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3,673 results for “Temporal”
Receptor exocytosis imaged with high temporal resolution for diverse receptor cargos
<p>Cells perceive and interact with their environment in part through the expression, activation, and regulation of receptors on their plasma membrane. These receptors are dynamically trafficked from the plasma membrane in a process called endocytosis and delivered to the plasma membrane via exocytosis. Different receptors take diverse routes through the cell before being delivered via exocytosis. The data in this project focuses on 3 prototypical plasma membrane receptors - the B2 adrenergic receptor, the µ opioid receptor, and the transferrin receptor. Using a pH-sensitive green fluorescent protein variant, we visualized these receptors in cells as they recycled to the plasma membrane. We subsequently hand-labeled a subset of the data in order to build an automated image analysis method that could be used to detect receptor exocytosis across diverse imaging conditions. This repository contains our primary microscopy data from these studies as well as the labeling for use in supervised machine learning.</p> <p>These data support <a href="http://arxiv.org/abs/2106.07623">Evans et al 2021</a> and subsequent publications.</p> <p><strong>Data Collection</strong><br> TIFF image stacks were collected using a Nikon Eclipse TiE Inverted Microscope using TIRF illumination with a solid state 488nm laser through a Nikon 60x/1.49NA TIRF objective and captured using an Andor iXon 897+ EMCCD camera. The camera was windowed to a 300x300 pixel view and images were collected with a 18.5ms exposures (~54Hz). Images were collected across two days, with two coverslips of each condition collected on day 1, and one coverslip collected on day 2.</p> <p><strong>DNA Constructs</strong><br> The 3 cargos imaged in these data are the transferrin receptor (TfR), the B2-adrenergic receptor (B2AR, B2), and the µ opioid receptor (MOR). Constructs encoding these receptors, tagged extracellularly with the ph-sensitive GFP variant Superecliptic pHluorin (SpH, <a href="https://www.cell.com/biophysj/fulltext/S0006-3495(00)76468-X">Sankaranarayanan et al. 2000</a>, have been previously described in <a href="http://www.nature.com/articles/nn1679">Yudowski et al. 2006</a> for B2AR, <a href="https://www.jneurosci.org/content/30/35/11703">Yu et al. 2010</a> for MOR, and <a href="https://www.molbiolcell.org/doi/10.1091/mbc.e08-08-0892">Yudowski et al. 2009</a> for TfR.</p> <p><strong>Cell Culture</strong><br> HEK293 cells were cultured in DMEM High Glucose (Hyclone) supplemented with 10% Heat Inactivated FBS (Gibco). Cells expressing B2 and MOR were stably selected from transient transfection using G418. Cells expressing TfR were transfected 3 days before the experiments presented here using Effectene following manufacturers' instructions. Before imaging, cells were transferred to 25mm diameter #1.5 glass coverslips (Electron Microscopy Sciences). Two days after plating, experiments began.</p> <p><strong>Imaging conditions</strong><br> Cells were imaged in L-15 minimal media supplemented with 1% FBS. For MOR and B2, cells were imaged for 1 minute at ~0.16Hz without perturbation. Then agonist was added (10µM DAMGO for MOR, 10µM isoproterenol for B2) to the media and cells were imaged for 5 minutes to ensure that receptors clustered and internalized. After internalization, cells were bleached with 100% laser power for 1 minute and then imaged at 54Hz to visualize exocytic events. exocytosis was captured for up to 20 minutes after initial treatment, one cell at a time. For TfR, a single frame was taken before bleaching to show receptor expression levels and then cells were bleached and imaged as described above.</p> <p><strong>Data blinding</strong><br> After collection, files were renamed as described in <em>map.md</em>. All metadata files and internalization imaging were separated into the 2 "extras" folders. The exocytosis movies were 'scrambled' to hide cargo identity using the included <em>scrambler.py</em> file. <em>OPP_scramble.log</em> described the mapping of scrambled filenames to the original imaging.</p> <p><strong>Human labeling</strong><br> A subset of the images (22, with roughly equal representation across cargos) were hand labeled for exocytic events. Images were viewed in FIJI <a href="https://www.nature.com/articles/nmeth.2019">Schindelin et al. 2012</a> nad played back at 0.5x. When exocytic events were identified by eye, the playback was paused and the appearance of an event was found through manual advancing of the frames of the movie. The event was labeled using the Cell Counter plugin. Each movie was watched twice to identify as many events as possible. Labeled events are saved a <em><movie-name>-ZYW-1.xml</em> in this dataset.</p> <p><strong>Data organization</strong><br> All exocytic event movies and any matching human labeling are included in this base directory. All internalization movies and all metadata for all movies are included in the Extras folder for the day that movie was recorded. Coverslip and cargo identity are listed in <em>map.md</em> and the ground truth for cargo identity is in <em>OPP_scramble.log</em></p>
Dataset: "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?"
