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6,569 results for “Long-term”
Weather Data, 1940-2016, Adirondack Long-Term Ecological Monitoring Program by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York, USA
These datasets include information collected from 1940 to 2016 in or around Huntington Wildlife Forest at the Adirondack Ecological Center in Newcomb, New York. Data were collected daily and include minimum and maximum daily temperature, precipitation, snowfall, and sometimes additional measurements (e.g., wind direction, weather events). These data were collected as part of the Adirondack Long Term Ecological Monitoring Program (ALTEMP).
Upper Midwest Long-Term Monitoring (UMWLTM) Program
The Upper Midwest Long-term Monitoring Program lakes were initially part of a larger EPA-funded project (http://www.epa.gov/airmarkets/assessments/TIMELTM.html) to investigate trends in the acid-base chemistry of lakes in sensitive regions of the US. At the start of the project in 1983, the UMW lakes included lakes in Voyageurs National Park in Minnesota, north-central Wisconsin, and the upper peninsula of Michigan. The lakes represent low alkalinity systems considered most sensitive to acid deposition, which increased from east to west across the 3-state region. Lakes in Wisconsin and Michigan were primarily seepage lakes. Over time the sampling frequency declined (from spring, summer and autumn to only autumn) and lakes were dropped from the initial set of lakes due to funding constraints. The longest period of record is from 1983 to 2000.
Long-term Intersite Decomposition Team (LIDET) Plant Litter Data at the Sevilleta National Wildlife Refuge, New Mexico (1990-2001)
Sevilleta data from a study testing the degree to which substrate quality and macroclimate control the carbon and nitrogen dynamics of decomposing leaf, wood, and fine-root litter in a 10-year, 28 site (17 LTER) team experiment. http://andrewsforest.oregonstate.edu/data/abstract.cfm?dbcode=TD023&topnav=97
Figure 1 from: Nair P, Huertas M, Nowlin WH (2020) Metabolic responses to long-term food deprivation in subterranean and surface amphipods. Subterranean Biology 33: 1-15. https://doi.org/10.3897/subtbiol.33.48483
Figure 1 Oxygen consumption in darkness for Stygobromus pecki and Synurella at 23 °C. Values are means ± Standard Error Means (SEM) for n = 5 animals.
Figure 2 from: Nair P, Huertas M, Nowlin WH (2020) Metabolic responses to long-term food deprivation in subterranean and surface amphipods. Subterranean Biology 33: 1-15. https://doi.org/10.3897/subtbiol.33.48483
Figure 2 Changes in the levels of body metabolites in Stygobromus pecki and Synurella sp. A Carbohydrates B proteins C lipids concentrations during long-term food deprivation at 23 °C in darkness. Values are means ± SEM for n = 5 replicates. (*) indicates significance at P < 0.05 for the main effects of Treatment, Time and the Time × Treatment interaction.
Display of the Crab long-term (15 year time span) spectrum extracted from a sample of 50 randomly selected ScWs.
<p><a href="https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&instrument=isgri&osa_version=OSA10.2&radius=15&E1_keV=20&E2_keV=40&product_type=isgri_spectrum&catalog_selected_objects=1,2,3,4&selected_catalog=%7B%22cat_column_descr%22:%5B%5B%22meta_ID%22,%22%3Ci8%22%5D,%5B%22src_names%22,%22%7CS18%22%5D,%5B%22significance%22,%22%3Ef4%22%5D,%5B%22ra%22,%22%3Ef4%22%5D,%5B%22dec%22,%22%3Ef4%22%5D,%5B%22NEW_SOURCE%22,%22%3Ei2%22%5D,%5B%22ISGRI_FLAG%22,%22%3Ci8%22%5D,%5B%22FLAG%22,%22%3Ci8%22%5D,%5B%22ERR_RAD%22,%22%3Cf8%22%5D%5D,%22cat_column_list%22:%5B%5B1,2,3,4%5D,%5B%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22%5D,%5B116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477%5D,%5B84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844%5D,%5B26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285%5D,%5B-32768,-32768,-32768,-32768%5D,%5B2,2,2,2%5D,%5B0,0,0,0%5D,%5B0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203%5D%5D,%22cat_column_names%22:%5B%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22%5D,%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22%7D">https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&instrument=isgri&osa_version=OSA10.2&radius=15&E1_keV=20&E2_keV=40&product_type=isgri_spectrum&catalog_selected_objects=1,2,3,4&selected_catalog={%22cat_column_descr%22:[[%22meta_ID%22,%22%3Ci8%22],[%22src_names%22,%22|S18%22],[%22significance%22,%22%3Ef4%22],[%22ra%22,%22%3Ef4%22],[%22dec%22,%22%3Ef4%22],[%22NEW_SOURCE%22,%22%3Ei2%22],[%22ISGRI_FLAG%22,%22%3Ci8%22],[%22FLAG%22,%22%3Ci8%22],[%22ERR_RAD%22,%22%3Cf8%22]],%22cat_column_list%22:[[1,2,3,4],[%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22],[116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477],[84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844],[26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285],[-32768,-32768,-32768,-32768],[2,2,2,2],[0,0,0,0],[0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203]],%22cat_column_names%22:[%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22],%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22}</a></p>
Crab long-term (15 year time span) lightcurve extracted froma sample of 50 randomly selected ScWs in 30-100 keV band, usingOSA10.2.
