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1,454 results for “large scale”
FIG. 5 in Les mantes (Dictyoptera, Mantodea) du massif du Mitaraka (Guyane), in Touroult J. (ed.), "Our Planet Reviewed" 2015 large-scale biotic survey in Mitaraka, French Guiana.
FIG. 5. — Paratithrone royi Lombardo, 1996, femelle. Photos S. Hugel.
Large-scale purification of HTT domain constructs 2019/04/01
<p><strong>Project: </strong>High resolution structural analysis of purified HTT samples</p> <p><strong>Experiment: </strong>Large-scale purification of HTT domain constructs</p> <p><strong>Date completed:­ </strong>2019/04/01</p> <p><strong>Rationale: </strong>Domain fragments of the huntingtin protein are useful reagents for examining protein-protein interaction characteristics of huntingtin and to map interaction interfaces. Stable, monodisperse and pure samples may also be amenable to high resolution structure solution by X-ray crystallography. Previously huntingtin fragments were cloned and screened for expression in small-scale (3 mL) culture experiments. Positive hits were scaled for production as detailed in the table on the next page. All previous work is described in this post:<a href="https://zenodo.org/record/2600051#.XKU89aeZPOQ">https://zenodo.org/record/2600051#.XKU89aeZPOQ</a>.</p>
Large-scale stringent purification of HTT-HAP40 using heparin affinity chromatography 2019/05/12
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Large-scale stringent purification of HTT-HAP40 using heparin affinity chromatography</p> <p><strong>Date completed:­ </strong>2019/05/12</p> <p><strong>Rationale: </strong>Previous attempts to generate a much purer and homogenous HTT-HAP40 sample showed that the complex can bind heparin resin. In small-scale experiment, I also incorporated helpful suggestions from scientists at the CHDI Palm Springs meeting full-length HTT research breakout group i.e. ATP wash to remove HSP proteins. The experiment included 3 affinity chromatography steps with FLAG, heparin and NiNTA resin and finally a gel filtration step and yielded a highly pure HTT-HAP40 sample: <a href="https://zenodo.org/record/2628064">https://zenodo.org/record/2628064</a>. </p>
Large-scale stringent purification of Q23 HTT and HTT-HAP40 using heparin affinity chromatography 2019/05/22
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Large-scale stringent purification of Q23 HTT and HTT-HAP40 using heparin affinity chromatography</p> <p><strong>Date completed:­ </strong>2019/05/22</p> <p><strong>Rationale: </strong>Previous attempts to generate a much purer and homogenous HTT-HAP40 sample showed that the complex can bind heparin resin. In small-scale experiment, I also incorporated helpful suggestions from scientists at the CHDI Palm Springs meeting full-length HTT research breakout group i.e. ATP wash to remove HSP proteins. The experiment included 3 affinity chromatography steps with FLAG, heparin and NiNTA resin and finally a gel filtration step and yielded a highly pure HTT-HAP40 sample: https://zenodo.org/record/3234174.I now want to try this purification with apo HTT. </p>
Agriculture erases climate constraints on soil nematode communities across large spatial scales
<p>Data supporting "Agriculture erases climate constraints on soil nematode communities across large spatial scales".</p>
Large-scale purification of Q23 HTT from Sf9 2019/09/30
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Large-scale purification of Q23 HTT from Sf9 </p> <p><strong>Date completed:­ </strong>2019/09/30</p> <p><strong>Rationale: </strong>HTT samples are to be purified from different systems for subsequent biophysical and structural studies. </p>
Large-scale purification of Q23 HTT-HAP40 from Sf9 expression system with contaminating nucleic acid material 2019/09/16
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Large-scale purification of Q23 HTT-HAP40 from Sf9 expression system with contaminating nucleic acid material</p> <p><strong>Date completed:­ </strong>2019/09/16</p> <p><strong>Rationale:</strong> To purify HTT-HAP40 + Sf9 derived nucleic acid material for cryoEM analysis</p>
Large-scale purification of Q23 and Q54 HTT-HAP40 from Sf9 expression system in PBS 2019/09/16
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Large-scale purification of Q23 and Q54 HTT-HAP40 from Sf9 expression system in PBS.</p> <p><strong>Date completed:­ </strong>2019/09/16</p> <p><strong>Rationale: </strong>To purify HTT-HAP40 Q23 and Q54 in PBS to see if this reduces nucleic acid contamination.</p>
Data for manuscript "Evidence for large-scale climate forcing of dense shelf water variability in the Ross Sea"
<p>Relevant data shown in figures of the manuscript "Evidence for large-scale climate forcing of dense shelf water variability in the Ross Sea"</p>
Script and data from: The best of two worlds: toward large-scale monitoring of biodiversity combining metabarcoding and optimised parataxonomic validation.
