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226 results for “long term monitoring”
Baltimore Ecosystem Study: Long-Term Monitoring of Riparian Water Table Depth and Groundwater Chemistry
Long-term monitoring of riparian water tables and groundwater chemistry began in 2000 along four first or second order steams in and around the Gwynns Falls watershed in Baltimore City and County, MD. One site (Oregon Ridge) is in the completely forested Pond Branch catchment that serves as a ""reference"" study area for the Baltimore LTER (BES). Two sites (Glyndon, Gwynbrook) were in suburban areas of the watershed; one just upstream from the Glyndon BES long-term stream monitoring site in the headwaters of the Gwynns Falls, and one along a tributary that enters the Gwynns Falls just above the Gwynnbrook BES long-term stream monitoring site farther downstream. The final, urban site (Cahill) was along a tributary to the Gwynns Falls in Leakin Park in the urban core of the watershed. Water table data and more detailed descriptions of soils, vegetation, stream channel properties and microbial processes at these sites can be found in Groffman et al. (2002, Environmental Science and Technology 36:4547-4552) and Gift et al. (2010, Restoration Ecology 18:113-120).
Long-term monitoring of wet, dry, and bulk atmospheric deposition in central Arizona-Phoenix, ongoing since 1999
The aims of this study are to examine (1) the magnitude and spatial variability in the concentration and flux of wet deposited major ions (NO3-N, NH4-N, DOC, PO4-P, Cl, SO4, H+, Ca, Mg, Na, K) across the greater Phoenix metropolitan area, including the developed urban core and outlying desert, and (2) patterns of coarse dry particulate deposition across stated area and provide some minimum estimates on levels of dry deposition of these ions. This study was designed particularly to answer the question: 'To what extent are concentrations and fluxes of these ions enhanced at sites within the urban core relative to undeveloped desert sites upwind and downwind of the city?'. At the outset, the project featured eight wet-dry collectors positioned spatially so as to form a transect running approximately west-to-east across the central Arizona region from outlying desert to the west, upwind of the prevailing synoptic wind direction, through agriculture to urban core sites, and, finally, to two downwind sites in the desert to the east and northeast. As much as possible, these collectors were co-located with Maricopa County or Arizona Department of Environmental Quality monitoring stations. Monitoring at most sampling locations ran from 1999 through the mid-2000s when sampling was discontinued at several sites. Sampling continued at the Lost Dutchman State Park, also a Desert Fertilization experiment site with a focus on atmospheric deposition, through 2016. Sampling continues at a site on the Arizona State University Tempe campus that was added to the program in 2009.
Long-term monitoring of macroinvertebrates in Sycamore Creek, Arizona, USA (2010-2019)
## overview The primary objective of this project is to understand how long-term climate variability and change influence the structure and function of desert streams via effects on hydrologic disturbance regimes. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Researchers try to (1) determine how climate variability and change over multiple years influence stream biogeomorphic structure (i.e., prevalence and persistence of wetland and gravel-bed ecosystem states) via their influence on factors that control vegetation biomass, and (2) compare interannual variability in within-year successional patterns in ecosystem processes and community structure of primary producers and consumers of two contrasting reach types (wetland and gravel-bed stream reaches). ## research objectives This dataset was collected to understand two questions: (1) how does inter- and intra-annual variability and directional change in winter precipitation and hence streamflow extremes influence macroinvertebrate community structure, and (2) how do these patterns differ in wetland- and gravel-dominated reaches.
Long-term monitoring of streamwater chemistry in Sycamore Creek, Arizona, USA (2010-2014)
The primary objective of this project is to understand how long-term climate variability and change influence the structure and function of desert streams via effects on hydrologic disturbance regimes. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Researchers try to (1) determine how climate variability and change over multiple years influence stream biogeomorphic structure (i.e., prevalence and persistence of wetland and gravel-bed ecosystem states) via their influence on factors that control vegetation biomass, and (2) compare interannual variability in within-year successional patterns in ecosystem processes and community structure of primary producers and consumers of two contrasting reach types (wetland and gravel-bed stream reaches). This specific dataset was collected to monitor long-term changes in dissolved nutrient concentrations (N, P, C) by sampling surface water within gravel and wetland dominated reaches during baseflow.