<p>This repository contains the dataset presented in "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?" (https://doi.org/10.3390/ijerph20053798) as well as the SPSS syntax used for the statistical evaluation. Additionally, calibrated binaural recordings of the evaluated stimuli are provided as 32 bit .wav files, the values stored in those files correspond to pascals.</p>
Drivers of spatial and temporal micro- and mesozooplankton dynamics in an estuary under strong anthropogenic influences (The Eastern Scheldt, Netherlands)
<p>Supplement to: Horn, H.G., van Rijswijk, P., Soetaert, K., van Oevelen, D. (2023): Drivers of spatial and temporal micro- and mesozooplankton dynamics in an estuary under strong anthropogenic influences (The Eastern Scheldt, Netherlands). J Sea Res. <a href="https://doi.org/10.1016/j.seares.2023.102357">https://doi.org/10.1016/j.seares.2023.102357</a></p> <p>This data set contains mesozooplankton and microzooplankton abundances, temperature, salinity, O2, DOC, Chl.a, SPM, and nutrient concentrations from eight stations in the Eastern Scheldt sampled in 2018. Phytoplankton growth and microzooplankton grazing rates from dilution experiments are also provided.</p>
Controllable temporal dynamics of titanium oxide memristor for analog time-based neuromorphic computing: Dataset
<p>Dataset used to produce graphs related to the temporal behavior of the Pt/TiO/Au memristors.</p>
Medical Temporal Constraint Extraction Dataset
<p>This dataset contains 836 drug usage guidelines labeled with medical temporal constraints. These constraints conform to a context-free grammar, allowing their semantics to be computationally represented.</p>
Data from: The spatial distribution and temporal trends of livestock damages caused by wolves in Europe
<p>The preprint of the corresponding manuscript can be found here: doi: https://doi.org/10.1101/2022.07.12.499715</p> <p>Wolf populations are recovering and expanding across Europe, causing conflicts with livestock owners. We here compiled incident-based livestock damage data caused by wolves across 21 European countries for the years 2018, 2019 and 2020.</p> <p>The file "<strong>wolf_damages_2018_2019_2020_complete_data_to_publish.csv</strong>" contains the following information per incident: country, target species, cause, number of animals killed/injured/missing, assessment level probability, reported date, number of days until inspection, location, incidentID, uniqueID, NUS1_ID, NUTS2_ID, NUTS3_ID, damage prevention measure, number of wolves attacking, latitude, longitude, comments, metadata constraints.</p> <p>The file "<strong>nuts3_regions_and_LC_where_wolves_are_present.csv</strong>" contains information of the percentage of area occupied by wolves per NUTS3 region for selected land cover variables.</p> <p>The file "<strong>prevention_measures.csv</strong>" contains information about the financial support of livestock damage prevention measure per country or NUTS region</p> <p>The file "<strong>wolf_presence_now_vs_50_years_ago_nuts3.csv</strong>" contains information on NUTS3 regions that had a documented wolf presence 50 years ago.</p> <p>The "<strong>scripts_to_publish.zip</strong>" folder contains the scripts that we used to conduct the analyses.</p>
Analytical Center of University Cultural Productions in the Context of the Conflict (caPAZ) - Temporality
<p>This dataset comprises a collection of journalistic articles written by young university students in Colombia, which is a product of the project: Analytical Center of University Cultural Productions in the Context of the Conflict (caPAZ), funded by the Ministry of Science, Technology and Innovation (Minciencias) and the National Center for Historical Memory (CNM) of Colombia (under the code: 1349-872-76354, agreement 872 of 2020) This corpus includes news written by the 24 colleges media of the Colombian Network of College Journalism from 2012 to 2016. The dataset includes digital news, for a total of <strong>589 </strong>news items related to the armed conflict, the memory of the victims and the peace process in Colombia. These news items were collected through a web-scraping technique, using 3 lemmatized keywords (conflicto armado, memoria de las víctimas y proceso de paz), with the aim of identifying these regular expressions in the logical operators that run through the HTML structure of each Web page</p>
Temporal variation in spider trophic interactions is explained by the influence of weather on prey communities, web building and prey choice
<p>Materials and Methods</p> <p><em>Fieldwork</em><em> and sample processing</em></p> <p>Field collection and sample processing has been described previously by Cuff, Tercel, et al., (2022), but is briefly described in Supplementary Information 1. In short, money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were collected from occupied webs and the ground in barley fields between April and September 2018. Linyphiids occupying webs (n = 78) were prioritised for collection, but ground-active linyphiid and lycosid spiders were also collected. For each linyphiid taken from a web, the height of the web from the ground (mm) and its approximate dimensions were recorded, the latter calculated as approximate web area (mm<sup>2</sup>). To obtain data on local prey density, ground and crop stems were suction sampled using a ‘G-vac’ for approximately 30 seconds at each 4 m<sup>2</sup> quadrat from which spiders were collected. Extraction, amplification and sequencing of DNA, and bioinformatic analysis is described by Cuff, Tercel, et al. (2022) and Drake et al. (2022), and is also detailed in Supplementary Information 2. Amplification was carried out using two complementary PCR primer pairs: one targeting invertebrates generally, and one intended to exclude amplification of spider DNA to reduce the prevalence of ‘host’ reads in the data output (Cuff et al. 2023). Amplicons were sequenced via Illumina MiSeq V3 with 2x300 bp paired-end reads. The resultant sequencing read counts were converted to presence-absence data of each detected prey taxon in each individual spider. Given the prevalence of sequencing reads associated with each spider analysed and the impossibility of disentangling these from detections of intraspecific predation (i.e., cannibalism), all such reads were removed (Cuff et al. 2023), although intrageneric and intrafamilial predation were still detected.