<h2>Description</h2> <div> <p>Zenodo linked to : Penel, B., Meynard, C.N., Benoit, L., Bourdonné, A., Clamens, A., Soldati, L., Migeon, A., Chapuis, M.-P., Piry, S., Kergoat, G. and Haran, J. (2025), The best of two worlds: toward large-scale monitoring of biodiversity combining COI metabarcoding and optimized parataxonomic validation. Ecography, 2025: e07699. <a href="https://doi.org/10.1111/ecog.07699">https://doi.org/10.1111/ecog.07699</a></p> <div> <div> <div> <div> <p><strong>Publication abstract </strong></p> </div> </div> </div> <p>In a context of unprecedented insect decline, it is critical to have reliable monitoring tools to measure species diversity and their dynamic at large-scales. High-throughput DNA-based identification methods, and particularly metabarcoding, were proposed as an effective way to reach this aim. However, these identification methods are subject to multiple technical limitations, resulting in unavoidable false-positive and false-negative species detection. Moreover, metabarcoding does not allow a reliable estimation of species abundance in a given sample, which is key to document and detect population declines or range shifts at large scales. To overcome these obstacles, we propose here a Human-Assisted Molecular Identification (HAMI) approach, a framework based on a combination of metabarcoding and image-based parataxonomic validation of outputs and recording of abundance. We assessed the advantages of using HAMI over the exclusive use of a metabarcoding approach by examining 492 mixed beetle samples from a biodiversity monitoring initiative conducted throughout France. On average, 23% of the species are missed when relying exclusively on metabarcoding, this percent being consistently higher in species-rich samples. Importantly, on average, 20% of the species identified by molecular-only approaches correspond to false positives linked to cross-sample contaminations or mis-identified barcode sequences in databases. The combination of molecular methodologies and parataxonomic validation in HAMI significantly reduces the intrinsic biases of metabarcoding and recovers reliable abundance data. This approach also enables users to engage in a virtuous circle of database improvement through the identification of specimens associated with missing or incorrectly assigned barcodes. As such, HAMI fills an important gap in the toolbox available for fast and reliable biodiversity monitoring at large scales.</p> <div> <h4><strong>File description: </strong></h4> <h4>MiSeq raw sequences of the COI barcode from 492 Coleoptera field samples :</h4> <div>The Raw_sequencage_data ZIP directory contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Coleoptera field samples in duplicate using the MiSeq platform GenSeq (ISEM - University of Montpellier)</div> <div> </div> <div>The HAMI_data_script_results_R zip directory contains Rmarkdown script files (.Rmd and .html) and associated data used to analyse the systemic errors of the metabarcoding approach (N= 492 Coleoptera field samples).</div> <div> </div> <div>The HAMI_pipeline zip directory contains all the codes associated with the HAMI pipeline, as well as a ReadMe file and a test data set.</div> <div> </div> <div>The Residual_chimera.zip directory contains lists of MOTUs associated to residual chimeric sequences that were not filtered using FROGS pipeline but secondarily detected with the <em>de novo</em> approach implemented in HAMI pipeline with ‘isBimeraDenovo’ R function from DADA2 v1.28.0. It contains two distinct files according to the two sequencing runs.</div> <div> </div> <div>The NUMTS_filtered.zip directory contains lists of MOTUs that were excluded of the final dataset according to the NUMTS filtering. File xxx_pseudogene_f1_deteled.csv corresponds to MOTUs that were excluded according to the first filtrering step based on DNA sequencing. File xxx_pseudogene_f2_deteled.csv corresponds to merged MOTUs that were excluded according to the second filter based on occurrence and percentage of identity. This folder contains files for the two sequencing runs.</div> </div> </div> </div>
Ab initio design of microbial communities from large-scale seed pools using deep learning and rapid ptimization
