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3,673 results for “Temporal”
Linked collectors and determiners for: First records of Leucania rawlinsi Adams and L. senescens M ̂ schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution.
Natural history specimen data linked to collectors and determiners held within, "First records of Leucania rawlinsi Adams and L. senescens M ̂ schler (Lepidoptera: Noctuidae) in Brazil: redescription, potential association with Bt maize, larval parasitoids, and spatial and temporal distribution". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4c9c5163-7fce-43f3-be05-c64780788e96">https://bionomia.net/dataset/4c9c5163-7fce-43f3-be05-c64780788e96</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4c9c5163-7fce-43f3-be05-c64780788e96">https://gbif.org/dataset/4c9c5163-7fce-43f3-be05-c64780788e96</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Danish Tingidae - Data for study on occurrence, temporal trends and potential species.
Natural history specimen data linked to collectors and determiners held within, "Danish Tingidae - Data for study on occurrence, temporal trends and potential species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a968d7e9-5391-482d-8927-4b34b9863d56">https://bionomia.net/dataset/a968d7e9-5391-482d-8927-4b34b9863d56</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a968d7e9-5391-482d-8927-4b34b9863d56">https://gbif.org/dataset/a968d7e9-5391-482d-8927-4b34b9863d56</a>. Formatted as a Frictionless Data package.
Data for "Temporal and vertical variability in phytoplankton primary production and microbial community respiration in the North Pacific Subtropical Gyre"
<p>This ALOHA_GOP&R.xslx data set provides measurements of biological rates conducted between April 2015 and July 2020 at different depths in the euphotic zone at or in the vicinity of Station ALOHA (22° 45' N, 158° W), the long-term sampling site of the Hawaii Ocean Time-series (HOT) program, within the North Pacific Subtropical Gyre. </p> <p>The file ALOHA_GOP&R.xslx contains incubation-based measurements of gross oxygen production and community respiration that were measured in the same incubation bottles by applying the <sup>18</sup>O-water method and tracking net changes in oxygen to argon ratios during dawn to dusk in situ incubations, following Ferrón et al. (2016). The samples were measured using membrane inlet mass spectrometry. Rates were measured at 6 depths within the euphotic zone: 5, 25, 45, 75, 100, 125 m, except in a few occasions in which there were no measurements made at 125 m.</p> <p>Data description</p> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Description</p> </td> <td> <p>Units</p> </td> </tr> <tr> <td> <p>Date </p> </td> <td> <p>Date of sampling and start of incubation (UTC -10 hours)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Cruise ID</p> </td> <td> <p>Cruise identification</p> </td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Latitude</p> </td> <td> <p>Latitude</p> </td> <td> <p>degrees N</p> </td> </tr> <tr> <td> <p>Longitude</p> </td> <td> <p>Longitude</p> </td> <td> <p>degrees E</p> </td> </tr> <tr> <td>Stn ALOHA </td> <td>Whether the data are from Station ALOHA (yes/no)</td> <td> </td> </tr> <tr> <td>IncT</td> <td> <p>Incubation time</p> </td> <td> <p>hours</p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td>Nominal depth of sampling and incubation </td> <td> <p>meters</p> </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>CR</td> <td>Estimate of community respiration </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td> <p>Flag GOP</p> </td> <td>Flag identification for gross oxygen production (good=1,questionable=2)</td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Flag CR</p> </td> <td> <p>Flag identification for community respiration (good=1,questionable=2)</p> </td> <td> <p>#</p> </td> </tr> </tbody> </table> <p> </p> <p>The Light-dark_ALOHA_rates.xlsx file contains metabolic rates measured by the ligth-dark oxygen method between June 2005 and June 2007 at different depths in the euphotic zone at Station ALOHA (22° 45' N, 158° W). Rates of net community production, communnity respiration, and gross oxygen production were measured at 6 depths within the euphotic zone (5, 25, 45, 75, 100, 125 m) in dawn to dawn incubations, following Williams et al. (2004). </p> <p>Data description</p> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Description</p> </td> <td> <p>Units</p> </td> </tr> <tr> <td> <p>HOT</p> </td> <td>HOT cruise number</td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td>Date of sampling and start of incubation (UTC -10)</td> <td> <p> </p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td>Nominal depth of sampling and incubation </td> <td> <p>meters</p> </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production, average of 8 replicates</td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>GOP SE</td> <td>Gross oxygen production standard error </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>CR</td> <td> <p>Dark community respiration, average of 8 replicates</p> </td> <td> <p>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></p> </td> </tr> <tr> <td> <p>CR SE</p> </td> <td>Dark community respiration standard error</td> <td> <p>meters</p> </td> </tr> <tr> <td>NCP</td> <td>Net community production,average of 8 replicates </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>NCP SE</td> <td>Net community production standard error </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> </tbody> </table> <p> </p>