<p><a href="https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&E1_keV=30&E2_keV=100&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&catalog_selected_objects=1,2,3,4&detection_threshold=7.5&instrument=isgri&osa_version=OSA10.2&product_type=isgri_lc&time_bin=10000&time_bin_format=sec&query_status=ready&query_type=Real&radius=15&use_scws=no&selected_catalog=%7B%22cat_column_descr%22:%5B%5B%22meta_ID%22,%22%3Ci8%22%5D,%5B%22src_names%22,%22%7CS18%22%5D,%5B%22significance%22,%22%3Ef4%22%5D,%5B%22ra%22,%22%3Ef4%22%5D,%5B%22dec%22,%22%3Ef4%22%5D,%5B%22NEW_SOURCE%22,%22%3Ei2%22%5D,%5B%22ISGRI_FLAG%22,%22%3Ci8%22%5D,%5B%22FLAG%22,%22%3Ci8%22%5D,%5B%22ERR_RAD%22,%22%3Cf8%22%5D%5D,%22cat_column_list%22:%5B%5B1,2,3,4%5D,%5B%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22%5D,%5B116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477%5D,%5B84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844%5D,%5B26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285%5D,%5B-32768,-32768,-32768,-32768%5D,%5B2,2,2,2%5D,%5B0,0,0,0%5D,%5B0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203%5D%5D,%22cat_column_names%22:%5B%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22%5D,%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22%7D">https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&E1_keV=30&E2_keV=100&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&catalog_selected_objects=1,2,3,4&detection_threshold=7.5&instrument=isgri&osa_version=OSA10.2&product_type=isgri_lc&time_bin=10000&time_bin_format=sec&query_status=ready&query_type=Real&radius=15&use_scws=no&selected_catalog={%22cat_column_descr%22:[[%22meta_ID%22,%22%3Ci8%22],[%22src_names%22,%22|S18%22],[%22significance%22,%22%3Ef4%22],[%22ra%22,%22%3Ef4%22],[%22dec%22,%22%3Ef4%22],[%22NEW_SOURCE%22,%22%3Ei2%22],[%22ISGRI_FLAG%22,%22%3Ci8%22],[%22FLAG%22,%22%3Ci8%22],[%22ERR_RAD%22,%22%3Cf8%22]],%22cat_column_list%22:[[1,2,3,4],[%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22],[116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477],[84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844],[26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285],[-32768,-32768,-32768,-32768],[2,2,2,2],[0,0,0,0],[0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203]],%22cat_column_names%22:[%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22],%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22}</a></p>
Long-term data from field erosion plot studies in eastern Austria
<p>The data include measured data for three different soil tillage practices from three experimental sites in eastern Austria. The Tables 1-3 include for each site and each year the period of operation, the planted crop, the annual precipitation, the surface runoff in mm, the soil loss in t/ha and the surface losses of total nitrogen, total phosphorus and soil organic carbon in kg/ha. Crop yields were determined from 45 m² large plots with one or two replications. Yields in kg/ha are not available for all years, but relative yield in % are.</p> <p>The experiments were conducted at three sites in eastern Austria: in Mistelbach, Pixendorf and Pyhra. The soils in Mistelbach and Pyhra are classified as Typic Argiudolls while the soil in Pixendorf is an Entic Hapludoll. Soil textures range from silt loam to loam. Average annual rainfall (1994-2018) at the sites amounts from 621 to 916 mm with average annual air temperatures between 9.4 and 10.4 °C.</p> <p>Following soil tillage treatments were investigated: (1) conventional tillage system with ploughing in fall (CT), (2) mulch tillage with cover crops during winter (MT) and (3) no-till with cover crops during winter (NT). The study design was a randomized block and each treatment was replicated twice. Each treatment was equipped with one field runoff plot. The experimental plots were 6 m wide and – depending on site conditions - between 40 and 80 m long. The study design consisted of 3 (Mistelbach) and 4 m wide (Pixendorf, Pyhra) and 15 m long runoff plots for each management variation. Each plot was bordered by stainless steel metal sheets. At the lower end of the plot surface runoff and soil loss were collected in a trough and then diverted by a 100-mm PVC pipe to an Automated Erosion Wheel (AEW). The design of this AEW is similar to a tipping bucket and consists of four equal sections of five liters resulting in a resolution of each tip of 0.08 mm for 60 m² plots. A magnetic sensor system was used for continuous runoff measurement.