</p> <p> </p> <p><em>Weather data</em></p> <p>Weather data were taken from publicly available reports from the Cardiff Airport weather station (6.6 km from the study site) via “Wunderground” (Wunderground, 2020), to represent local weather conditions. This does not necessarily reflect smaller-scale effects (e.g., microclimate-scale; Bell, 2014; Holtzer et al., 1988), but the timescale of detection for dietary metabarcoding reduces the value of that resolution given that spiders may forage across multiple microclimates. We collated data from 1<sup>st</sup> January 2018 to 17<sup>th</sup> September 2018 (the last field collection). Weather data were also separately extracted for the week preceding each of the two 2017 collection dates (3<sup>rd</sup> to 9<sup>th</sup> August and 29<sup>th</sup> August to 4<sup>th</sup> September 2017). Specifically, daily average temperatures (°C), daily average dew point (°C), maximum daily wind speed (km h<sup>-1</sup>), daily sea level pressure (hPa) and day length (min; sunrise to sunset) were recorded. Precipitation data were downloaded via the UK Met Office Hadley Centre Observation Data (UK Met Office, 2020) as regional precipitation (mm) for South West England & Wales. Weather data were converted to mean values for seven days preceding the collection of spider samples to correspond with the longevity of DNA in the guts of spiders (Greenstone et al., 2014).</p> <p> </p> <p><em>Statistical Analysis</em></p> <p>All analyses were conducted in R v4.0.3 (R Core Team, 2020). To assess how weather affects spider trophic interactions over time, we analysed dietary changes across weather gradients using multivariate models. To identify whether this was likely to be driven by changes in prey abundance, we assessed the corresponding changes in the prey communities and then used null models to ascertain whether spiders were responding to prey abundance changes through prey choice. Given the dependence of linyphiid spiders on webs for foraging, we also compared web height and area over weather gradients to assess whether this may be a component of adaptive foraging. To assess the inter-annual consistency of prey choices in response to weather conditions, we also assessed whether prey preference data could be used to improve the predictive power of prey choice models. For this, we generated null models for 2017 data with prey abundance weighted by prey preferences estimated with the 2018 data. This allowed us to assess the consistency of prey choice under similar conditions, but also provides insight as to whether this framework can be used to predict predator responses to diverse prey communities under dynamic conditions. We detail the specific stages of this analytical framework in the below sections.</p> <p> </p> <p><em>Sampling completeness and diversity assessment</em></p> <p>To assess the diversity represented by the dietary analysis and the invertebrate community sampling, and the completeness of those datasets, coverage-based rarefaction and extrapolation were carried out, and Hill diversity calculated (Chao et al., 2014; Roswell, Dushoff, & Winfree, 2021). This was performed using the ‘iNEXT’ package with species represented by frequency-of-occurrence across samples (Chao et al., 2014; Hsieh et al., 2016; Figures S4 & S6).</p> <p> </p> <p><em>Relationships between weather, spider trophic interactions and prey community composition</em></p> <p>Prey species that occurred in only one spider individual were removed before further analyses to prevent outliers skewing the results. Spider trophic interactions were related to temporal and weather variables in multivariate generalized linear models (MGLMs) with a binomial error family (Wang, Naumann, Wright, & Warton, 2012). Trophic interactions were related to temporal variables and their pairwise interactions (including spider genus to account for any confounding effect), weather variables and their pairwise interactions, and weather variables and their interactions with spider genus and time (to account for any confounding effects) in three separate MGLMs. These variables were separated into different models (Temporal model, Weather interaction model and Confounding effects model) to improve model fit and reduce singularity. Invertebrate communities from suction sampling were related to temporal and weather variables in identically structured MGLMs (excluding the spider genus variable) with a Poisson error family.</p> <p>All MGLMs were fitted using the ‘manyglm’ function in the ‘mvabund’ package (Wang et al., 2012). ‘Temporal model’ independent variables were calendar day (<em>day</em>), mean day length in minutes for the preceding week (<em>day length</em>), spider genus (for dietary models only, to ascertain any effect of spider taxonomic differences on dietary differences over time and day lengths) and all two-way interactions between these variables. ‘Weather interaction model’ independent variables were mean temperature, precipitation, dewpoint, wind speed and pressure for the preceding week, and pairwise interactions between weather variables. ‘Confounding effects model’ independent variables were day (to investigate the interaction between time and weather), spider genus (for dietary models only, to ascertain any effect of spider taxonomic differences on dietary differences over time and day lengths), mean temperature, precipitation, dewpoint, wind speed and pressure for the preceding week, and two-way interactions of each weather variable with day and genus.</p> <p>Trophic interaction and community differences were visualised by non-metric multidimensional scaling (NMDS) using the ‘metaMDS’ function in the ‘vegan’ package (Oksanen et al., 2016) in two dimensions and 999 simulations, with Jaccard distance for spider diets and Bray-Curtis distance for invertebrate communities. For the dietary NMDS, outliers (n = 21; samples containing rare taxa) obscured variation on one axis and were thus removed to facilitate separation of samples and achieve minimum stress. For visualization of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the ‘ordisurf’ function in the ‘ggplot’ package (Wickham, 2016).