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122 results for “temporal diversity”
Data: Disentangling drivers of temporal changes in urban pond macroinvertebrate diversity
<p>Data for: (i) presence and abundance of Odonata and Trichoptera (larvae), and Coleoptera and Hemiptera (larvae and adults) species in ponds in Stockholm, Sweden, in 2014 and 2019, (ii) environmental data 2014 and 2019 (pond data like water chemistry, and land-change data), (iii) coordinates of ponds and pond area, (iv) and R script to reproduce analyses presented in Granath et al. 2024 (Urban Ecosystems, https://doi.org/10.1007/s11252-023-01500-2). A meta-data file with descriptions of the data files is also included.</p>
Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea
<p>Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea</p>
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>
Data from: Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior
<p>Animals rely on their experience to guide their next choice. In foraging-type tasks guided by history-dependent value, these experiences are typically integrated such that the weights of past events initially decay quickly over time but show a longer tail than expected by exponential decay. Rather, such integration is better described by a hyperbolic function. Hyperbolic integration affords sensitivity to both recent environmental dynamics and long-term trends, however the mechanism by which the brain implements this hyperbolic integration is unknown. We trained mice on a history-dependent, value-based decision task and found that the mice indeed showed hyperbolic decay on their weighting of past experience. However, the activity of history-encoding cortical neurons showed weighting with exponential decay. In resolving this apparent mismatch, we observed that cortical neurons encode history information heterogeneously across a wide variety of exponential time-constants, with the retrosplenial cortex (RSC) overrepresenting longer time-constants compared to other areas. A model that combines these diverse timescales of exponential history integration can recreate the heavy-tailed, hyperbolic history integration observed in behavior. In particular, time-constants of RSC neurons best matched the behavior, and optogenetic inactivation of RSC uniquely reduced the use of history information. These results indicate that behavior-relevant history information is maintained in neurons across multiple timescales in parallel, and suggest that the neural population in RSC is a critical reservoir of this information guiding decision-making.</p>
Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea [Supplementary Material]
<p>Supplementary Material of the PhD [Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea [Supplementary Material]</p> <p> </p>
Abundance-diversity relationship as a unique signature of temporal scaling in the fossil record
<p>Species diversity increases with the temporal grain of samples according to the species-time relationship, impacting paleoecological analyses because the temporal grain (time averaging) of fossil assemblages varies by several orders of magnitude. We predict a positive relation between total abundance and sample size-independent diversity (ADR) in fossil assemblages because an increase in time averaging, determined by a decreasing sediment accumulation, should increase abundance and depress species dominance. We demonstrate that, in contrast to negative ARDs of non-averaged living assemblages, the ARD of Holocene fossil assemblages is positive, unconditionally or when conditioned on the energy availability gradient. However, the positive fossil ADR disappears when conditioned on sediment accumulation, suggesting that ADR can be a signature of diversity scaling induced by variable time averaging. Conditioning ADR on sediment accumulation can identify and remove the scaling effect caused by time averaging, providing an avenue for unbiased biodiversity comparisons across space and time.</p>
Data from: Temporal changes in taxonomic and functional alpha and beta diversity across tree communities in subtropical Atlantic forests