<h4>This repository contains the full results of our paper: <strong><em>Ab initio</em> design of microbial communities from large-scale seed pools using deep learning and rapid ptimization.</strong></h4> <p>Authors: Xiaoqing Jiang#, Jiaheng Hou#, Haoyu Zhang#, Jinyuan Guo, Shaohua Gu, Yulin Liao, Xinrun Yang, Peter X. Geng, Yiyan Zhou, Qian Guo, Chunhui Wang, Mo Li, Alexandre Jousset, Zhong Wei*, and Huaiqiu Zhu*</p> <p>The results including:</p> <p><strong>(1)</strong> <strong>GEM.tar.gz</strong>: The eBiota-GEM dataset, containing 21,514 Genome-Scale Metabolic Models (GEMs) constructed using CarveMe based on RefSeq complete genomes.</p> <p><strong>(2) Baterial_evaluation.tar.gz</strong>: The evaluation of the ability to uptake substrates and secret productions for all 21,514 GEMs.</p> <p><strong>(3) Community_results.tar.gz</strong>: The results calculated from eBiota-GEM includes various combinations for two-bacterial consortia, covering strain IDs, substrates, products, yields, dual-bacterial growth, single-bacterial growth, co-occurrence predictions, interactions and total production.</p> <p><strong>(4) DeepCooc_files.tar.gz</strong>: The parameter files of DeepCooc, required by eBiota platform.</p>
Systematic creation and phenotyping of Mendelian disease models in C. elegans: towards large-scale drug repurposing
<p>Data collected for the eLife OpenAccess paper: Systematic creation and phenotyping of Mendelian disease models in <em>C. elegans</em>: towards large-scale drug repurposing. (doi: 10.7554/eLife.92491.1)</p> <p>Contains: extracted features, calculated stats, normalised z-scores and timerseries data of all the disease model mutants generated. In addition, there is a static .html file that allows for mousing over the clustermaps to easily view differences in strains compared to the N2 wild-type. Dataset also contains, metadata and feature summary/file name information of FDA-library drug screen and the confirmation screen of the hit from this (i.e., all data collected in published in the associated paper). </p>
Large Scale Medical Image Dataset
<p>Multiple dataset from different sources has been aggregated to create a large-scale medical image benchmark dataset in order to measure its performance. As each of the dataset’s images are of different sizes, the images are resized to 3 × 224 × 224 before the training process. This dataset contains total of 35 diseases of 4 different modality and is divided into train, validation, and test with a ratio of 7 : 1 : 2.</p>
Data on Epitaxial growth of large scale α phase antimonene
<ol> <li>ARPES: electronic structure of α phase antimonene measured with angle-resolved photoemission spectroscopy.</li> <li>DFT: a) sb_alpha_bs_GGA.tar.gz - contains input VASP files (POSCAR, POTCAR, INCAR, KPOINTS) and output (PROCAR) for band structure calculations within GGA-PBE approximation. <br>b) sb_alpha_bs_HSE06.tar.gz - contains input VASP files (POSCAR, POTCAR, INCAR, KPOINTS) and output (PROCAR) for band structure calculations within HSE06 approximation. </li> <li>LEEM: a) LEED pattern of α phase antimonene grown on w-Sb/W(110) surface. E=40 eV. <br>b) movies presenting growth of the α phase antimonene on the w-Sb/W(110) substrate: <br>Movie 1A: Field of View (FoV) = 10 μm, T = 390 K, E = 3.5 eV.<br>Movie 1B: Field of View (FoV) = 10 μm, T = 350 K (0 – 0.1 ML) plus T = 430 K (0.1 – 1.0). E = 3.5 eV.<br>Movie 2: FoV = 50 μm, T = 390 K, E = 3.5 eV.<br>Movie 3: FoV = 10 μm, T = 410 K, E = 3.5 eV.<br>Movie 4: FoV = 50 μm, T = 330 K, E = 3.5 eV.<br>Movie 5: FoV = 5 μm, T = 400 K, E = 24 eV. The movie has been recorded using a tilted electron beam enabling the observation of antimonene domains. <br>c) LEEM images recorded with a tilted electron beam. <em><span>E</span></em><span> = 22 eV, FOV=5 μm. </span></li> <li><span>STM: STM images of α phase antimonene grown on w-Sb/W(110). </span></li> </ol> <p> </p> <p> </p>
Replication package for: Acceptance of inequality between children: Large-scale experimental evidence from China and Norway
<p>CAppelen, Alexander W., Falch, Ranveig, Huang, Zhongjing, and Tungodden, Bertil, "Acceptance of inequality between children: Large-scale experimental evidence from China and Norway," October 2024</p>
Reduced atomic models for large-scale computations: Fe XIII near-infrared lines
<p>The tar file contains two directories. One contains the CHIANTI v.8-11 format main files<br>produced relative to a 33 merged states Fe XIII model.</p> <p>The other contains CHIANTI and P-CORONA format files for a 55-states Fe XIII model.</p> <p>In both cases the models were built to obtain accurate emissivities of the Fe XIII near-infrared lines withtin the ground configuration. </p> <p><br>The models are described in Del Zanna and Hebbur Dayananda, 2024, submitted to MNRAS.</p> <p>Information on the CHIANTI programs can be found in chianti-vip.com</p> <p>Information on P-CORONA can be found on https://research.iac.es/proyecto/polmag/pages/codes/p-corona.php</p>