</p> <p> </p> <p><em>Relationships between web characteristics and weather variables</em></p> <p>Web area and height were compared against weather and temporal variables using a multivariate linear model (MLM) with the ‘manylm’ command in ‘mvabund’ (Wang et al., 2012). Log-transformed web area and height comprised the multivariate dependent variable, and day, spider genus, temperature, precipitation, dewpoint, wind, pressure and two-way interactions between each of these and day and genus comprised the independent variables.</p> <p> </p> <p><em>Variation in spider prey choice across weather conditions</em></p> <p>To separately represent spiders from different weather conditions in prey choice analyses, sample dates for every spider were clustered based on the mean weather conditions (temperature, precipitation, dewpoint, wind and pressure) of the week before collection (7 days, to align approximately with spider gut DNA half-life; Greenstone et al., 2014). Alongside data from 2018 (n = 24 collection dates), two sampling periods from 2017 were included in the clustering to ascertain similarity of weather conditions for additional inter-annual prey choice analyses described below. The clustering process is described in Supplementary Information 3. Five clusters were generated: High Pressure (HPR), Hot (HOT), Wet Low Dewpoint (WLD), Dry Windy (DWI), Wet Moderate Dewpoint (WMD), and 2017 (2017 sampling periods).</p> <p>Prey preferences of spiders in each of the weather clusters was analysed using network-based null models in the ‘econullnetr’ package (Vaughan et al., 2018) with the ‘generate_null_net’ command. Consumer nodes in this case represented spiders belonging to each of the weather clusters. Econullnetr generates null models based on prey abundance, represented here by suction sample data, to predict how consumers will forage if based on the abundance of resources alone. These null models are then compared against the observed interactions of consumers (i.e., interactions of spiders within each weather cluster with their prey) to ascertain the extent to which resource choice deviated from random (i.e., density dependence). The trophic network was visualised with the associated prey choice effect sizes using ‘igraph’ (Csardi & Nepusz, 2006) with a circular layout, and as a bipartite network using ‘ggnetwork’ (Briatte, 2021; Wickham, 2016). The normalised degree of each weather cluster node was generated using the ‘bipartite’ package (Dormann, Gruber, & Fruend, 2008) and compared against the normalised degree of the same node in the null network to determine whether spiders were more or less generalist than expected by random. Prior to the prey choice analysis, an hemipteran prey identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty.</p> <p> </p> <p><em>Validating and predicting relationships between years</em></p> <p>To test how generalisable the results are and the extent to which weather drives prey preferences, we used a measure of prey preference (observed/expected values; observed interaction frequencies divided by interaction frequencies expected by null models) from the above prey choice analysis to assess whether we could more accurately predict observed trophic interactions under similar weather conditions for data from a linked study at the same location in 2017. These additional data represent a subset of the spider taxa analysed above (<em>Tenuiphantes tenuis</em> and <em>Erigone</em> spp.) collected using the same methods by the same researchers and in the same locality (Cuff, Drake, et al., 2021).</p> <p>The similarity in weather conditions between the 2017 study period and each of the five 2018 weather clusters was determined via NMDS of the weather data in two dimensions with Euclidean distance. Centroid coordinates for each 2018 weather cluster and the 2017 data were extracted and pairwise distances calculated between weather clusters:</p> <p> </p> <p>In order, the most proximate weather clusters to the 2017 weather data were HPR (mean Euclidean distance = 8.845), HOT (9.290), WMD (13.626), DWI (13.817) and WLD (18.682; Figure S3).</p> <p>To facilitate comparison between the two years, observed/expected values from the 2018 prey choice models were extracted separately for each of the weather clusters and scaled between 0.1 and 1. For this, 0.1 was used as a minimum since 0 would result in interactions being excluded altogether in the null models, and one as a maximum given the limits of econullnetr but also because this is a multiplier applied to the prey abundances, so greater values would skew prey abundances beyond realistic proportions. Scaling was achieved by the following equation:</p> <p> </p> <p>Missing values (e.g., prey that were absent in certain weather conditions) were represented as 1 to prevent transformation of their abundances in the null models; this treats prey for which data were absent naively, but could increase perceived preferences for them. The scaled values were used to weight the abundance of prey available to the spiders in the 2017 data using the weighting option in econullnetr, whereby values less than 1 proportionally reduce the probability of that taxon being predated in the null models. This effectively redistributes the 2017 relative prey abundance data according to the preference effect sizes generated for each of the 2018 weather clusters. If prey preferences are similar between the 2017 spiders and those from the weather cluster being used to weight the model, the composition of simulated diets should more closely resemble observed diets and fewer significant deviations from the null model should be found.