionally the dataset contains stable isotope values for individuals that were retained for Stable Isotope Analysis in addition to stomach content data. These data provide insight into teleost communities that utilize various habitats within Shark Bay with further insights into their trophic relationships.
Periphyton, hydrological and environmental data in a coastal freshwater wetland (FCE), Florida Everglades National Park, USA (2014-2015)
The characteristic, calcareous periphyton mats of the Everglades, and particularly their diatom assemblages, provide an ideal community to study the patterns and mechanisms of community assembly along environmental gradients with ecotones. Understanding patterns and mechanisms of diatom community assembly along salinity and P gradients can be incorporated into tools for predicting changes in these gradients, and the location and movement of the "white zone" ecotone, caused by saltwater intrusion and water management outcomes in the Southern Everglades. Patterns of environmental variation and periphytic-diatom community structure along the freshwater-marine gradient of Everglades National Park, FL., USA were examined by sampling along a series of 7 transects extending from oligotrophic, freshwater marshes through the ecotone and down to the northern edge of the fringing mangrove forests. Seven transects spanning the west-east extent of the southeast Everglades, from the Main Park Road in the west to the Model Lands in the east, were sampled once in the dry season (May) and once in the wet season (November) of 2014 and 2015. These data are published in "Mazzei and Gaiser. 2018. Diatoms as tools for inferring ecotone boundaries in a coastal freshwater wetland threatened by saltwater intrusion. Ecological Indicators. 88:190-204."
Environmental and periphyton composition data from Biscayne Bay Coastal Wetlands, Florida, USA, July 2022 - November 2022
Environmental and periphyton data were collected from transects in the Biscayne Bay Coastal Wetlands (BBCW) during the wet and dry seasons of 2022 to investigate the rate of carbonate sediment production by periphyton. Environmental data include surface water metrics (pH, salinity, conductivity, and water depth) and soil depths. Periphyton data include nutrient, production, and diatom species composition in samples collected from artificial substrates (periphytometers) placed in the field. Data collection for this project is complete, although the South Florida Management District continues to monitor these transects for a larger ongoing BBCW project.
Diatom composition and environmental data from the Greater Everglades, Florida, USA (2013-2020)
Environmental and diatom data were collected from sites in the Big Cypress National Preserve (BICY) by the South Florida/Caribbean Inventory and Monitoring Network of the National Park Service and from sites in the Everglades Protection Area (EPA) as part of the Monitoring and Assessment Program of the Comprehensive Everglades Restoration Plan. Samples from years 2012, 2013, 2019, 2019, and 2020 are included in this dataset. Environmental data include drier variables that have been found to influence diatom assemblage composition in the greater Everglades ecosystem, including periphyton mat total phosphorus (a proxy for phosphorus in the environment), water column pH, water column conductivity, water depth, days since last dry, and hydroperiod. Diatom data include diatom species composition as percent relative abundances. Code included is pertinent to the methods described in "Robust species optima estimates from non-uniformly sampled environmental gradients" by Solomon et al. 2025, Journal of Paleolimnology.
Periphyton Abundance and Structural Traits, Diatom Taxa Relative Abundance, and Associated Environmental Data from Samples Collected from the Greater Everglades, Florida, USA from September 2005 - ongoing
This data package contains benthic algae (periphyton) and environmental data collected annually during the wet season between 2005 and 2021 from sites distributed throughout the greater Everglades ecosystem. This project is part of the Comprehensive Everglades Restoration Program's Monitoring and Assessment Plan (CERP MAP) intended to document baseline variability in periphyton attributes for assessing the effectiveness of restoration projects. A total of 200 primary sampling units (PSU) of 800 m x 800 m are nested in 32 landscape units (LSU) and each year, random coordinates are 'drawn' within each PSU and one draw is visited in each sampleable PSU. Sampled periphyton is processed for aggregate structural traits (i.e., biomass, chlorophyll-a, organic content, and phosphorus concentration) and for diatom taxa. For diatoms, slides are prepared, and at least 500 frustules are enumerated and identified to the lowest possible taxonomic resolution per slide. Taxon abundances are then relativized to the total count. These data accompany environmental and spatial data for each sampled draw. In addition to the CERP MAP data, this dataset also includes data on the same variables collected from up to 21 primary sampling units in the Broward County Water Preserve Area beginning in 2020. The data in this package replace and supersede those in package knb-lter-fce.1210 (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-fce&identifier=1210).
Bisley daily rainfall (Bisley weekly environmental data)
Data set includes all available daily, weekly, and monthly rainfall from several climate stations in the northeast section of the Luquillo Experimental Forest. These stations are surround the Bisley Experimental watersheds and the Sabana Field Station are are operated by the USFS and the USGS. Weekly canopy throughfall is also collected weekly from the Bisley experimental watersheds. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Potentilla demographic and environmental data for Rocky Mountains of Colorado (Niwot LTER & RMBL), 2018 - 2020.
To understand parent-hybrid dynamics in cinquefoil (Potentilla) species in the Colorado Rocky Mountains, I am estimating environmental overlap among parents and hybrids, interbreeding among parents and hybrids, and hybrid population growth in multiple natural populations at NWT and the Rocky Mountain Biological Laboratory (RMBL). This data was collected to test broad hypotheses about hybrid-parent dynamics in changing montane environments.
Pond environmental and taxonomic data for Niwot Ridge and Green Lakes Valley, 2021 - ongoing.
This is a summary of basic environmental data and benthic macroinvertebrates from water in ponds in the vicinity of the Niwot Ridge LTER. Ponds were selected across a range of elevations, sizes, and positions relative to glacial, stream, and lake water sources. Ponds sampled occurred on Niwot Ridge and throughout the Green Lakes Valley.
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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.