Data for "Effects of forest dieback on deadwood patterns: large scale trends from a cross-analysis of European databases"
<p><strong><span>Aims</span></strong></p> <p><span>We carried out an opportunistic correlative study between past crown conditions and current deadwood volumes.</span></p> <p><span>Our aim was to mobilise available data on site factors and long-term monitoring of crown vitality indicators in Europe to investigate the influence of current and recent local defoliation levels on plot-level deadwood volume.</span></p> <p><span>For a subset of level I, 16*16-km monitoring plots located throughout Europe, we benefitted from data on both (i) deadwood measurements carried out within the framework of the Forest Focus Biosoil Project </span><span>(Galluzzi et al., 2019)</span><span>, pre-processed into a consistent and harmonized deadwood dataset by </span><span>Puletti et al. (2019)</span><span>, and (ii) defoliation assessments provided yearly since 1989 by the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests), the most comprehensive European monitoring network for the large-scale assessment of forest ecosystem health </span><span>(Vitale et al., 2014)</span><span>. </span></p> <p><span>Biosoil data on deadwood and ICP data on defoliation have never been crossed before.</span></p> <p><span>We used defoliation level as a proxy for the severity of stand dieback. Deadwood patterns can be addressed through deadwood profiles, which subdivide local deadwood stocks into classes based on size, position and decay stage.</span></p> <p><a name="_Toc175840512"></a><a name="_Toc116027761"></a><span><strong><span>ICP database and defoliation protocol</span></strong></span></p> <p><span>The International Cooperative Program to assess and monitor air pollution effects on the forest (ICP Forests) is responsible for an extensive level I monitoring system of forest sites </span><span>(Hauβmann & Fischer, 2004)</span><span>, which has been in operation since 1986. This large-scale level I network is made up of dense, spatially representative sampling points placed throughout European forests on a 16 × 16 km virtual grid, and is dedicated to monitoring forest conditions. The sampling points cover most European forested areas and encompasses ca. 6000 monitoring plots in 42 countries. In each plot, a visual evaluation of defoliation and discoloration of tree crowns is performed annually to survey forest health status (<a href="http://icp-forests.net/page/largescale-forest-condition">http://icp-forests.net/page/largescale-forest-condition</a>). Data management is presently carried out at the Programme Co-ordinating Centre (PCC) of ICP Forests in Eberswalde, Germany, and all data are available upon request. Since 1989, a standardized procedure for “annual surveys of crown condition’’ has been applied to 24 selected dominant and co-dominant trees with a minimum height of 60 cm and showing no significant mechanical damage. The defoliation and discoloration level of each tree crown is visually assessed on a sliding scale of 5% increments as the percentage of needle/leaf loss in the assessable crown as compared to a reference tree with full foliage. Mean defoliation at the plot scale was defined as the proportion of “damaged” trees i.e., with a defoliation rate of more than 25%, and used as a proxy for plot decline level. In the ICP database, the factors associated with observed defoliation related to natural disturbances or management (i.e., vertebrate or insect herbivory, fungal or fire damage, drought impacts, signs of removal of coarse woody debris, past landscape) were not recorded in a sufficiently standardized way to be used as covariates in our models. Similarly, plot-level living tree density and above-ground biomass for standing living trees (expressed in kg.ha<sup>−1</sup>), presumably surveyed in subplot 2, were not available.