</p> <p>Null models were generated as above (<em>Variation in spider prey choice across weather conditions</em>) but based on the prey availability and trophic interactions from 2017 samples. Three types of model were run: i) a conventional model based on observed prey abundances; ii) a model with prey abundances set to be equal across all prey taxa; and iii) observed prey abundances weighted by prey preferences determined for each of the weather clusters in the 2018 prey choice analysis. A separate model was run for each 2018 weather cluster with abundances weighted by the corresponding scaled observed/expected values. The unweighted conventional model was compared against weighted models to ascertain whether the prey preference weightings from 2018 improved the predictive power of the null models. To compare effect sizes between the unweighted and each other null model for each resource taxon, mean standardised effect size (SES) values were calculated from the paired ‘pre-harvest’ and ‘post-harvest’ data from each model, and paired <em>t</em>-tests were carried out with these between the unweighted and each weighted model. The SES values were plotted for each model and joined between taxa to visualise these paired differences using ‘ggplot’ (Wickham, 2016). Null model-predicted trophic interactions were generated via a modified ‘econullnetr’ function (generate_null_net_indiv) which produces outputs at the individual level to generate simulated diets for individual spiders to compare dietary composition between null model predictions and observed data. These models were run with 2300 simulations to represent 50 simulations per individual spider in the 2017 dataset (n = 46). Null diets were associated with sample IDs by aggregating the 50 simulations per sample and retaining a mean incidence of prey (i.e., mean occurrence across all 50 simulations). A visualisation of the per-sample differences in null model and observed data was generated via NMDS. Mean centroid coordinates for the observed 2017 data and the predicted diets of each model were extracted and the Euclidean distance between the observed data centroid and that of each model was calculated (as above for weather conditions).</p> <p> </p> <p><strong>Supplementary Information 1: Field collection and sample processing</strong></p> <p>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018 (five visits per week of which spiders from 24 collection dates were used). Transects were randomly distributed across the entire field. Along these transects, 64 separate 4 m<sup>2</sup> quadrats, at least 10 m apart, were searched and all observed linyphiids and lycosids were collected. Spiders were placed in 100 % ethanol using an aspirator, regularly changing meshing to limit potential cross-contamination. Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground (mm) and its approximate dimensions were recorded, the latter calculated as approximate web area (mm<sup>2</sup>). Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until DNA extraction.</p> <p>To obtain data on local prey density, ground and crop stems were suction sampled using a ‘G-vac’ for approximately 30 seconds at each 4 m<sup>2</sup> quadrat (n = 64) from which spiders were collected. The collected material was emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab. All invertebrates were identified to family level to match the resolution of the least resolved of the metabarcoding-derived trophic interaction data, and due to difficulties associated with identification to finer taxonomic resolution for many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level, and wasps of the superfamily Ichneumonoidea which were identified no further due to obscurity of wing venation due to damage.</p> <p> </p> <p><strong>Supplementary Information 2: Molecular analysis and bioinformatics</strong></p> <p><em>Extraction and high-throughput sequencing of spider gut DNA</em></p> <p>Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera <em>Erigone</em>, <em>Tenuiphantes</em>, <em>Bathyphantes</em> and <em>Microlinyphia </em>(Araneae: Linyphiidae), and the Lycosidae genus <em>Pardosa</em>. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key (Roberts, 1993). Abdomens were removed from spiders and again transferred to and washed in fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens (Krehenwinkel et al., 2017).</p> <p>For amplification of DNA, two primer pairs were used. BerenF-LuthienR (Cuff et al., 2021) amplified a broad range of invertebrates including spiders, and TelperionF-LaureR (Cuff et al., 2022), amplified a range of invertebrates but fewer spiders. Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively.</p> <p>Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet <em>et al. </em>(2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads).</p> <p><em>Bioinformatic analysis</em></p> <p>Bioinformatic analysis followed Drake et al., (2022). The Illumina run generated 11,165,405 and 10,959,010 reads for BerenF-LuthienR and TelperionF-LaureR, respectively, which were quality-checked and paired via FastP (Chen et al., 2018) to retain only sequences of at least 200 bp with a quality threshold of 33, resulting in 10,561,874 and 9,355,112 paired reads. The paired reads were demultiplexed and assigned to their respective spider sample according to their MID-tags via the “trim.seqs” command in Mothur v1.39.5 (Schloss et al., 2009), leaving 7,854,610 and 7,437,929 reads with exact matches to the primer and MID-tags.</p> <p>Replicates were removed, and denoising and clustering to zero-radius operational taxonomic units (ZOTUs; clustered without % identity to avoid multiple species represented within a single operational taxonomic unit (OTU)) completed via Unoise3 in Usearch11 (Edgar, 2010). The resultant sequences were assigned a taxonomic identity from GenBank via BLASTn v2.7.1 (Camacho et al., 2009) using a 97 % identity threshold (Alberdi et al., 2017). The BLAST output was analysed in MEGAN v6.15.2 (Huson et al., 2016). Where the top BLAST hit, determined by lowest e-value, was resolved at a higher taxonomic level than species-level, the results were checked; where possibly erroneous entries were preventing species-level assignment (e.g., poorly resolved identifications on GenBank), finer resolution was assigned based on the next-closest match. Where ZOTUs were assigned the same taxon, these were aggregated.