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225 results for “Environmental variability”
Pulse amplitude modulated (PAM) 5-minute chlorophyll fluorescence (ChlF) with accompanying environmental variables from the GCE-LTER Keenan Field site on Sapelo Island, GA in July 2020
Pulse amplitude modulated (PAM) chlorophyll fluorescence (ChlF) from July 11, 2020 to July 27, 2020 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River. PAM ChlF were processed in WinControl-3.25. Additional biophysical variables included are photosynthetically active radiation (PAR) from onsite quantum sensors (Licor-192), and tide height from an onsite pressure transducer (Hobo U20).
Greenhouse gas partial pressure (CO2, CH4, N2O) and environmental variables (physical, chemical, and biological) measured in urban ponds of Barcelona during summer and winter (2023-2024)
This dataset provides information on the partial pressure of greenhouse gases (CO₂, CH₄, and N₂O) measured in 41 artificial urban ponds—28 naturalized and 13 non-naturalized—using the headspace technique. Additionally, GPS coordinates, as well as physical, chemical, and biological variables for each pond, are included. Data were collected during the summer and winter seasons, during daytime. Furthermore, a subset of 16 ponds (8 naturalized and 8 non-naturalized) was also sampled at night in both seasons. All samples were taken from the water surface.
Spatial variability in water chemistry of four Wisconsin aquatic ecosystems - High speed limnology Environmental Science and Technology datasets
Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. We developed a new sensor platform to continuously samples surface water at a range of speeds (0 to > 45 km hr-1) resulting in high-density, meso-scale spatial data. Here, we archive data associated with an Environmental Science and Technology publication. Data include a single spatial survey of the following aquatic ecosystems: Lake Mendota, Allequash Creek, Pool 8 of the Upper Mississippi River, and Trout Bog. Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected).
Data in support of 'Mechanistic insights into plant community responses to environmental variables: genome size, cellular nutrient investments, and metabolic trade-offs.'
Data was collected to examine whether and how the plant genome size (GS) influences traits (stomata size, stomata density, cellular and tissue level carbon (C), nitrogen (N), and phosphorus (P) contents) and metabolic-tradeoffs (of photosynthesis, evapotranspiration, water-use, efficiency) of plants in treatment plots in which nothing, N, P, or NP had been annually added. Data was collected from ~500 plants from seven grassland sites that are all part of the Nutrient Network (https://nutnet.org), a globally distributed experiment in which plots have different nutrient amendment treatments that are administered identically to allow cross-site comparisons of the effects of nutrients on biodiversity patterning. The sites chosen varied along a North-South latitude, longitude, mean annual precipitation (MAP) and mean annual temperature (MAT) gradient.
SBC LTER: Reef: California kelp canopy and environmental variable dynamics
This dataset contains time series of kelp canopy area for giant kelp, Macrocystis pyrifera, and bull kelp, Nereocystis luetkeana, and canopy biomass of giant kelp derived from Landsat satellite imagery along with time series of environmental variables known to be associated with kelp forest dynamics. Data are organized into a single NetCDF file and kelp canopy data are co-located with the nearest environmental data point.