Temporal dynamics of biodiversity effects and light-use related traits in two intercropping systems
<p>Dataset for "Temporal dynamics of biodiversity effects and light-use related traits in two intercropping systems"</p>
Four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018)
<p>These data were four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018). They were also the output results of the findings of Ma et al. (2021).</p>
Data for "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities"
<p>Data for the paper "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities" which is in minor revision in Ecology Letters (manuscript id:ELE-00589-2021.R1). A doi will be provided upon publication.</p> <p>Current citation: Danet, A., Mouchet, M., Bonnaffé, W., Thébault, E., & Fontaine, C. (In revision) Species<br> richness and food-web structure jointly drive total biomass and its temporal stability in<br> fish communities Minor revision in Ecology Letters.</p> <p>The repository constains data describing fish community monitoring across stream sections in metropolitan France over the period 1995-2018 by the French Office of Water and Aquatic Ecosystems (ONEMA) using electrofishing.</p> <p>The repository contains:</p> <ul> <li> description of fishing: fishing_protocol.csv <ul> <li>surface: sampled surface</li> <li>opcod: fishing operation code, a unique identifier for each sampling event</li> <li>station: unique identifier for each site</li> <li>nb_sp, nb_ind: number of species, number of individuals</li> </ul> </li> <li>geographical information: station_basin.csv <ul> <li>X, Y: spatial coordinates of the station, expressed in metres in Lambert93 (epsg:2154)</li> <li>basin: name of the hydrographic basin</li> </ul> </li> <li>environment: environment.csv ( _mean: mean, _med: median, _cv: coefficient of variation) <ul> <li>alt: altitude</li> <li>d_source: distance to source</li> <li>strahler: strahler order</li> <li>BOD: Biological Oxygen Demand</li> <li>temperature: water temperature</li> <li>flow: water flow</li> </ul> </li> <li>community data: community_data.csv <ul> <li>species: three digits code corresponding to a given species (see Table S1, Danet et al. in revision)</li> <li>nind: number of individuals</li> <li>biomass: biomass in gram</li> </ul> </li> <li>Length of each fish individual: fish_length.csv <ul> <li>length: length of the fish in millimeter</li> </ul> </li> <li>Inferred food-web: class_network.rda <ul> <li>data: <ul> <li>class_id: size class of a fish individual</li> </ul> </li> <li>network: these data.frame can be handled by igraph::graph_from_data_frame() <ul> <li>from, to: "to" eats "from"</li> </ul> </li> <li>composition: <ul> <li>sp_class: concatenation of species and class_id columns</li> <li>bm_std: biomass reported to the sampled surface</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
A relative-motion method for parsing spatio-temporal behaviour of dyads using GPS relocation data
<p>In this paper, we introduce a novel method for classifying and computing the frequencies of movement modes of intra- and interspecific dyads, focusing in particular on distance-mediated approach, retreat, following and side by side movement modes. Besides distance, other factors such as time of day, season, sex, or age can be included in the analysis to assess if they cause frequencies of movement modes to deviate from random. By subdividing the data according to selected factors, our method allows us to identify those responsible for (or correlated with) significant differences in the behaviour of dyadic pairs. We demonstrate and validate our method using both simulated and empirical data. Our simulated data were obtained from a relative-motion, biased random-walk (RM-BRW) model with attraction and repulsion components. Our empirical data were GPS relocation data collected from African elephants in Etosha National Park, Namibia. The simulated data were primarily used to validate our method while the empirical data were used to illustrate the types of behavioural assessment that our methodology reveals. Our method facilitates automated, observer-bias-free analysis of the locomotive interactions of dyads using GPS relocation data, which are becoming increasingly ubiquitous as telemetry and related technologies improve. It should open up a whole new vista of behavioural-interaction type analyses to movement and behavioural ecologists.</p>
Fig. 3 in Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances
Fig. 3. Principal Components Analysis of the environmental variables' matrixes recorded in the upper Uruguay River between October 2001 and March 2004. Sampling sites: LIG: Ligeiro, ULIG: Uruguay-Ligeiro, CH: Chapecó and UCH: Uruguay-Chapecó. Reproductive Periods: RP1: First Reproductive Period, RP2: Second Reproductive Period and RP3: Third Reproductive Period.