Long-term monitoring of floodwater chemistry in Sycamore Creek, Arizona, USA (2010-2021)
The primary objective of this project is to understand how long-term climate variability and change influence the structure and function of desert streams via effects on hydrologic disturbance regimes. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Researchers try to (1) determine how climate variability and change over multiple years influence stream biogeomorphic structure (i.e., prevalence and persistence of wetland and gravel-bed ecosystem states) via their influence on factors that control vegetation biomass, and (2) compare interannual variability in within-year successional patterns in ecosystem processes and community structure of primary producers and consumers of two contrasting reach types (wetland and gravel-bed stream reaches). These data were collected to understand how climate change alters flood-mediated delivery of the limiting resource, nitrogen. Specifically, how does the amount of winter rainfall and the number, timing, and intensity of winter and monsoon floods alter N delivery. Previous research indicates that nitrogen is a limiting element in Sycamore Creek, and that pulses of nitrogen enter the system from the landscape during winter rains and summer monsoons. Nitrogen in high concentrations can be a pollutant so consideration of downstream export is a consideration. Researchers collected water samples during storms to compare inter- and inter-annual variability in storm dynamics, and to examine the pulse of various nutrients associated with these events.
Long-term water quality monitoring in the Altamaha, Doboy and Sapelo sounds and the Duplin River near Sapelo Island, Georgia from May 2001 to August 2009
Water samples were collected on Georgia Coastal Ecosystems LTER oceanographic monitoring cuirses approximately every three months from May 2001 through December 2006 and monthly from November 2006 to August 2009. Concentrations of dissolved nutrients (ammonium, nitrate+nitrite, phosphate), dissolved organics (DOC, DON, DOP), particulate organic carbon and nitrogen, total suspended sediment, and total particulate iron and phosphorus were measured using standard analytical methods. The concentrations of six elements (calcium, potassium, magnesium, sodium, silicon, and strontium) were also determined occasionally using elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS).
Long-term water quality monitoring on the Altamaha River and major tributaries from September 2000 through April 2009
Water samples were collected from the Altamaha River (approximately weekly) and several tributaries (bimonthly) from September 2000 through September 2001. Samples were then collected at less frequent intervals from September 2001 through April 2009. The concentration of dissolved nutrients (ammonium, nitrate+nitrite, phosphate, silicate), dissolved organics (DOC, DON, DOP) and total suspended solids were measured using standard methods. The concentrations of 20 elements (Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, Si, Sr and Zn) were also determined using elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS). Total dissolved inorganic carbon (DIC) was measured using a custom automated DIC analyzer. Total alkalinity (TA)was determined by Gran titration and pH of surface water was measured using a glass electrode.