Spain's marginal electricity mix and its relevance for assessing the environmental performance of installations with variable load or power
<p>This upload contains the Supplementary Information file and the underlying data as Excel-file for the Journal article with the same name. More specifically, it provides time series of the Spanish electricity generation mix for the years 2015-2020 for energy system analysis and the life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1). Further details are available on request.</p>
CO2, CH4, and H2O flux data and associated environmental variables for the BBC collapse scar for 2004
This data set contains flux measurements for the transect from the center of the BBC collapse scar (0m) into the surrounding fire scar (30m) of the Survey Line Fire (burned in June-July 2001). We measured CO2, H2O and CH4 fluxes every one to two weeks throughout the growing season of 2004. We measured fluxes at permanent plots established from the center of the bog into the surrounding burn at 0, 6, 12, 18, 24, and 30 m on the east and west side of the transect. Flux measurements on either side of the transect were treated as replicates. CO2 and H2O fluxes were measured using a Li-840 infrared gas analyzer (Licor Inc., Lincoln, NB, USA). The IRGA was calibrated before each trip to the field using a span of 400 ppm and a zero of N2 gas. We logged data every 0.5 seconds for 2 min. To account for measurement variability, we conducted two measurements in succession at each location, after flushing the chamber for accumulated CO2 and H2O. For the flux measurements, we built plexiglass chambers with pipe insulation bases with dimensions of 61 x 61 x 30.5 cm, 61 x 61 x 61 cm, or 61 x 61 x 122 cm. The shorter chambers were used in the collapse portion of the transect. Chambers included fans for air circulation, inlet and outlet ports for CO2 measurements, or just outlet ports for CH4 measurements (Carroll and Crill 1997). We placed chambers directly on the soil surface and used pipe insulation and plastic sheeting to make a solid seal during the measurement. To estimate the volume for each chamber measurement, we measured the distance to the soil surface from a 6 cm grid suspended 30 cm above each plot, the surface area was then used to calculate the chamber volume for each measurement. Dark measurements were used to determine CO2 derived from soil and root respiration (ecosystem respiration) using a two-layer cloth shroud with a reflective surface to exclude solar radiation. To estimate net ecosystem exchange (NEE) of CO2, we conducted chamber measurements of plant and soi
Odontocete detections and corresponding values of environmental variables in the Hawaiian Archipelago
<p>This dataset contains detections of echolocation clicks at two sites in the Hawaiian Archipelago. These sites are Hawaii and Manawai (also known as Pearl and Hermes Reef). Echolocation clicks have been labeled using a neural network classifier that was trained and tested on data from the Hawaiian Islands and can successfully identify many species of regionally present odontocetes. During the labeling process, clicks were grouped into five-minute bins and each bin was given a class label. The data provided here is further binned at a daily level, where counts of a given class represent the number of five-minute bins within a given day that were labeled as that class. One file is provided per site, and files are in .csv format that can be read using any desired coding language. </p> <p>In addition to acoustic counts, values for environmental variables considered in the corresponding manuscript are provided in the CSV files. The final file in this dataset contains satellite-derived chlorophyll-a concentration values from NASA MODIS for the Hawaiian region (used to create Supplementary Fig. 1). Details on all variables and how they were accessed can be found in the manuscript and in the README file accompanying this dataset. </p>
FIGURE 4 Average values for eight environmental variables over 17 windows that follow the Bufo bufo – B in Environmental correlates of the European common toad hybrid zone
FIGURE 4 Average values for eight environmental variables over 17 windows that follow the Bufo bufo – B. spinosus hybrid zone from the Atlantic coast (window 1) to the Mediterranean (window 17). Variables shown are those selected by a logistic regression analysis, with 'species' as dependent variable and explanatory variables available for selection as in table 1. Units are as in table 1; see also Hijmans et al. (2005). Values for B. bufo and B. spinosus are shown by small and large dots, respectively. Grey areas indicate that values for B. bufo are lower than for B. spinosus. The graph at the top left provides AUC model fit values along with major topographical references. Rectangles indicate stretches of the species contact for which the environmental models have good fit (AUC> 0.8), with consistent results indicated by green shadings. For the other windows with less than good model fit, signals are likely to be absent or void, either from poor sampling (window 1), the presence of rivers (windows 5–9, 13–14), or a thin or absent species' contact (windows 16–17) (see fig. 3).
