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2,009 results for “Disruptions”
Data associated with "A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations"
<p><strong>Datasets used in <em>A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations.</em></strong></p> <p><strong>Datatypes:</strong></p> <ol> <li>Multi-index pandas dataframe (.pkl)</li> <li>Numpy array (.npy)</li> <li>Collection of numpy arrays (.npz)</li> <li>Python dictionary objects (.pkl)</li> </ol> <p><strong>Datasets:</strong></p> <p><strong>alignments.pkl: A dataframe containing numpy arrays of image displacements for each mouse in each memory context.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are ['T', 'F1', 'N1', 'F2', 'N2'] for the training, recall 1-hour, neutral, recall 1-day, neutral day 2 memory contexts respectively. Each element of this dataframe is a numpy array of shape images x 2 that hold x and y image displacements respectively. These alignments are computed after the inscopix software motion correction and are used in Supplemental Figure 2 of the paper.</p> <p><strong>behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id.The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context (*) in ('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2').</p> <p><strong>correlated_pairs_df.pkl: A dataframe containing arrays of neuron indices that have a correlation in activity pattern > 0.3.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a numpy array with three columns. The first two columns are the neuron indices that are correlated and the last column is the strength of the correlation.</p> <p><strong>dredd_freezes_df.pkl: A dataframe containing freezing percentages for SOM-Cre and RTT-SOM-Cre mice treated with DREADDS.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment (mcherry, hm3d, hm4d). The columns contain one of ['Neutral', 'Fear', 'Fear_2']. Each element of the dataframe is a freezing percentage for a single mouse. This dataframe is built from reading the dredd_behavior.xlsx excel file. This is used to generate figure 5E of the paper.</p> <p><strong>high_degree_df.pkl: A dataframe containing list of high degree neuron indices.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'=not applicable since no DREADD used). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a list of neuron indices that are high-degree cells.</p> <p><strong>N006_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N006 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Supplemental Figure 2 of the paper.</p> <p><strong>N006_wt_cxtbasis.pkl: A dictionary containing arrays for basis images and singular values for each context.</strong></p> <p>This dictionary has keys, ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing the memory contexts. Each value is a 2 element list containing the U-basis images as column vectors and singular values, one per basis image in U. The shape of the basis images is the same shape stored in N006_wt_basis.pkl. This dataset is used in Supplementary Figure 2 to track cells across contexts of the CFC task (see also N006_wt_cxtsources.pkl)</p> <p><strong>N006_wt_cxtsources.pkl: A dictionary containing the independent component source images computed from the basis images for automatically identifying regions of interest (ROIs). </strong></p> <p>The dictionary is keyed on ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] contexts. Each value in the dictionary at a given key is a 3-D numpy array of shape sources x height x width. These data were used to construct the source images and max intensity projection image of the sources in Supplemental Figure 2F-J of the paper.</p> <p><strong>N006_wt_rois.pkl: A dictionary containing the boundaries and annuli coordinates of all rois for mouse N006 of genotype wild-type.</strong></p> <p>This dictionary is keyed on ['boundaries', 'annuli'] contexts and each value is a 179 element list of arrays of boundary line coordinates or annulus point coordinates one per ROI detected for this mouse.</p> <p><strong>N006_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N006 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=205 source images, height=517 pixels and width=704 pixels. This data was used to construct Supplemental Figure 3F.</p> <p><strong>N019_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N019 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 1C of the paper.</p> <p><strong>N019_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=204 source images, height=516 pixels and width=698 pixels. This data was used to construct Figure 1C of the paper.</p> <p><strong>P80_animals.pkl: A pandas multi-index object containing the genotype, mouse_id and treatment of the top 80% behavioral performance animals.</strong></p> <p>In this study, we drop the lowest 20% performing WT and RTT animals based on freezing percentage during the recall contexts. This multi-index is used to filter the data before each computation or plot in this study. So for example Figure 1B contains only the top 80% performing WT and RTT mice.