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163 results for “temporal variability”
Figure 1 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 1. Map of the Rio de Janeiro state coast highlighting the 12 sampling stations in Araruama lagoon.
Figure 5 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 5. Cladocera Assemblage of Araruama Lagoon from January 2010 to December 2013. Stations 11 and 12 with different scales. E. spinifera (black and white lines arranged laterally); P. tergestina (black with small white spots);P. avitostris (vertical black and white lines); P. polyphemoides (chess pattern); P. sckmackeri (black and white lines waved horizontally).
Figure 3 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 3. BoxPolt of temperature presented from means and standard deviation, spatial variation (A) and temporal variation (B).
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 sequences obtained from specimens of C. osculatum sp. D and C. osculatum sp. E analysed in the present study, based on Bayesian Inference (BI) method using MrBayes v3.2.2 (Ronquist et al., 2012). Evolutionary distance was estimated using the TrN + G (G = 0.60) substitution model as implemented in jModeltest (Posada, 2008), with the AIC approach (Posada and Buckley, 2004). Posterior probability values are the result of 1.000000 of runs and are reported at the nodes. The coloured icons correspond to the two species considered in this study (red = C. osculatum sp. D and blue = C. osculatum sp. E).
Fig. 2 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 2. Schematic distribution of the fish species examined in the present study for larval of C. osculatum sp. D and C. osculatum sp. E, along the continental shelf of the Ross Sea coastal ecosystem. Arrows indicating preferred preys and the diet preference for each fish species are reported according to the literature (La Mesa et al., 2004). The represented pelagic organisms comprise species of euphausiids and fish juveniles, benthic and epibenthic organisms are polychaetes, amphipods, decapods and gastropods. A pie chart with the relative proportions of C. osculatum sp. D and C. osculatum sp. E is given for each fish species. Squares and circles represent the hypothetical distribution of C. osculatum sp. D and C. osculatum sp. E larvae in their intermediate hosts.
Fig. 3 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 3. Schematic representation of the hypothetic life-cycle of C. osculatum sp. D (a) and C. osculatum sp. E (b) in the Ross Sea.
Data for "Temporally Variable Stream Width and Surface Area Distributions in a Headwater Catchment" by Barefoot et al.
<p>This repository holds data necessary for reproducing results from Barefoot et al. (in prep). The data is a record of high-resolution stream width measurements in Stony Creek, a headwater stream in North Carolina, USA. Included are:</p> <ul> <li>Width measurements for 13 surveys collected from 2015 to 2016. </li> <li>High-resolution discharge measurements of discharge collected over the same period.</li> <li>Data summarizing the topology of the drainage network during each survey.</li> <li>A 5-year record of discharge from the closest USGS gauge for historical flow characterization. </li> <li>Data for estimating measurement error. </li> <li>A summary table for drainage density during each survey as well as other summary statistics. </li> <li>Geographic data detailing the mapped stream locations. </li> </ul> <p>The records span a range of discharge conditions measured from summer 2015 to spring 2016. Other hydrological data for this catchment for a longer range of time is available by request from Dr. Margaret Zimmer and Dr. Brian McGlynn. </p> <p>Code for analyzing and reproducing these results can be found on Github at [insert link to repo here.]</p>
Code for 'Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'
<p>Code to reproduce the analyses in the study '<strong>Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'</strong></p> <p>The analysis requires two R scripts available here, plus original 2013-4 NHANES data files, downloadable from the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2013).</p> <p>The first R script, 'merging script.r' takes the original NHANES files, extracts the variables required for the study, merges them into a single data frame, and saves this in .csv format. The NHANES files it requires are:</p> <p># Demographics, food insecurity and BMI<br> DEMO_H.XPT<br> FSQ_H.XPT<br> BMX_H.XPT</p> <p># Summary files of food recalls<br> DR1TOT_H.XPT<br> DR2TOT_H.XPT</p> <p># Individual foods files from food recalls<br> DR1FF_H.XPT<br> DR2FF_H.XPT</p> <p>The second R script takes the .csv file output by the merging script, and reproduces the analyses and figures described in the paper.</p> <p>Initially uploaded by Daniel Nettle, April 23rd 2019. Slightly revised versions uploaded August 6th 2019 by Daniel Nettle.</p>
Figure 3 in Tintinnina (Ciliophora) and Foraminifera in plankton of hypersaline Lagoon Bardawil (Egypt): spatial and temporal variability
Figure 3. Dependence of number of found tintinnid species on number of analyzed samples in Lagoon Bardawil (a) and the Mediterranean Sea (b).