</p> <p>Data clean-up used the optimal minimum sequence copy thresholds identified by Drake et al. (2022). The maximum value for a ZOTU present in blank or negative controls was identified and subtracted from all read counts for that ZOTU to remove background contaminants. Simultaneously, known lab contaminants (e.g., German cockroach <em>Blattella germanica</em>), artefacts and errors of the sequencing process, unexpected reads in positive controls and positive control taxon reads in dietary samples were identified. These were calculated as a percentage of their respective sample’s read count and any read counts lower than the highest of these percentages for their respective sample were removed to eliminate additional instances of contamination. These thresholds were defined as 0.38 % and 0.39 % for BerenF-LuthienR and TelperionF-LaureR, respectively. The data from the two libraries (i.e., from each primer pair) were then aggregated together by sample and aggregated again by taxon. Non-target taxa (e.g., fungi) and instances in which predator DNA was amplified (i.e., ZOTUs with high read counts matching the individual’s morphological identity) were removed. All remaining read counts were converted to presence-absence.</p> <p> </p> <p><strong>Supplementary Information 3: Cluster analysis</strong></p> <p>Prior to clustering, weather variables were scaled by subtracting the mean and dividing by the standard deviation. A Euclidean distance matrix was calculated using the ‘dist’ function, and this scaled distance matrix was hierarchically clustered using the ‘hclust’ function. Optimal clustering solutions were determined by comparison of Dunn’s index between methods and <em>k</em> values; this was calculated using the ‘dunn’ function in the “clValid” package (Brock et al., 2008) for each cluster <em>k</em> value above five until the Dunn index decreased. The <em>k</em> value after which Dunn’s index decreased was deemed the optimal solution for each clustering method. Clustering methods based on ‘average’, ‘complete’, ‘single’, ‘median’, ‘centroid’ and ‘mcquitty’ linkages were compared, and the ‘complete’ method selected for subsequent analysis as it resulted in the smallest number of clusters (6; thus, the most efficient simplification of the data; Figure S1). Different clustering methods altered the composition of some clusters, but most sampling dates showed consistent clustering between methods.</p> <p>A heatmap dendrogram was produced using the ‘heatmap.2’ function in the ‘gplots’ package (Warnes et al., 2020), with cluster colours assigned with the ‘Accent’ palette of ‘RColorBrewer’ (Neuwirth, 2014) and relative weather value colour scaling generated using the ‘viridis’ package (Garnier 2018; Figure S2). Weather clusters were named according to unique characteristics relative to the other clusters. These names comprise: High Pressure (HPR; days 142, 253, 256, 250, 173), Hot (HOT; days 162, 204, 205, 208, 201, 187, 198, 197, 183, 184, 194, 190 and 191), Wet Low Dewpoint (WLD; day 121), Dry Windy (DWI; days 131, 169 and 170), Wet Moderate Dewpoint (WMD; days 149 and 152), and 2017 (pre- and post-harvest 2017 sampling periods).</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Alberdi, A., Aizpurua, O., Gilbert, M. T. P., & Bohmann, K. (2017). Scrutinizing key steps for reliable metabarcoding of environmental samples. <em>Methods in Ecology and Evolution</em>, <em>9</em>(1), 1–14. https://doi.org/10.1111/2041-210X.12849</p> <p>Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). clValid: an R package for cluster validation. <em>Journal of Statistical Software</em>, <em>25</em>(4), 1–22.</p> <p>Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009). BLAST+: architecture and applications. <em>BMC Bioinformatics</em>, <em>10</em>, 1–9. https://doi.org/10.1186/1471-2105-10-421</p> <p>Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). Fastp: An ultra-fast all-in-one FASTQ preprocessor. <em>Bioinformatics</em>, <em>34</em>(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560</p> <p>Cuff, J. P., Drake, L. E., Tercel, M. P. T. G., Stockdale, J. E., Orozco-terWengel, P., Bell, J. R., Vaughan, I. P., Müller, C. T., & Symondson, W. O. C. (2021). Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. <em>Ecological Entomology</em>, <em>46</em>(2), 249–261.</p> <p>Cuff, J. P., Tercel, M. P. T. G., Drake, L. E., Vaughan, I. P., Bell, J. R., Orozco-terWengel, P., Müller, C. T., & Symondson, W. O. C. (2022). Density-independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops. <em>Environmental DNA</em>, <em>4</em>(3), 549–564.</p> <p>Drake, L. E., Cuff, J. P., Young, R. E., Marchbank, A., Chadwick, E. A., & Symondson, W. O. C. (2022). An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. <em>Methods in Ecology and Evolution</em>, <em>13</em>(3), 694–710.</p> <p>Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. <em>Bioinformatics</em>, <em>26</em>(19), 2460–2461. https://doi.org/10.1093/bioinformatics/btq461</p> <p>Garnier, S. (2018). <em>viridis: default color maps from ‘matplotlib’</em> (0.5.1). https://cran.r-project.org/package=viridis</p> <p>Huson, D. H., Beier, S., Flade, I., Górska, A., El-Hadidi, M., Mitra, S., Ruscheweyh, H. J., & Tappu, R. (2016). MEGAN Community Edition - interactive exploration and analysis of large-scale microbiome sequencing data. <em>PLoS Computational Biology</em>, <em>12</em>(6), 1–12. https://doi.org/10.1371/journal.pcbi.1004957</p> <p>Krehenwinkel, H., Kennedy, S., Pekár, S., & Gillespie, R. G. (2017). A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. <em>Methods in Ecology and Evolution</em>, <em>8</em>, 126–134. https://doi.org/10.1111/2041-210X.12647</p> <p>Neuwirth, E. (2014). <em>RColorBrewer: ColorBrewer palettes</em> (1.1-2). https://cran.r-project.org/package=RColorBrewer</p> <p>Roberts, M. J. (1993). <em>The Spiders of Great Britain and Ireland (Compact Edition)</em> (3rd ed.). Harley Books.</p> <p>Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., Sahl, J. W., Stres, B., Thallinger, G. G., Van Horn, D. J., & Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. <em>Applied and Environmental Microbiology</em>, <em>75</em>(23), 7537–7541. https://doi.org/10.1128/AEM.01541-09</p> <p>Taberlet, P., Bonin, A., Zinger, L., & Coissac, E. (2018). <em>Environmental DNA</em>. Oxford University Press.</p> <p>Warnes, G. R., Bolker, B., Bonebakker, L., Gentleman, R., Huber, W., Liaw, A., Lumley, T., Maechler, M., Magnusson, A., Moeller, S., Schwartz, M., & Venables, B. (2020). <em>gplots: Various R programming tools for plotting data</em> (R package version 3.1.0). https://cran.r-project.org/package=gplots</p>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