</p> <p><strong>pc_sipscs_amps.pkl: A dictionary containing the amplitudes of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC amplitudes, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>pc_sipscs_freqs.pkl: A dictionary containing the frequencies of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC frequencies, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>rois_df.pkl: A multi-index dataframe containing all ROI information for each non-DREADD treated cell in this study (Figures 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0. The columns are ['centroid', 'cell_boundary', 'annulus_boundary']. The centroid for each cell is a 2-tuple of row, column pixel centroid coordinates. The cell_boundary is a two-column array of row, col boundary points for each ROI. The annulus_boundary is a two-column array of row, column interior points in the annulus. The annulus region excludes points of overlap with nearby cell bodies (See STAR methods of the paper).</p> <p><strong>signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD experiments used in this study (Figs 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 5771 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This dataframe is used in the construction of Figures 1-3 in the paper.</p> <p><strong>som_behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context in *=('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'). This dataframe was not used in the paper but may still be useful for further analysis.</p> <p><strong>som_sepsc_amplitudes:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC amplitudes. This data was used in Figure 4E-G.</p> <p><strong>som_sepsc_freqs:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC frequencies. This data was used in Figure 4E-G.</p> <p><strong>som_signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD SOM cell recordings used in this study (Figs 5).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 710 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This data was used to construct Figure 5B-C.</p> <p><strong>ssn33_sstcre_basis.npz: a dict containing three numpy arrays representing the basis images for mouse ssn33 of genotype sst-cre.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 100 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 5A of the paper.</p> <p><strong>ssn33_sstcre_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=86 source images, height=516 pixels and width=654 pixels. This data was used to construct Figure 5A of the paper.</p>
e-DIPLOMA - Dataset: The values of using disruptive technologies
<p>This is the dataset "The values of using disruption technologies" of the e-DIPLOMA project and includes the following documents:</p> <ul> <li>Values.xlsx</li> <li>Scenarios.ppt</li> <li>ReadmeFile.rtf</li> </ul> <p>The data were collected in the values-workshop. They describe how the practice based example learning scenarios with disruptive technologies are perceived regarding values. The data were collected in workshops held in several European countries. The participants could read about the learning design scenarios with disruptive technologies and then discuss the values they perceived regarding these learning situations.In the workshops the instrument with 45 values names and descriptions was provided to elicit values. The values could be associated with four different learning scenarios with disruptive technologies. The group interview was held at the workshop at 2,5 h, the groups jointly discussed the values. The participants of the group workshop were heterogeneous: students, educators, technical support personnel at institutes, educational technology developers. There were 4 groups with 4-5 persons in each workshop. The values were collected using the anonymous online survey approach. The workshops were conducted in the national languages and data were translated into English.</p>
S109 | PARCEDC | List of 7074 potential endocrine disrupting compounds (EDCs) by PARC T4.2
<p>This is the collection associated with list S109 PARCEDC List of 7074 potential endocrine disrupting compounds (EDCs) by PARC T4.2 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <div>A comprehensive list of 7074 endocrine disruptors compiled by PARC T4.2., integrating assessments by EU and national regulators, contributions from entities like the European Chemicals Agency (ECHA)and complemented by other potential endocrine disruptors (hormones, bisphenols, etc.).The list also includes potentially active endocrine disruptors screened from all substances in the NORMAN SusDat database (https://www.norman-network.com/nds/susdat/) using VEGA (QSAR) EDC prediction models (https://www.vegahub.eu/about-qsar), ToxCast database's in vitro assay data and ToxCast based machine learning predictions.</div> <div> </div> <div>List kindly provided by Sandrine Andres and Valeria Dulio, INERIS.</div>
Data for a publication "Impact disruption of Bjurböle porous chondritic projectile"