</span></p> <p><a name="_Toc175840513"></a><a name="_Toc116027762"></a><span><strong><span>Biosoil database and deadwood protocol</span></strong></span></p> <p><a name="_Toc116027763"></a><span>In the framework of the large collaborative European Forest Focus BioSoil-Biodiversity project</span><span>, a system of circular concentric subplots was built around certain ICP level I plots to collect additional data on stand structure and biodiversity between 2005 and 2008 (Figure 1). </span><span><span>The individual countries were responsible for selecting the ICP level I plots to be included in the BioSoil project </span></span><span><span>(Galluzzi et al., 2019)</span></span><span><span>. Overall, a total of 3243 geocoded Level I plots were considered in 19 European countries </span></span><span><span>(Puletti et al., 2017)</span></span><span><span>: Austria, Belgium (Flanders only), Cyprus, the Czech Republic, Denmark, Finland, France, Germany (eight federal states only), Hungary, Ireland, Italy, Latvia, Lithuania, Poland, Slovakia, Slovenia, Spain, Sweden and the United Kingdom (Figure 1). BioSoil project results are recorded in the multi-dimensional LI-BioDiv geodatabase that contains raw data on forest structure and vegetation records used to calculate simple plot-level structural and compositional forest variables (i.e., biomass, deadwood volume, plant alpha-diversity; </span></span><span><span>Bastrup-Birk et al. 2007; Hiederer & Durant 2010)</span></span><span><span>. At each plot, deadwood was quantified on an area of 400 m<sup>2</sup> (BioSoil subplots 1 and 2, radius of 11.28 m; </span></span><span><span>Puletti et al., 2017)</span></span><span><span>. The deadwood survey included coarse woody debris (including lying dead trees), snags (including standing dead trees) and stumps more than 10 cm in diameter. Only snags and stumps more than 130 cm in height were considered. Diameter, length or height, tree species and decay stage (5 classes) were recorded for each deadwood piece. The raw ICP deadwood data were processed by </span></span><span><span>Puletti et al. (2017, 2019)</span></span><span><span> into a consistent and harmonized pan-European deadwood dataset, which we used in this study. The dataset provides total deadwood volume and the volume of several deadwood types for each plot. Further details can be found in the ICP Forests manual (</span></span><a href="http://icp-forests.net/page/icp-forests-manual"><span><span>http://icp-forests.net/page/icp-forests-manual</span></span></a><span><span>), </span></span><span><span>Puletti et al. (2019)</span></span><span><span> and </span></span><span><span>Augustynczik et al. (2024)</span></span><span><span>.</span></span></p> <p><span><span>In our study, we considered the following response variables</span></span><span>: (i) total deadwood volume, (ii) </span><span>standing deadwood (snags) volume, (iii) volume of ground-lying deadwood, (iv) </span><span>fresh deadwood volume </span><span>(= Vm3_dec1_Biosoil + Vm3_dec2_Biosoil), and (v) decayed deadwood volume = (= Vm3_dec4_Biosoil + Vm3_dec5_Biosoil).</span></p> <p><span>A few environmental covariates were collected from the Biosoil data: (i) management intensity (grouped into two classes: recently harvested, i.e., with management evidence within the last 10 years; and not recently harvested, i.e., unmanaged (no management evidence) or managed a long time ago (management evidence but more than 10 years previously), (ii) average stand age (separated into 3 classes: mature [>100 yrs], mid-aged [41-100 yrs], young [1-40 yrs]), (iii) elevation (above sea level, a.s.l.), a continuous quantitative variable, (iv) dominant tree genus, and (v) forest type, depending on the dominant tree species: coniferous, deciduous or mixed.</span></p> <p><a name="_Toc175840514"></a><a name="_Toc116027764"></a><span><strong><span>Database joint</span></strong></span><span><strong><span>: <a name="_Toc116027765"></a>plot matching in time series</span></strong></span></p> <p><span>After harmonizing plot names and coordinates in the two datasets (ICP-defoliation and Biosoil-deadwood), only plots with matched data in both datasets were selected. Plots with a maximum of one year’s discontinuity in the data were retained, and the missing values were reconstructed from the average values in contiguous years. Plots with discontinuities in defoliation measurements of more than 2 years were deleted. We matched defoliation measurements for the Biosoil-ICP datasets from 1989 to 2007 and finally obtained 2,070 five-year, 1,804 ten-year and 1,399 fifteen-year time series. This approach made it possible to define three 10-year time series [1995-2005, 1996-2006, 1997-2007] with plots in 17 countries, from five plots in Ireland and nine in the United Kingdom, to 337 plots in Finland and 461 in France.