High Temporal Resolution Records of Hansbreen Ice Flow Velocity for Years 2006-2019
<p>This repository contains the datasets of the positions of 16 mass balance stakes, horizontal velocity (m/yr) and accuracy of velocity (m/yr) for Hansbreen, a tidewater glacier in southern Svalbard. Data were derived from GNSS measurements conducted in the period 2006-2019. Stake positions are given in UTM zone 33X, and elevation in geoidal height (EGM96). Additionally, we provide files with annual, summer and winter velocities (m/yr) with a standard deviation of velocity, estimated for the hydrological year. The file „Hansbreen_preprocessing_code_stakes.zip” contains the code used for the velocity estimation.</p>
Temporal Validity Change Prediction - Dataset
<p>This dataset contains data for <em>temporal validity change prediction</em>, an NLP task that will be defined in an upcoming publication. The dataset consists of five columns. </p> <ul> <li>target - A Tweet ID. This column must be manually rehydrated via the Twitter API to obtain the tweet text.</li> <li>follow_up - A synthetic follow-up tweet that semantically relates to the target tweet.</li> <li>context_only_tv - The expected temporal validity duration of the <strong>target </strong>tweet, when read in isolation.</li> <li>combined_tv - The expected temporal validity duration of the <strong>target </strong>tweet, when read <strong>together with the follow-up tweet</strong>.</li> <li>change - The TVCP task label, i.e., whether the temporal validity duration of the target tweet is <em>decreased</em>, unchanged (<em>neutral</em>), or <em>increased </em>by the information in the follow-up tweet.</li> </ul> <p>The duration labels (context_only_tv, combined_tv) are class indices of the following class distribution:<br> [no time-sensitive information, less than one minute, 1-5 minutes, 5-15 minutes, 15-45 minutes, 45 minutes - 2 hours, 2-6 hours, more than 6 hours, 1-3 days, 3-7 days, 1-4 weeks, more than one month]</p> <p>Different dataset splits are provided.</p> <ul> <li>"dataset.csv" contains the full dataset.</li> <li>"train.csv", "val.csv", "test.csv" contain an 80-10-10 train-val-test split.</li> <li>"train[0-4].csv" and "test[0-4].csv" respectively contain training and test data for one of 5 folds for 5-fold cross-validation. The train file contains 80% of the data, while the test file contains 20%. To replicate the original experiments, the train file should be sorted by the preprocessed target tweet text, then the first 12.5% of target tweets should be sampled to generate validation data, leading to a 70-10-20 train-val-test split. </li> </ul>
TempTabQA: Temporal Question Answering for Semi-Structured Tables
<p>This repository contains resources, namely <strong>TempTabQA</strong>, developed for the paper: Gupta, V., Kandoi, P., Vora, M., Zhang, S., He, Y., Reinanda R., Srikumar V., <i>TempTabQA: Temporal Question Answering for Semi-Structured Tables</i>. In: Proceeding of the The 2023 Conference on Empirical Methods in Natural Language Processing, Dec 2023.</p><p><strong>TempTabQA</strong> is a dataset which comprises 11,454 question-answer pairs extracted from Wikipedia Infobox tables. These question-answer pairs are annotated by human annotators. We provide two test sets instead of one: the <strong>Head</strong> set with popular frequent domains, and the <strong>Tail</strong> set with rarer domains. </p><p>Files to access the annotation follow the below structure:</p><p>Maindata</p><ul><li>qapairs: split into train, dev, head, and tail sets, in both csv and json formats</li><li>Tables: Wikipedia category and tables metadata in csv, json and html formats</li></ul><p>Carefully read the ```LICENCE``` for non-academic usage.</p><p><i>Note : Wherever required consider the year of 2022 as the build date for the dataset.</i></p><p> </p><p> </p><p> </p>
Temporal heterogeneity increases with spatial heterogeneity in ecological communities
Heterogeneity is increasingly recognized as a foundational characteristic of ecological systems. Indeed, spatial heterogeneity is commonly used in alternative state theory as an early indicator of regime shifts. To evaluate if spatial heterogeneity of communities is a predictor of temporal heterogeneity, we used mixed effects models to synthesize 68 community datasets spanning freshwater and terrestrial systems where measures of species abundance were replicated over space and time. Overall, we found a significant positive relationship between spatial and temporal heterogeneity across all ecosystems. In addition, lifespan and successional stage were related to temporal heterogeneity. Therefore we found evidence that spatial heterogeneity is a potential tool to predict temporal heterogeneity in ecological communities. This data package consists of six files. First we used a (1) R script to derive community dynamic metrics from source files to calculate (2) spatial and temporal heterogeneity over time as well as other measures of the community. We used this derived dataset to run analyses (3) with a R script to study the relationship between spatial and temporal heterogeneity communities. These analyses resulted in three figures, (4) the overall relationship between spatial and temporal heterogeneity, (5) output of mixed models investigating how experimental and biological factors affect this relationship, and (6) figures exploring how lifespan of the study organism affects the relationship between spatial and temporal datasets.