<p>This archive contains raw and processed research data and photographic documentation for a publication "<em><strong>Impact disruption of Bjurböle porous chondritic projectile</strong></em>" by Kohout et al. The sample identifiers are consistent with the one used in the publication.</p> <p>Content:</p> <ol> <li><strong>Bjurböle photos.zip</strong>, <strong>Bjurböle photos II.zip</strong>, and <strong>Bjurböle photos III.zip</strong> - Photographs of studied Bjurböle meteorites from Finnish Museum of Natural History as well as from worldwide collections. Unless specified otherwise in the photographs (worldwide collections) the photo credits are Tomas Kohout, Assi-Johanna Soini, Arto Luttinen, University of Helsinki.</li> <li><strong>Meteoriteidentifier.ply</strong> - 3D shape reconstructions of studied Bjurböle and Chelyabinsk meteorites. By default a 3D mesh reconstruction with relaxed fitting was applied to partial aligned laser scans. In certain cases where multiple reconstruction techniques were applied (e.g. volume merge) this is indicated in file name.</li> <li><strong>All tables.xlsx</strong> - Spreadsheet file containing the tables presented in the publication</li> </ol>
Data from: Global climate disruption and regional climate shelters after the Toba supereruption
<p>CESM1.3 simulations for Toba eruption scenarios. Run numbers correspond to scenarios listed in Appendix 1, Table S1 of:</p> <p><strong>Black, Lamarque, Marsh, Schmidt, and Bardeen. Global climate disruption and regional climate shelters after the Toba supereruption. PNAS. DOI: 10.1073/pnas.2013046118</strong></p> <p> </p>
Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions and in Steady-State (dataset)
<p>Database for reproducing the calculations presented in the publication "Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions", submitted to <em>Fusion Engineering and Design</em>.</p> <p>Work carried out within the framework of the EUROfusion Consortium.</p>
S99 | ANSESEDC | List of potential endocrine disrupting compounds (EDCs) from ANSES
<p>This is the collection associated with list S99 ANSESEDC List of potential endocrine disrupting compounds (EDCs) from ANSES on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A list of 908 potential endocrine disrupting compounds (EDCs) from <a href="https://www.anses.fr/en">ANSES, the French Agency for Food, Environmental and Occupational Health & Safety</a> to be assessed for their <a href="http://(https://www.anses.fr/en/content/accelerating-assessment-endocrine-disruptors)">endocrine disrupting properties under France’s Second National Endocrine Disruptor Strategy (SNPE 2)</a>. Endocrine disruptors (EDs) are substances that interfere with the hormonal functions of humans and animals. List kindly provided by Sandrine Andres, INERIS. <br> </p>
Supplemental Data Sets for "Buried Ice Deposits in Lunar Polar Cold Traps were Disrupted by Ballistic Sedimentation"
<p>Supporting Data Sets for manuscript "Buried Ice Deposits in Lunar Polar Cold Traps were Disrupted by Ballistic Sedimentation". Contains Data Sets S1-S7 as described in the manuscript and Supplementary information S1 (see <a href="https://doi.org/10.1029/2022JE007567">https://doi.org/10.1029/2022JE007567</a>).</p>
Dataset for article "From X-rays to physical parameters: a comprehensive analysis of thermal tidal disruption event X-ray spectra"
<p>This repository contains the data used in the modeling of TDE X-ray emission within the article: Mummery et al. 2023, " From X-rays to physical parameters: a comprehensive analysis of thermal<br> tidal disruption event X-ray spectra" published as Mummery et al. 2023, MNRAS, 519, 5828</p>
Space weather disrupts nocturnal bird migration
<p>Our paper tests for the effects of space weather-induced geomagnetic disturbances on radar-detected nocturnal bird migration. We find evidence for a ~10% decrease of migration intensity after controlling for weather variables and spatiotemporal autocorrelation, and also for a decrease in the effort birds spent flying against the wind in the fall, especially under overcast conditions. This repository provides the data and the code used to arrive at these conclusions and plot the main results. Weather radar data was processed from the NOAA NEXRAD network, weather data was accessed from the North American Regional Reanalysis, and magnetometer data was accessed from the SuperMAG inventory. </p>
Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow
This dataset includes data inputs from public sources, scripts and outputs to evaluate riparian vegetation reliance on groundwater across California from 2015 to 2020. This dataset accompanies the Rohde et al. paper titled, Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow. The provided scripts process groundwater, vegetation, climate, and streamflow input data from various sources. Further, all output data and statistical analyses are included.
Fig. 4 in Disruptive coloration and habitat use by seahorses
Fig. 4. Plain-colored (Left column) seahorses occupy similar background color, while banded disruptive seahorses (Right column) occupy more diverse habitats.