</p> <p>Soil-water-suspension was divided by an adapted multi-tube divisor taking 3.3% of the sample that is collected in a 60 L collection tank. After each erosive storm the collection tank was emptied and the runoff sample was brought to the laboratory, weighed and dried until constant mass was achieved to determine sediment concentration. Based on the continuously measured runoff data from the data logging system and the sediment concentration the amount of soil loss from the plots was calculated for each erosive event.</p> <p>Throughout the investigation period soil erosion, surface runoff and nutrient and carbon losses and partly also pesticide losses due to erosion processes were determined for all sites and tillage systems. Immediately after planting/seeding of summer crops the erosion plots were installed and then operated until harvest. After the harvest the equipment was removed. No measurements were performed during winter due to limited accessibility and frost damages to the equipment. At each site an automatic tipping bucket rain gauge was placed to measure rainfall in 5-min intervals.</p> <p>Crop yield was determined from each treatment with three replications.</p> <p>Total nitrogen and total carbon in sediment samples was analysed by dry combustion [3](OeNorm L1080, 1989) using a C/N Analyzer (Vario Max CN, Elementar). Soil organic carbon content was obtained by subtracting inorganic carbon content measured volumetrically by the Scheibler method with a Calcimeter (OeNorm L1084, 1989). Total phosphorus of the sediment was determined using a UV/VIS spectral photometer (DU-640 Beckmann).</p> <p>Pesticides were extracted from water by solid phase extraction and from sediments using distilled water or organic solvent [6]. After shaking for several hours and centrifugation, the sample passed through a solid phase extraction. After evaporation of the extract the pesticide residues were redissolved in another solvent and analysed with high performance liquid chromatography (HPLC).</p>
Figure 2 in Compositionı characteristics and long-term variability of the freshwater microcrustacean fauna of the Faroe Islands
Figure 2. CCA of the microcrustacean communities in the surveyed water bodies on the Faroe Islands.
Fig. 5 in Spatial and temporal distribution of Gymnorhamphichthys rondoni (Gymnotiformes: Rhamphichthyidae) in a long-term study of an Amazonian terra firme stream, Leticia - Colombia
Fig. 5. Heat map showing the probability of finding Gymnorhamphichthys rondoni individuals within any quadrant of the grid located in a terra firme stream in Leticia, Amazonas. White represents zero probability. Dark blue represents the lowest probability for quadrants in which G. rondoni rested during the day (1.45%) while red represents the highest probability (36.2%) of finding an individual resting within a given quadrant. Notice that individuals were never found in the white quadrants, which dried out during low water periods.
Fig. 4 in Spatial and temporal distribution of Gymnorhamphichthys rondoni (Gymnotiformes: Rhamphichthyidae) in a long-term study of an Amazonian terra firme stream, Leticia - Colombia
Fig. 4. Monthly distribution of Gymnorhamphichthys rondoni within the grid in the Yahuarcaca terra firme stream in Leticia, Amazonas. The continuous line represents the maximum stream width reached during high water months, and the dashed line the minimum stream width reached in low water months. a. to m. Each sketch represents a sampling month and each number represents the total number of G. rondoni individuals found during five to six days at each quadrant for that particular month.