Long-term monitoring of the fish community in the Minho Estuary (NW Iberian Peninsula)
<p>The dataset contains data from fyke nets deployed in the Minho Estuary (Portugal) from 2010 to 2019. The fyke nets were used for fish sampling and data collection. The sampling frequency varied but, on average, data was collected weekly using five different fyke nets. However, due to technical issues (e.g. lost or damaged fyke nets), the sampling pattern is not constant, with some fyke nets staying underwater for shorter or longer periods, and occasionally having fewer than five fyke nets per parentEventID. The dataset includes various terms such as parentEventID, eventID, eventDate, year, startDayOfYear, endDayOfYear, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, DEIMS.iD, habitat, basisOfRecord, samplingProtocol, sampleSizeValue, sampleSizeUnit, samplingEffort, occurrenceStatus, occurrenceID, organismQuantity, organismQuantityType, degreeOfEstablishment, vernacularName, scientificName, acceptedNameUsageID, taxonRank, kingdom, phylum, order, family, genus, and scientificNameAuthorship.</p>
Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)
<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. <em>International Journal of Applied Earth Observation and Geoinformation </em>133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> <p>Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.</p> <p>The first dataset (<strong>Dataset_1.gpkg</strong>) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>CLASS</em>": EUNIS first level habitat type, classified as following:<br> <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"<em>DATE</em>": Date associated with EUNIS habitat sample</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>TYPE</em>": Either training ("<em>train</em>") or validation ("<em>test</em>") sample</li> </ul> <p>The second dataset (<strong>Dataset_2.gpkg</strong>) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>REFERENCE</em>": Change class reference</li> <li>"<em>CCDC</em>": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"<em>PCCD</em>": Change class obtained from the Post-Classification Change Detection approach</li> </ul> <p>Supplementary layout files (<strong>Dataset_1.qml</strong> and <strong>Dataset_2.qml</strong>) support formatting of the points in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># EUNIS habitat</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. </p> <p>Habitat maps are given for the two approaches in years 1984 and 2022:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_HABITAT_1984.tif</strong> and <strong>CCDC_HABITAT_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_HABITAT_1984.tif </strong>and <strong>PCCD_HABITAT_2022.tif</strong>)</li> </ul> <p>Supplementary layout files (<strong>CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml</strong>) support formatting of raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># Change detection during the 1984-2022 period</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.</p> <p>Change detection maps are given for the two approaches:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_CHANGE_1984_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_CHANGE_1984_2022.tif</strong>)</li> </ul> <p>Supplementary layer files (<strong>CCDC_CHANGE_1984_2022.qml</strong> and<strong> PCCD_CHANGE_1984_2022.qml</strong>) support formatting of the raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># GEE repository</h3> <p>To get direct access to GEE scripts and assets, please follow those two links:</p> <p>https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin</p> <p>https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin</p>
PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing
<p>The urban population continues to grow despite health risks associated with densely populated cities, such as traffic congestion and air pollution. At the same time cities are also further heating up due to climate change. Environmental monitoring is increasingly critical to react quickly to temporarily increased concentrations of, for example, carbon monoxide, nitrogen oxides, ozone, and particulate matter. <br>We introduce a significantly improved version of our PhytoNode, an energy-efficient sensor node designed for phytosensing, that is, using of plants as environmental sensors. We aim for a scalable and sustainable real-time monitoring solution following our vision of an `intelligent plant' as an inexpensive and accurate sensor node. <br>We measure electrical potentials and leaf temperatures of plants to assess their well-being and, in turn, environmental conditions. <br>The PhytoNode achieves long-term energy autonomy by harvesting energy via solar cells and shares data via Bluetooth Low Energy (BLE) communication. We process the gathered time series plant data onboard in real-time using methods of Machine Learning (ML) to analyze the plant's activity and to detect dangerous concentrations of gases. In a few showcasing experiments, we demonstrate the feasibility of both our hardware and software approach for continuous, long-term environmental monitoring based on phytosensing. By embedding engineered devices in living plants as a `plant wearable' that listens to plant responses, we hope to help pushing towards smarter future cities and healthier urban environments. </p> <p> </p> <p>Data repository for our paper "PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing", submitted to the 8th Future of Information and Communication Conference 2025 (FICC 2025). Please refer to the paper for more information.</p>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</p>