Endocrine disruption: the noise in available data adversely impact the models' performance
<p>This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption: the Estrogen (ER) and the Androgen (AR) receptor. The ED-relevant data were retrieved from multiple sources, including the CERAP, CoMPARA, and the Tox21 data challenge projects as well as ChEMBL and PubChem databases. Data analysis performed with the help of Generative Topographic Mapping technique revealed the problem of a low agreement between experimental values issued from different sources.</p> <p>Collected data were used to train both classification models for AR and ER binding activities and regression models for Relative Binding Affinity (RBA) and median Inhibition Concentration (IC50) models. These models displayed relatively poor performance in classification (sensitivities ER = 0.34, AR = 0.49) and in regression (determination coefficient R<sup>2</sup> for the RBA and IC50 models in external validation varied from 0.44 to 0.76). Our analysis demonstrates that low models performances resulted from misinterpreted experimental endpoints or wrongly reported values.</p> <p>Developed models and collected data sets included of 6215 (ER) and 3789 (AR) unique compounds; they are freely available.</p> <p>The repository includes data on estrogen and androgen receptor binding behavior (binder, non-binder), median inhibitory concentration (IC50) and relative binding affinity (RBA). </p> <p><strong>SDF fields:</strong></p> <ul> <li><em>DB</em> = database; where: COMPARA = Collaborative Modelling Project for Androgen Receptor Activity; CERAPP = Collaborative Estrogen Receptor Activity Prediction Project; Tox-DC = data from Tox21 program; PubChem = data from PubChem. </li> <li><em>Set</em> = whether the compound was used in training or test set for the given model</li> <li><em>Receptor</em> = AR stands for Androgen Receptor and ER stands for Estrogen Receptor</li> <li><em>binding_prp</em> = binding behaviour for the classification model (ER and AR). 1 = binder; = non-binder</li> <li><em>IC50 (nM) </em>and <em>logIC50</em> = median inhibitory concentration values in nanoMolar and log.</li> <li><em>RBA(%) </em>and <em>logRBA</em> = relative binding affinity values in % and log.</li> </ul>
Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic - Estimates for Local Authority Districts
<p><strong>Overview:</strong></p> <p>Population estimates from the publication: <em>Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic.</em> </p> <p>Population estimates were aggregated to Local Authority Districts (LADs). </p> <p><strong>Methodology: </strong></p> <p>Population estimates were extracted from Bing Tiles (Zoom Level 12) to 2019 LADs by assigning tiles to LADs by their percent areal overlap. This method assumes constant population distribution across a single Bing Tile.</p> <p>2019 LAD boundaries are available from the <a href="https://geoportal.statistics.gov.uk/datasets/local-authority-districts-december-2019-boundaries-uk-bfc/explore">UK Government Open Geography Portal</a>.</p> <p> </p>
Periodical cicadas disrupt trophic dynamics via community-level shifts in Avian Foraging
<p>Once every 13 or 17 years within eastern North American deciduous forests, billions of periodical cicadas concurrently emerge from the soil and briefly satiate a diverse array of naive consumers, offering a rare opportunity to assess the cascading impacts of an ecosystem-wide resource pulse on a complex food web. Here, we quantify the effects of the 2021 Brood X emergence, and report that >80 bird species opportunistically switched their foraging to include cicadas, releasing herbivorous insects from predation, and essentially doubling both caterpillar densities and accumulated herbivory levels on host oak trees. These short-lived but massive emergence events help us to understand how resource pulses can rewire interaction webs and disrupt energy flows in ecosystems, with potentially long-lasting effects. </p> <p> </p>
Data from: Herbivores disrupt clinal variation in plant responses to water limitation
<p>Plasticity in plant traits, including secondary metabolites, is critical to plant survival and competitiveness under stressful conditions. The ability of a plant to respond effectively to combined stressors can be impacted by crosstalk in biochemical pathways, resource availability, and evolutionary history, but such responses remain underexplored. In particular, we know little about intraspecific variation in response to combined stressors or whether such variation is associated with the stress history of a given population.</p> <p>Here, we investigated the consequences of combined water and herbivory stress for plant traits, including relative growth rate, leaf morphology, and various measures of phytochemistry, using a common garden of <em>Asclepias fascicularis</em> milkweeds. To examine how plant trait means and plasticities depend on the history of environmental stress, seeds for the experiment were collected from across a gradient of aridity in the Great Basin, USA. We then conducted a factorial experiment crossing water limitation with herbivory.