<h2><strong>The study is published in Oikos and available at: <a href="https://doi.org/10.1111/oik.10961">https://doi.org/10.1111/oik.10961</a></strong></h2> <p>Here we aim to assess temporal taxonomic and functional alpha and beta diversity of adult and juvenile tree communities across 11 sites in the subtropical Brazilian Atlantic Forest to infer about trends and drivers of biodiversity change. The tree communities were evaluated for temporal changes in: (1) taxonomic and functional alpha diversity, (2) taxonomic and functional composition (beta diversity), and (3) identifying potential abiotic and biotic drivers of these changes, considering three censuses across a period of 10 years.</p> <p> </p> <h2>Files description:</h2> <p><strong>traits-adults.csv</strong> - adult tree species and their functional traits values.</p> <p><strong>traits-juveniles.csv</strong> - juvenile tree species and their functional trait values.</p> <p><strong>abundance-adults_synthesis.csv</strong> - adult tree species abundance over the three time periods of forest surveys (T1, T2, and T3). Raw data on tree individual level is available at ForestPlots.net database (<a href="https://forestplots.net/">https://forestplots.net/</a>) under request.</p> <p><strong>abundance-juveniles_synthesis.csv</strong> - juvenile tree species abundance over the three time periods of forest surveys (T1, T2, and T3). Raw data on tree individual level is available at ForestPlots.net database (<a href="https://forestplots.net/">https://forestplots.net/</a>) under request.</p> <p>Functional traits abbreviations are defined as follows: LA = leaf area; SLA = specific leaf area; WD = wood density; SM = seed mass; range_temp = range of mean annual temperature; range_CWD = range of climatological water deficit; and biomes_distrib = number of Brazilian biomes that the species occur according to Flora and Funga do Brazil.</p> <p> </p> <h2><strong>Acknowledgments</strong></h2> <p>This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Finance Code 001, through Portal de Periódicos and scholarships granted to JMFK, JK and RCP. The fieldwork was supported by Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS grant numbers 2218 – 2551/12-2 and 19/2551-0001698-0), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq/FAPERGS/PELD number 441590/2020-9), and the Instituto Nacional de Ciência e Tecnologia (INCT) in Ecology, Evolution and Biodiversity Conservation, supported by MCTIC/CNPq (grant number 465610/2014-5). KMB gratefully acknowledge the financial support by the National Institute of Science and Technology in Low Carbon Emission Agriculture (INCT-ABC) sponsored by Brazil’s National Council for Scientific and Technological Development (CNPq, grant no. 406635/2022-6), the Foundation for Research Support of the State of Rio Grande do Sul (Fapergs, grant no. 22/2551-0000392-3), and the Ministry of Agriculture (MAPA). SCM is supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; grant number 309659/2019-1.</p> <p> </p> <h3><strong>Please find below the data used in the study.</strong></h3>
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 4 in Diversity and temporal variation of brown lacewings (Neuroptera, Hemerobiidae) from Atlantic rainforest areas in southeastern Brazil
Figure 4 Habitus of collected species of Megalomus Rambur, 1842 (Neuroptera, Hemerobiidae) and their geographical distribution in Neotropics; red circles = previous records, red stars = new records. A-B, M. impudicus (Gerstaecker, 1888). C-D, M. rafaeli Penny & Monserrat, 1985. E-F, M. ricoi Monserrat, 1997.
Figure 10 in Diversity and temporal variation of brown lacewings (Neuroptera, Hemerobiidae) from Atlantic rainforest areas in southeastern Brazil
Figure 10 Species distributions of Hemerobiidae (Neuroptera) along altitudinal gradient in five areas in the Atlantic rainforest of São Paulo state, Brazil, collected between October 2009 and December 2011. asl = above sea level, PEI = Parque Estadual Intervales, PEMD = Parque Estadual Morro do Diabo, PESM/NSV = Parque Estadual da Serra do Mar, Núcleo Santa Virgínia, PESM/NP = Parque Estadual da Serra do Mar, Núcleo Picinguaba and EEJI = Estação Ecológica Juréia-Itatins.
Figure 1 in Diversity and temporal variation of brown lacewings (Neuroptera, Hemerobiidae) from Atlantic rainforest areas in southeastern Brazil
Figure 1 Map of Brazil with the original extension of the Atlantic rainforest biome in black color and map of the São Paulo state with the collection sites. PEI = Parque Estadual Intervales, PEMD = Parque Estadual Morro do Diabo, PESM/NSV = Parque Estadual da Serra do Mar, Núcleo Santa Virgínia, PESM/NP = Parque Estadual da Serra do Mar, Núcleo Picinguaba and EEJI = Estação Ecológica Juréia-Itatins. Image sources: www.wwf.org.br and Google Earth.