Saw Kill river (NY, USA) metagenomics and environmental variables
<p>Microbial community structure and diversity in waterways are altered by wastewater treatment plant (WWTP) discharge which can introduce human-associated microbes, antimicrobial resistance (AMR), and significantly change environmental variables. To better understand these interactions, we investigated the impact of the Bard College WWTP on microbial communities collected from the surface water and sediment from four sites, two sites above and two sites below the Bard College outflow, as well as the outflow itself, this was performed over a period of five months. We measured physico-chemical parameters such as temperature, turbidity, conductivity, dissolved oxygen, and salinity as well as the bioindicators <em>Escherichia</em> <em>coli</em>, total coliforms, and <em>Enterococcus</em> sp. concentration, endotoxins, the <em>intI1</em> gene marker, and the total bacterial abundance through <em>16S</em> <em>rRNA</em> gene.</p>
Fig. 5 in Effects Of The Environmental Variables On The Alien Amphipod Pontogammarus Robustoides In The Daugava River And Its Reservoirs
Fig. 5. Ordination diagramm of RDA between data of the individual size groups of P. robustoides and environmental variables identified as significant (p<0.05) by Monte Carlo permutation test for study area. PR, P – Pļaviņas Reservoir, Pikstere, PR, VB - Pļaviņas Reservoir, Vārpu backwater, PR, GB - Pļaviņas Reservoir, Gobena backwater, PR, US - Pļaviņas Reservoir, upper stretch, DK – Daugava River, Klidziņa, KR, N – Ķegums Reservoir, Ņega, KR, G – Ķegums Reservoir, Graužupīte, RR, T – Riga Reservoir, Tome, RR, O - Riga Reservoir, Ogre, DR – Daugava River, Riga; ORP – oxred potential
Fig. 3 in Effects Of The Environmental Variables On The Alien Amphipod Pontogammarus Robustoides In The Daugava River And Its Reservoirs
Fig. 3. Seasonal changes of the physico-chemical parameters and relative water level in the Pļaviņas Reservoir.
Fig. 2 in Effects Of The Environmental Variables On The Alien Amphipod Pontogammarus Robustoides In The Daugava River And Its Reservoirs
Fig. 2. Seasonal changes of the water level in the Daugava River (the hydrological station at Jēkabpils) and the Pļaviņas Reservoir (the hydrological station at Pļaviņas).
Figure 3 in Fish community assemblages in relation to environmental variables in the Lefini River, middle Congo River basin (Republic of Congo)
Figure 3. – RDA ordination of species, stations and the three forward selected environmental variables. A: Biplot of stations and environment variables (1-5: habitats 1-5); B: Biplot of stations and species. ▲: grass bank stations; ●: forest bank stations.
Figure 1 in Fish community assemblages in relation to environmental variables in the Lefini River, middle Congo River basin (Republic of Congo)
Figure 1. – Sampling stations (1-12). Grass bank stations: 1, 4, 5, 6, 10 and 11; Forest bank stations: 2, 3, 7, 8, 9 and 12; LFR: Lefini Faunal Reserve; LLGNR: the Lesio-Louna Gorilla Natural Reserve.
Figure 3. CCA showing the relationship between 16 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 3. CCA showing the relationship between 16 species (red triangles) and 9 environmental variables (red arrows). See Tables 2 and 4 for an explanation of abbreviations and variables.
Figure 2 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 2. Jaccard's coefficient similarity dendrograms showing the faunal similarity among the 12 sampling sites (based on presence/absence of species) and clustering relationships among the 16 ostracod species. (Species codes are given in Table 3.)
Fig. 1 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Fig. 1. Map of the eight studied coastal lagoons. Selected sampling sites at Tuzla Lake 1 (St-1), Tuzla Lake 2 (St-2), Tuzla Lake 3 (St-3), Taz (St-4), Işık (St-5), Dalyan (St-7, 8, and 9), Kuvalak (St-10), and Taşaltı (St-11 and 12) were used for comparisons of the lagoons. The sampling sites are indicated by red circles; the red arrows show the direction of water currents.
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B in PhylOgeOgraphy and pOtential distributiOn OF Sturnira lilium and S. giannae (ChirOptera: PhyllOstOmidae) With range eXtensiOn FOr S. giannae in the CerradO and Pantanal biOmes
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B, and C were the ones that most affected the potential distribution of S. giannae, and D, E, and F most affected the potential distribution of S. lilium. The curves show the average response of the 10 replicate Maxent runs (red) and the standard deviation (blue).
Fig. 7 in Wing shape is influenced by environmental variability in Polietina orbitalis (Stein) (Diptera: Muscidae)
Fig. 7. Comparison between the PLS analysis (with elevation) and CVA (CV1) showing that individuals with a wider wing phenotype occur in populations living at a maximum elevation of 700 m (A) and those with a thinner wing phenotype occur at elevations over 900 m (B). Localities are as follows: 1. Mbaracayú, 2. Fênix, 3. Jundiaí do Sul, 4. Guarapuava, 5. Ponta Grossa and 6. Colombo.
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.