</p> <p>Plants responded to water limitation alone by increasing the evenness of UV-absorbent secondary metabolites, and to herbivory alone by increasing the richness of metabolites. However, plants that experienced combined water and herbivory stress exhibited similar phytochemical diversity to well-watered control plants. This lack of plasticity in phytochemical diversity in plants experiencing combined stressors was associated with a reduction in relative growth rates.</p> <p>Leaf chemistry means and plasticities exhibited clinal variation corresponding to seed-source water deficits. The total concentration of UV-absorbent metabolites decreased with increasing water availability among seed sources, driven by higher concentrations of flavonol glycosides, which are hypothesized to act as antioxidants, among plants from drier sites. Plants sourced from drier sites exhibited higher plasticity in flavonol glycoside concentrations in response to water limitation, which increased phytochemical evenness, but simultaneous herbivory dampened plant responses to water limitation irrespective of seed source.</p> <p>Synthesis: These results suggest that climatic history can affect intraspecific phytochemical plasticity, which may confer tolerance to water limitation, but that co-occurring herbivory disrupts such patterns. Global change is increasing the frequency and intensity of stress combinations, such that understanding intraspecific responses to combined stressors is critical for predicting the persistence of plant populations.</p>
Transgenerational exposure to deoxygenation and warming disrupts mate detection in Gammarus locusta
<p class="s4"><span class="s9">Ocean deoxygenation and warming have been shown to pose a growing threat to the health of marine organisms and ecosystems. Yet, the potential for acclimation and adaptation remains poorly understood. The aim of this study was to evaluate the effects of transgenerational exposure to reduced oxygen availability and elevated seawater temperature on the chemosensory-dependent mating mechanisms of male amphipods </span><span class="s10"><em>Gammarus</em> <em>locusta</em></span><span class="s9">. Three subsequent generations were exposed to four experimental treatments for 30 days: i) present-day scenario, ii) warming; iii) deoxygenation; and iv) warming+deoxygenation. After exposure, the number of individuals that reached adulthood was gauged, and adult males from F<sub>0</sub> and F<sub>1</sub> were subjected to behavioral trials to assess their capacity of long-distance female cue detection through quantification of response time, first direction of movement, activity rate, and proportion of time spent in female scent cues. Ocean-warming-induced mortality and reduced oxygen availability had adverse effects on each of the investigated behavioral traits, which were amplified when combined with elevated temperature. Still, when compared to F<sub>0</sub>, the F<sub>1</sub> generation demonstrated more adaptability (i.e., higher activity rate and preference for female odors) to the combination of the two stressors, suggesting positive carry-over effects. Nevertheless, full recovery to control levels was not observed. Altogether, this study indicates that future scenarios of ocean deoxygenation and warming have the potential to disrupt chemosensory-dependent mate detection in amphipods, but also suggests possible behavioral adaptations. We call for greater research efforts on long-term impacts of ocean change on the behavioral and physiological processes of benthic coastal communities.</span></p>
Data for "Disrupted connectivity within a metapopulation of a wind-pollinated declining conifer Taxus baccata L."
<p>A spreadsheet contains microsatellite genotypes and population coordinates necessary for estimating seed and pollen migration rates. In addition, a spreadsheet contains detailed individual data necessary for parentage analysis.</p> <p>For more details, see:</p> <p>Chybicki IJ, Robledo-Arnuncio JJ, Bodziarczyk J, Widlak M, Meyza, K, Oleksa A, Ulaszewski B (2024) Disrupted connectivity within a metapopulation of a wind-pollinated declining conifer, Taxus baccata L. Forest Ecosystems 100240 (https://www.sciencedirect.com/science/article/pii/S2197562024000769)</p>
YY1 mutations disrupt corticogenesis through a cell type-specific rewiring of cell-autonomous and non-cell-autonomous transcriptional programs
<p>This supplementary data includes counts from bulk and pseudobulk omic experiments, h5ad for single-cell experiments, and outputs of differential expression and enrichments performed on different omics assays.</p>
Forest defoliator outbreaks disrupt nutrient cycling in northern waters
<p>Datasets for manuscript Forest defoliator outbreaks alter nutrient cycling in northern waters. IO_df is the main insect outbreak dataframe used to generate the bulk of the figures and results. It contains measures of monthly lake chemistry, insect disturbance, and catchment characteristics. Defoliator_Bark-Wood-Beetle_df is used to generate figure S1 and contains yearly measures of defoliator and bark/wood beetle outbreaks. ndvi_lai_data is used to generate figure S6 and contains the relationship between MOIDS LAI and Landsat NDVI. Outbreak_History is used to generate figure 5 and is a record of catchment-level disturbances in our study region. </p>
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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.