Figure 2 in Diversity and temporal variation of brown lacewings (Neuroptera, Hemerobiidae) from Atlantic rainforest areas in southeastern Brazil
Figure 2 Habitus of collected species of Hemerobius Linnaeus, 1758 (Neuroptera, Hemerobiidae) and their geographical distribution in Neotropics; red circles = previous records, red stars = new records. A-B, H. cubanus Banks, 1930. C-D, H. edui Monserrat, 1991. E-F, H. gaitoi Monserrat, 1996.
Figure 6 in Diversity and temporal variation of brown lacewings (Neuroptera, Hemerobiidae) from Atlantic rainforest areas in southeastern Brazil
Figure 6 Habitus of collected species of Sympherobius Banks, 1904 (Neuroptera, Hemerobiidae) and their geographical distribution in Neotropics; red circles = previous records, red stars = new records. A-B, S. ariasi Penny & Monserrat, 1985. C-D, S. mirandus (Navás, 1920).
Figure 8 in Diversity and temporal variation of brown lacewings (Neuroptera, Hemerobiidae) from Atlantic rainforest areas in southeastern Brazil
Figure 8 Genera of Hemerobiidae (Neuroptera) collected monthly with Malaise traps in five areas of Atlantic rainforest of São Paulo State, Brazil, between October 2009 and December 2011. A, Nusalala Navás, 1913. B, Hemerobius Linnaeus, 1758. C, Megalomus Rambur, 1842. D, Notiobiella Banks, 1909. E, Sympherobius Banks, 1904.
Figure 5 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 5 Canonical correlation analysis (CCA) ordering diagram between environmental factors and Anopheles species in the pre (a) and post-construction (b) phases of the Jirau hydroelectric plant: Relative Humidity of the air (R. H%); Temp (Temperature ° C); Subtitle: Anopheles albit – An. albitarsis; Anopheles argyrit – An. argyritarsis; Anopheles benar – An. benarrochi; Anopheles braz – An. braziliensis; Anopheles darl – An. darlingi; Anopheles evan – An.evansae; Anopheles mattog – An. mattogrossensis; Anopheles mediop – An. mediopunctatus; Anopheles osw – An. oswaldoi; Anopheles per – An. peryassui; Anopheles rang – An. rangeli; Anopheles trian – An. triannulatus.
Figure 3 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 3 Density of Anopheles species (x) in the sampled months (January to August) before (a) and after (March to October) the construction (b) of the Jirau hydroelectric w plant, in Rondônia, Brazil.
Figure 1 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 1 Sampling points of anophelines in the area covered by the Jirau hydroelectric plant, in the stretch between the locations of Jaci Paraná and Abunã (squares), in the pre (black) and post-construction (gray) phases.
Figure 2 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 2 Housing types (a-d) spatial variation of Anopheles darlingi before (e) and after (f) the construction of the Jirau hydroelectric plant, in Rondônia, Brazil. Subtitle: AB – Abunã; JHP – Jirau Hydroeletric Plant; JP – Jaci Paraná; NMP – Nova Mutum Paraná.
Fig. 5 in Spatio-temporal distribution of Anastrepha fraterculus and Ceratitis capitata (Diptera: Tephritidae) captures and their relationship with fruit infestation in farms with a diversity of hosts
Fig. 5. Fruit infestation and population fluctuation of Anastrepha fraterculus in (A) pears, (B) peaches, and (C) mandarins. Cn = Canelones, Py = Paysandú; FTD = flies per trap per d; Af = Anastrepha fraterculus.
Fig. 6 in Spatio-temporal distribution of Anastrepha fraterculus and Ceratitis capitata (Diptera: Tephritidae) captures and their relationship with fruit infestation in farms with a diversity of hosts
Fig. 6. Population fluctuation of Ceratitis capitata males (M) in Jackson traps (Cc) and females (H) in McPhail traps. A, B, C, D = cultivars where fruit infestation was recorded (A, B = Canelones; C, D = Paysandú); E, F, G, H = cultivars where no fruit infestation was recorded (E, F = Paysandú; G, H = San José).
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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.