</span></p> <p><a name="_Toc175840515"></a><a name="_Toc116027766"></a><span><strong><span>Calculation of global defoliation metrics</span></strong></span></p> <p><span>We calculated 16 univariate metrics to summarize changes in defoliation throughout the 10-year period prior to the Biosoil deadwood measurements. Some of the selected parameters describe the immediate possible effects of defoliation severity in the recent past on a given year: (i) defoliation level of the previous year (n-1), (ii) defoliation level of the year before the previous year (n-2), (iii) defoliation level of the year two years before the previous year (n-3). Other defoliation metrics relate to the cumulative effects of defoliation levels in the near or the distant past: (i) average defoliation level over the last two years, (ii) average defoliation level over the last three years, (iii) average defoliation level over the last five years, (iv) average defoliation level over the first five years of the 10-year time series, and (v) time elapsed since last peak defoliation. Several other parameters depict general trends in the level of defoliation over the 10-year time series: for cumulative metrics: (i) arithmetic mean of annual defoliation level; (ii) geometric mean of annual defoliation level; (iii) Area Under the defoliation time Curve (AUC), i.e., the cumulative sum of defoliation levels; and for the overall trend: (iv) the estimated slope of the linear regression line for defoliation level over time. Finally, some of the metrics reflect defoliation severity and repetition along the 10-year time series, and their potentially time-lagged effects: (i) maximum defoliation level; (ii) total number of years elapsed after the dieback peak level, whether successive or not; (iii) the number of peaks, consecutive or discontinuous, i.e., the number of severe defoliation events and defoliation frequency; and (iv) duration of the longest peak, i.e., the longest continuous time during which the level of defoliation was greater than the relative threshold.</span></p> <p><span>A peak in defoliation was defined as a year in which the level of defoliation exceeded a relative threshold, i.e., the third quartile value. In our 10-year time series, the peak value was 25% and above. <span><span> </span></span></span></p>
Large-Scale Atmospheric Drivers of Snowfall over Thwaites Glacier, Antarctica
<p>Monthly RACMO2 snowfall rates (1979-2015, in mm w.e. per month), as described in Lenaerts et al., 2018 (https://www.cambridge.org/core/journals/annals-of-glaciology/article/climate-and-surface-mass-balance-of-coastal-west-antarctica-resolved-by-regional-climate-modelling/E3DD6D0DA914C6031F96A548AF53603A), and used in this paper. </p>
Rapid adaptive evolution to drought in a subset of plant traits in a large-scale climate change experiment
<p>Rapid evolution of traits and of plasticity may enable adaptation to climate change, yet solid experimental evidence under natural conditions is scarce. Here, we imposed rainfall manipulations (+30%, control, -30%) for ten years on entire natural plant communities in two Eastern Mediterranean sites. Additional sites along a natural rainfall gradient and selection analyses in a greenhouse assessed whether potential responses were adaptive. In both sites, our annual target species <i>Biscutella didyma</i> consistently evolved earlier phenology and higher reproductive allocation under drought. Multiple arguments suggest that this response was adaptive: it aligned with theory, corresponding trait shifts along the natural rainfall gradient, and selection analyses under differential watering in the greenhouse. However, another seven candidate traits did not evolve, and there was little support for evolution of plasticity. Our results provide compelling evidence for rapid adaptive evolution under climate change. Yet, several non-evolving traits may indicate potential constraints to full adaptation.</p>
Replication package for: The real effects of monetary expansions: evidence from a large-scale historical experiment
<p>The replication materials contain a README file, STATA datasets and do-files. This replication package for Palma (2021) constructs the entire analysis from the data sources described in the published paper, using STATA. The replicator should expect the code to run for less than 10 minutes.</p> <p>Palma, N. (2021). The real effects of monetary expansions: evidence from a large-scale historical experiment. Review of Economic Studies, forthcoming</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.