Fig. 2 in Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances
Fig. 2. Abundance of fish larvae in different stages of development recorded in the sampling sites of the upper Uruguay River from October 2001 to March 2004. Larval development stages: LY = Larval Yolk; PF = Pre-flexion; FL = Flexion and FP = Post-flexion. Sampling sites: LIG: Ligeiro, ULIG: Uruguay-Ligeiro, CH: Chapecó and UCH: Uruguay- Chapecó. Reproductive Periods: RP1: First Reproductive Period, RP2: Second Reproductive Period and RP3: Third Reproductive Period.
Fig. 1 in Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances
Fig. 1. Location of the sampling sites in the upper Uruguay River in southern Brazil. Samplings sites: LIG: Ligeiro, ULIG: Uruguay-Ligeiro, CH: Chapecó, UCH: Uruguay-Chapecó.
Data of "Impact of temporal correlations on high risk outbreaks of independent and cooperative SIR dynamics"
<p>The data reported in the paper: Sajjadi et al. (2021) Impact of temporal correlations on high risk outbreaks of independent and cooperative SIR dynamics. PLoS ONE 16(7): e0253563. https://doi.org/10.1371/journal.pone.0253563<br> Each directory contains the data illustrated in one figure. The data structure and properties are described in .info files within each directory.</p> <p><br> All the simulations, analyses and illustrations have been conducted via the Epyc package (written in C++ and Python), developed by Sina Sajjadi. Epyc is available under GPLv3, at https://github.com/Sepante/Epyc.</p>
Figure 1 in Spatial and temporal distribution of breeding anurans in streams in southeastern Brazil
Figure 1. Location of (A) the RPPN Santuário do Caraça, Minas Gerais state, southeastern Brazil, and (B) the eight streams sampled within the reserve.
Figure 3 in Spatial and temporal distribution of breeding anurans in streams in southeastern Brazil
Figure 3. Relationship between estimated volume of stream sampled sections (m3) and (A) anuran species richness and (B) number of anuran species with calling males at the RPPN Santuário do Caraça, southeastern Brazil. Numbers in the graphs correspond to streams 1–8.
Figure 3 in Factors influencing spatial and temporal structure of frog assemblages at ponds in southeastern Brazil
Figure 3. Distribution of nine tadpole species within ponds at Santuário do Caraça, southeastern Brazil, according to variables used to describe microhabitats used by them and period of occurrence, in the first three axes of discriminant space. In the first discriminant axis, smaller values represent larger association to the bottom. In the second axis, the largest values indicate use of deeper microhabitats by tadpoles. In the third axis, larger values indicate species that used both microhabitats with and without aquatic vegetation, and lower values indicate species that used only microhabitats with aquatic vegetation. Centroids for each species are shown on the right.
Figure 1 in Factors influencing spatial and temporal structure of frog assemblages at ponds in southeastern Brazil
Figure 1. Mean monthly temperatures and monthly rainfall at the study site between September 2003 and December 2004.
Figure 2 in Factors influencing spatial and temporal structure of frog assemblages at ponds in southeastern Brazil
Figure 2. Distribution of adult individuals of 22 anuran species at Santuário do Caraça, southeastern Brazil, according to variables used to describe microhabitat use and activity periods, in the first three axes of the discriminant function. Centroids for each species are shown on the right.
Fig. 3 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 3. Kernel density estimates of daily predator activity patterns. A, tiger, leopard, and dhole; B, tiger and leopard; C, tiger and dhole; D, leopard and dhole. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.
Fig. 5 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 5. Kernel density estimates of daily activity patterns of leopards and prey species. A, leopard and gaur; B, leopard and sambar; C, leopard and barking deer; D, leopard and tapir; E, leopard and wild boar. The dashed lines are kernel density estimates for leopards and the solid lines are kernel density estimates for the prey species. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.
Fig. 4 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 4. Kernel density estimates of daily activity patterns of tigers and prey species. A, tiger and gaur; B, tiger and sambar; C, tiger and tapir; D, tiger and barking deer; E, tiger and wild boar. The dashed lines are kernel density estimates for tigers and the solid lines are kernel density estimates for the prey species. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.
Fig. 6 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 6. Kernel density estimates of daily activity patterns of dholes and prey species. A, dhole and gaur; B, dhole and sambar; C, dhole and tapir; D, dhole and barking deer; E, dhole and wild boar. The dashed lines are kernel density estimates for dholes and the solid lines are kernel density estimates for the prey species. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.
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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.