Monitoring long-term peat subsidence with subsidence platens in Zegveld, The Netherlands
<p><span>Peat oxidation in peat meadow areas is causing greenhouse gas emissions as well as land subsidence. Due to yearly fluctuations in soil surface level, long-term monitoring is needed to determine long-term net subsidence rates. In the experimental peat-meadow farm at Zegveld (NL) subsidence platens were installed in 1970 in a field with low ditchwater level, and in 1973 in a field with high ditchwater level. Platens were installed at 7 different depths, allowing to investigate where in the peat profile subsidence occurs. Elevation of platens as well as soil surface has been measured with surveyor’s levelling each year at the end of winter, so that a long timeseries up to 2023 is available. Analysis showed that surface level in the field with high ditchwater level subsided by 23 cm in 50 years (4.6 mm/yr), while in the field with low ditchwater level this was 31 cm in 53 years (5.8 mm/yr). Results also showed that in the field with low ditch water level, most subsidence due to permanent shrinkage and peat oxidation occurred between 40 and 100 cm depth, while for the other field this was between 20 and 40 cm depth. Finally, in 2023 subsidence was still observed under continuously saturated conditions at 140 cm depth. Presumably, in the aerated part of the profile peat oxidation and the associated earthification process is the main cause of subsidence, while the observed subsidence in the saturated soil at 140 cm depth must be due to other processes, such as consolidation and creep.</span></p>
IRIDIA-AF, a large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database
<h2>Abstract</h2><p>Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing. Training these models rely on a few open and annotated databases. We present a new Holter monitoring database from patients with paroxysmal AF with 167 records from 152 patients, acquired from an outpatient cardiology clinic from 2006 to 2017 in Belgium. AF episodes were manually annotated and reviewed by an expert cardiologist and a specialist cardiac nurse. Records last from 19 hours up to 95 hours, divided into 24-hour files. In total, it represents 24 million seconds of annotated Holter monitoring, sampled at 200 Hz. This dataset aims at expanding the available options for researchers and offers a valuable resource for advancing ML and DL use in the field of cardiac arrhythmia diagnosis.</p><h2>Article</h2><p><a href="https://www.nature.com/articles/s41597-023-02621-1">https://www.nature.com/articles/s41597-023-02621-1</a></p><h2>Repository</h2><p><a href="https://github.com/cedricgilon/iridia-af">https://github.com/cedricgilon/iridia-af</a></p><h2>Versions history</h2><ul><li>2023-10-04: v1.0.1 – remove hidden files from .zip archive</li><li>2023-07-26: v1.0.0 – initial release</li></ul><h2>Keywords</h2><p>Paroxysmal Atrial Fibrillation, AF, long-term electrocardiogram, ECG, Holter monitoring, Database, Dataset, IRIDIA, IRIDIA-AF</p>
FISHPASS ASSESSMENT PLAN LONG-TERM MONITORING OF HYDROLOGIC AND WATER QUALITY DATA
The Great Lakes Fishery Commissions’ (GLFC) FishPass project seeks to reconnect the waterscape for only desired species (i.e., selective passage) by integrating a multitude of existing and novel passage techniques and technologies. The probability of a fish passing through a sorting system is dependent on environmental conditions and a fish’s motivation ─ its internal state in relation to environmental stimuli. While fish decision making abilities introduce complexity to the sorting operations, they also provide an opportunity to exploit behavioral tendencies and abilities to achieve selective sorting. The FishPass Assessment Plan details a monitoring program aimed at quantifying fish movement and sorting capabilities associated with both individual mechanisms and integrated sorting systems. The results of the monitoring program will be used to inform future adjustments to the selection of techniques and technologies and their configuration to optimize passage of desirable species while blocking and/or removing undesirable species. A key component to the Assessment Plan is the long-term monitoring of abiotic variables in and around FishPass. This data set contains the hydrologic (e.g., river discharge, water level) and water quality data (e.g., temperature, specific conductivity, conductivity, and turbidity) collected at mostly static stations throughout the Boardman/Ottaway River. The dataset is updated annually. These data are collected until the initiation and/or substantial completion of the FishPass structure. Collection of this type of data are expected to continue after FishPass construction completion but modifications to the extent and location of monitoring stations are anticipated. As a result, a new dataset will be updated in the future containing all long term hydrologic and water quality monitoring post construction. R. Swanson, GLFC Assessment Biologist, is primarily responsible for maintaining the monitoring equipment, data retrieval, quality assuran
Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.