Fig. 3 in Spatial and temporal distribution of Gymnorhamphichthys rondoni (Gymnotiformes: Rhamphichthyidae) in a long-term study of an Amazonian terra firme stream, Leticia - Colombia
Fig. 3. The EOD pulse in the weakly electric fish Gymnorhamphichthys rondoni. EOD waveform shows four distinctive phases of alternating polarity (Left). Pictures are showing the lateral, dorsal and ventral view of G. rondoni ICN-MHN 6169 (Right). © Institute of Natural Sciences, Faculty of Sciences, Universidad Nacional de Colombia.
Fig. 6 in Spatial and temporal distribution of Gymnorhamphichthys rondoni (Gymnotiformes: Rhamphichthyidae) in a long-term study of an Amazonian terra firme stream, Leticia - Colombia
Fig. 6. Water conductivity and its effects on distance to nearest neighbors and the daily ratio of departure from a random distribution (R). a. The nearest neighbor distance between all individuals of Gymnorhamphichthys rondoni found within the grid for all sampling days during all thirteen months (linear regression, n= 1375; p= 0.45; r2= 0.0004). The daily ratio of departure from a random distribution (R) within the grid for all sampling days during all thirteen months for b. Exponential Model (r2 = 0.09; F = 6.4; p = 0.014) and c. Power Model (r2 = 0.07; F = (1, 62) (1, 62) 4.9; p = 0.03).
Fig. 2 in Spatial and temporal distribution of Gymnorhamphichthys rondoni (Gymnotiformes: Rhamphichthyidae) in a long-term study of an Amazonian terra firme stream, Leticia - Colombia
Fig. 2. Sampling site in the Yahuarcaca terra firme stream in Leticia, Amazonas. a. Image during low water levels. b. Image with high water levels after a local rain.
Fig. 1 in Spatial and temporal distribution of Gymnorhamphichthys rondoni (Gymnotiformes: Rhamphichthyidae) in a long-term study of an Amazonian terra firme stream, Leticia - Colombia
Fig. 1. Geographical position of the sampling site in the Yahuarcaca terra firme stream close to Leticia, Amazonas in Colombia. The picture shows a panoramic view of the sampling site.
Predicting the Long-Term Stability of Compact Multi-Planet Systems
<p>REBOUND N-body simulation archives used to train and test the machine learning models in Tamayo et al. (2020), 'Predicting the Long-Term Stability of Compact Multi-Planet Systems'. See https://github.com/dtamayo/spock for scripts to process the data, train and test the SPOCK XGBoost model.</p>
phytoplankton dataset: Marine diatoms in a warming world: phospholipidome model experiments and long-term field analysis
<p>Phytoplankton data set for Marine diatoms in a warming world: phospholipidome model experiments and long-term field analysis. The data set includes abundances of total phytoplankton and of Chaetoceros pseudocurvisetus.</p>
A dataset of long-term consistency values of resting-state fMRI connectivity maps in a single individual derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol
<p>This dataset contains preprocessed resting state fMRI data (.nii.gz) with accompanying confound files (.tsv) from the Single Individual volunteer for Multiple Observations across Networks (SIMON; http://fcon_1000.projects.nitrc.org/indi/retro/SIMON.html) dataset that has been minimally preprocessed using the NeuroImaging Analysis Kit (NIAK; http://niak.simexp-lab.org/build/html/PREPROCESSING.html). Preprocessing steps included: (1) Slice timing correction; (2) Estimation of rigid-body motion in fMRI runs, both within- and between sessions; (3) Linear or non-linear coregistration of the structural scan in stereotaxic space; (4) Individual coregistration between structural and functional scans; (5) Resampling of functional scans in stereotaxic space. Confound files (.tsv) have been included for purposes of scrubbing and regression of confounds using NIAK or other analysis software, allowing for further processing without conflicts.</p>
Fig. 2 in Skeleton of a Cretaceous mammal from Madagascar reflects long-term insularity
Fig. 2 | Cranium, lower jaw and dentition of A. hui holotype (UA 9030).
Data: Interacting effects of short-term and long-term noise exposure on anti-predator behaviour: A field experiment with sand gobies
<p>Data on anti-predator behaviour of sand gobies during trials with exposure to either boat noise or a silent control. Gobies were living in a range of quiet to noisy habitats.</p>
ScienceDex guides
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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.