LAGOS-NE-GIS v1.0: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 2013-1925
This data package, LAGOS-NE-GIS v1.0, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO v1.087.1: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE GIS v1.0 module includes GIS datasets for: lake polygons and their hydrologic classification; wetland polygons and their classification; streams as a line coverage and their classification by stream order; the zones used for this study (state and county; hydrologic units [at the 4, 8 and 12 scales]); and, lake watersheds (IWS). We also include boundaries of U.S. stat
Spatial and temporal distribution and abundance of moths in the Andrews Experimental Forest, 1994 to 2008
The distribution and abundance of macromoth species is strongly influenced by geographical (region-neighboring plots) scale, elevation, aspect, plant community, management regime, and time of year. Noctural macromoths have been observed at a total of 263 sample sites throughout the Andrews Forest watershed since 1994. Only a limited subset of these sites is sampled each year. From 2004 to 2008, 20 sites were sampled consistently using a hierarchical sampling design stratified by elevation and vegetation type. Moths are sampled using blacklight traps deployed for one night every two weeks at each site from April through October. A total of 503 species have been observed, and approximately 300 species may be observed in any given year. The watershed can be divided into 13 distinct zones. The northwest ridge above the Andrews headquarters has the highest number of species (n = 321) and the lowest number of species occurred at upper Lookout Creek (n = 239). Each of 13 zones is missing ca. 200 of the 500 resident species, suggesting that heterogeneity in the landscape is important. A breakdown of the species into functional groups based on larval feeding habits: conifers, hardwood, herb, mix, unknown shows that 43% of Andrews species rely on a hardwoods and 63% rely on hardwoods and herbaceous angiosperms. Conifer-feeders only represent 8% of moth species. However, moths associated with conifer hosts are the most abundant; for instance, in the zone representing the midlevel of Carpenter Mountain 67% of moth individuals are conifer feeders, but only 14% of the species feed on conifers. In contrast, within the zone represented by the Headquarters site, only 32% of the individual moths feed on conifers whereas 56% feed on hardwoods. Moth biogeographic zones correspond to elevation zones and to potential vegetation.
Spatial and temporal distribution and abundance of butterflies in the Andrews Experimental Forest, 1994-1996
This database contains information on species abundance according to date and location within the H.J. Andrews Experimental Forest Lookout Creek watershed. The database provides the information needed to assess patterns in the abundance of butterflies across time and space. The distribution and abundance of butterfly species on the Andrews Forest is strongly influenced by geographical scale, elevation, aspect, plant community, management regime, and time of year. Patterns of distribution and abundance are based on an historical total of 80 species, of which 73 are resident species and about 55 of which may be observed in any given year. Butterflies were surveyed at two- week intervals from late April through early October over a three-year period (1994-6). Approximately one-third of the watershed was covered during each visit, thus each area was sampled at about 6 week intervals within each sample season.
Modeling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin , Alaska, 1921 to 2100.
Output data set of the MBL-GEM III model run for tussock tundra in the Kuparuk River Basin, Alaska, described in detail in Le Dizès, S., B. L. Kwiatkowski, E. B. Rastetter, A. Hope, J. E. Hobbie, D. Stow, and S. Daeschner, Modeling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin (Alaska), J. Geophys. Res., 108(D2), 8165, doi:10.1029/2001JD000960, 2003. We ran the model at a 10 km x 10 km resolution for 123 cells at a yearly time step for 180 years, from 1921 to 2100. Two scenarios enabled the investigation of the effects of two opposing climate change scenarios for the 2001-2100 future period: warmer and wetter ("wet scenario" or Scenario 1) and warmer and drier ("dry scenario" or Scenario 2). These 246 files contain all simulation results for each scenario for individual cells in the Kuparuk River basin.
Identifying the drivers and responses of abrupt changes across spatial and temporal scales in ecology: a review
Recently, the theoretical basis for understanding abrupt changes in ecosystems relative to regime shifts has emerged (Ratajczak et al. 2018). Abrupt changes are defined as, “substantial changes in the mean or variability of a system that occur in a short period of time relative to typical rates of change” (Ratajczak et al. 2018). Despite a driver-response framework to guide the environmental conditions under which abrupt changes are likely to occur coupled with many examples of unexpected changes from long-term ecological research, our theoretical basis of understanding of abrupt changes doesn’t include long-term scales, variability in drivers and responses, changes in the magnitude or direction of drivers, or the interactions among multiple drivers across spatiotemporal scales (sensu Ratajczak et al. 2018). Further, a critical review of the literature is lacking and essential to further understanding how common abrupt changes are detected and reported, as well as patterns and scales of drivers and responses of abrupt change in ecosystems. To address this knowledge gap, we searched the existing ecological literature for evidence and commonalities of abrupt change across ecosystems to identify commonalities and differences of abrupt change drivers and responses across terrestrial, freshwater, and marine ecosystems. We specifically asked the following questions: (1) How common are abrupt changes reported in the ecological literature? (2) How do driver and response temporal and spatial scales of abrupt changes compare and vary across terrestrial, freshwater, and marine ecosystem types? (3) Is there relative congruence between the temporal and spatial scale of drivers and responses? (3) What are common types of drivers and responses to abrupt changes, and how do they vary across ecosystem types? (4) What terms are most associated with drivers and responses of abrupt changes among ecosystem types?
A role for the medial temporal lobe subsystem in guiding prosociality: the effect of episodic processes on willingness to help others
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Learning Naturalistic Temporal Structure in the Posterior Medial Network
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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.