SGS-LTER Long-Term Monitoring Project: Vegetation Cover on Small Mammal Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1999 -2006, ARS Study Number 118 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/326/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/140/17. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83458. The abundance and diversity of small mammals in shortgrass steppe is strongly influenced by the structure and composition of vegetation. Vegetation structure provides cover from predators and harsh abiotic conditions. Plant species composition affects the types of seeds and herbaceous material available to granivores and herbivores, and influences arthropod populations, which are important prey for the omnivorous species that dominate in shortgrass steppe. Both vegetation structure and plant community composition are sensitive to the availability of precipitation as well as the activity of large mammalian herbivores. In 1999, we began measuring vegetation structure and p
SGS-LTER Long-term Monitoring Project: Spotlight Rabbit Count on the Central Plains Experimental Range, Nunn, Colorado, USA 1994-2006, ARS Study Number 98 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/327/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/136/17. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83448. Rabbits are the most important small-mammal herbivores in shortgrass steppe, and may significant influence the physiognomy and population dynamics of herbaceous plants and woody shrubs. Rabbits also are the most important prey of mammalian carnivores such as coyotes and large raptors such as golden eagles and great horned owls. Two hares (Lepus californicus, L. townsendii) and one cottontail rabbit (Sylvilagus audubonii) occur in shortgrass steppe. In 1994, we initiated long-term studies to track changes in relative abundance of rabbits on the Central Plains Experimental Range (CPER). On four nights each year (one night each season, usually on new moon nights in January,
SGS-LTER Long-Term Monitoring Project: Small Mammals on Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1994 -2006, ARS Study Number 118 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/329/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/137/17. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83452. Small mammals (rabbits, rodents) are integral components of semiarid ecosystems because of their roles as consumers of plants, seeds and arthropods, as soil disturbance agents, and as food for raptors, snakes and mammalian carnivores. Because of their vagility and intermediate trophic position, populations of small mammals may track changes in vegetation and the abiotic environment that may result from shifts in land-use and other anthropogenic disturbances. However, these populations are variable over space and time, and their response to environmental changes may not be immediately apparent given their behavioral flexibility and relatively long life-spans and generatio
Throw trap and electrofishing data collected during 1996–2022 from the Everglades, Florida, United States for the publication "Contrasting invasion histories and effects of three non-native fishes observed with long-term monitoring data"
This dataset was used to analyze the effects of three non-native fishes in the Florida Everglades for a publication in the journal Biological Invasions. The dataset incorporates plot-level mean densities (# of individuals per square meter) of common aquatic animals collected during 1996–2022 from 17 sites across three regions of the Everglades: Taylor Slough, Shark River Slough, and Water Conservation Area 3A. Prey species included are nine common small fishes and three common decapod species (two crayfish species and grass shrimp). The dataset includes throw trap data on three predator taxa: African Jewelfish (Hemichromis letourneuxi), Mayan Cichlids (Mayaheros uruphthalmus), and sunfishes (Lepomis spp.). Annual indices of mean wet season electrofishing catch-per-unit-effort of Asian Swamp Eels (Monopterus albus/javanesis), Mayan Cichlids, sunfishes, and the three other large 'top predator' fishes (Amia calva, Lepisosteus platyrhincus, Micropterus salmoides) are included for plots where electrofishing was performed from 1997-2021. Hydrologic measures used in analyses and R code used to conduct analyses are also included.
Sonadora elevational plots: long-term monitoring of air temperature
This is a long term monitoring of air temperature at each elevation plot along the Sonadora gradient. At each plot a HOBO sensor is located close to the middle of the plot: at 1m above ground: and placed inside a radiation shield (a plastic cup). Sensors are programmed to sample and store air temperature every hour. A daily average is computed from hourly readings. Sensors are downloaded twice a year: thus blanks represent sensor malfunction: loss of battery: or memory full. Initial blanks were due to lack of enough sensors to cover the gradient. 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.
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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.