Figure 2 in Tintinnina (Ciliophora) and Foraminifera in plankton of hypersaline Lagoon Bardawil (Egypt): spatial and temporal variability
Figure 2. Dependence of total tintinnid abundance on number of tintinnid species in Lagoon Bardawil during 2009 and 2010 (a- winter, b- all seasons).
Figure 7 in Temporal variability of the macroinvertebrate community associated with Eichhornia azurea (Swarts) Kunth (Pontederiaceae) in a lake marginal to a tropical river
Figure 7. NMDS diagram of macroinvertebrates in the sampled periods. (A) NMDS: structure; (B) NMDS: composition.
Figure 1 in Temporal variability of the macroinvertebrate community associated with Eichhornia azurea (Swarts) Kunth (Pontederiaceae) in a lake marginal to a tropical river
Figure 1. Study site and sampling stations (P1, P2, and P2) in Barbosa Lake (the circle shows the connection site between the lake and the river).
Figure 4 in Temporal variability of the macroinvertebrate community associated with Eichhornia azurea (Swarts) Kunth (Pontederiaceae) in a lake marginal to a tropical river
Figure 4. Means and standard deviations of root biomass of E. azurea (g.DW–1) in Barbosa Lake during the study period.
Figure 2 in Temporal variability of the macroinvertebrate community associated with Eichhornia azurea (Swarts) Kunth (Pontederiaceae) in a lake marginal to a tropical river
Figure 2. Scheme of an adult E. azurea individual showing the sampled root mass in sequential direction from the apical to the basal parts of the plant.
Figure 22 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 22. Outlines of 8 dipole structures that preceded all intense algal blooms in the central part of the South Caspian during the study period from 1999 to 2022. Different colors mark different years.
Figure 20 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 20 shows interannual variability of monthly averaged cloudiness for June, July, August and September in 2002-2022. One can see that all bloom events have occurred when monthly averaged cloudiness was in the range of 0.1-0.57. In general, absence of clouds should be favorable for algal bloom due to high level of insolation, but we can point to years 2006, 2012, 2014, 2016, and 2022 when monthly averaged cloudiness in August was less than 0.3 and no algal blooms were identified.
Figure 21 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 21. Interannual variability of photosynthetically active radiation (Einstein/m2day) in June, July, August and September (2002-2022) in the southern part of the South Caspian. Red circles mark cases of intense algal bloom.
Figure 20 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 20. Interannual variability of cloudiness in June, July, August and September (2002-2022) in the southern part of the South Caspian. Red circles mark cases of intense algal bloom.
Figure 17 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 17. Interannual variability of SST (0C) in June, July, August and September (2002-2022) in the southern part of the South Caspian. Red circles mark cases of intense algal bloom.
Figure 19 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 19 shows interannual variability of monthly averaged wind speed for June, July, August and September in 2000-2022. We can see that all bloom events occurred when monthly averaged wind speed was in the range of 4.3-5.0 m/s. In general, low wind speed should be favorable for algal bloom due to absence of wind-wave mixing, but the problem is that this area of the Caspian is the calmest area of the sea (Rahimi et al. 2022). Only twice, in September 2016 and 2019, wind speed reached 5.5-5.75 m/s (Figure 19). Seemingly, every year should be favorable for algal bloom, but that is not true. Moreover, in August 2003, 2006, 2007, 2014, and 2016, wind speed was less than 4.3 m/s and no algal blooms were recorded in these years.
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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.