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3,139 results for “fits”
Polysaccharide utilization loci in Bacteroides determine population fitness and community-level interactions
<p>Polysaccharide utilization loci (PULs) in the human gut microbiome have critical roles in shaping human health and ecological dynamics. We develop a CRISPR-FnCpf1-RecT genome-editing tool to study 23 PULs in the highly abundant species <em>B. uniformis</em> (BU). We identify the glycan-degrading functions of multiple PULs and elucidate transcriptional coordination between PULs that enables the population to adapt to the loss of PULs. Exploiting a pooled BU mutant barcoding strategy, we demonstrate that the <em>in vitro</em> fitness and the colonization ability of BU in the murine gut is enhanced by deletion of specific PULs and modulated by glycan availability. We show that BU PULs can mediate complex glycan-dependent interactions with butyrate producers that depend on the mechanism of degradation and the butyrate producer glycan utilizing ability. In sum, PULs are major determinants of community dynamics and butyrate production and can provide a selective advantage or disadvantage depending on the nutritional landscape. </p>
Data for fitness analyses used in: Environmentally-induced DNA methylation is inherited across generations in water fleas (Daphnia magna)
<p><span>Data of</span> fitness effects of environmental stressors on <em>Daphnia magna</em> over multiple generations. Ages of first and second reproduction, and sizes of first and second brood were measured and used to calculate replacement rate. This data is part of a study on whole-genome bisulphate sequencing on individual <em>Daphnia magna</em> to assess whether environmentally-induced DNA methylation can persist for up to four generations.</p>
Longer and warmer prewinter periods reduce post-winter fitness in a diapausing insect
<p>1. Diapause is considered an important adaptation for survival of winter; however, insects often enter diapause long before its onset. Thus, diapausing insects must also be able to survive these prewinter conditions which warm temperatures could make quite energetically taxing despite relative inactivity.</p> <p>2. We tested for both immediate and delayed fitness effects of prewinter conditions in diapausing Pieris napi butterfly pupae, experimentally exposing them to different prewinter treatments in a factorial design. We placed diapausing pupae at one of three temperatures (15, 20, 25 °C) for 1 to 16 weeks, followed by the same standardized winter for all individuals.</p> <p>3. We monitored survival at multiple points during the experiment, including after winter, as well as their change in mass. For a subset of individuals, we also made repeated metabolic measurements.</p> <p>4. We found substantial weight loss during prewinter warm periods, greater during longer prewinter treatments at higher temperatures. This weight loss was associated with elevated metabolic rates at higher temperatures which increased over the duration of the prewinter treatment.</p> <p>5. Although we found little prewinter mortality associated with these conditions, mortality was much greater post-winter for individuals in long, warm prewinter treatments and the dry mass of adults that did survive these conditions was lower, highlighting the need to understand chronic or delayed effects of stress on fitness.</p> <p>6. Ultimately, we found substantial fitness consequences of prewinter conditions for a diapausing insect. Given that climate change will make these prewinter periods both longer and more intense, it will be important to understand how dormant organisms tolerate or reduce the length of these dormant, inactive periods.</p>
Replication data for: Enhancing user awareness on inferences obtained from fitness trackers data
<p>Survey results and fitness trackers datasets used for the evaluation of PrivacyEnhAction application.</p>
Contrasting parasite-mediated reductions in fitness within vs. between patches of a nematode host
<p>Host and parasites interact across spatial scales, but parasite-mediated fitness effects are often measured at local scales only. Recent work suggests that parasites can reduce host fitness during dispersal between patches, highlighting the potential for both within- and between-patch effects to contribute to the net fitness consequences of parasitism. Building on this work, we measured the contribution of the dispersal phase to parasite-mediated reductions in host fitness. We used the nematode <em>Caenorhabditis elegans</em> and its natural microsporidian parasite <em>Nematocida parisii</em> to quantify the fitness consequences of parasitism at the individual, population, and metapopulation level. <em>N. parisii</em> reduced individual fecundity and population growth but had its greatest fitness impact at the dispersal stage: parasitism reduced the fitness of dispersing larvae by 63 – 100%. These results indicate that the cost of parasitism in this system is greatly underestimated if the metapopulation level is not taken into account. We also found that the effects of <em>N. parisii</em> vary with host genotype, and the relative advantage of the most resistant genotype increases with inclusion of the dispersal stage. Taken together, our findings demonstrate that host-parasite interactions at the dispersal stage magnify selection for parasite resistance.</p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character's evolution: R scripts and simulated trees
<p>All R scripts used in this study, and the set of simulated phylogenetic trees used in the study.</p> <p>1. Modern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.<br><br>2. In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (1) the effect of alternate branch lengths on ASE error, and (2) whether phylogenetic signal statistics and/or model-fit statistic can be used to select the branch lengths most correlated with a binary character.<br><br>3. In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model-fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model-fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favorable for model optimization, and when shape differences between alternate phylogenies are greater.<br><br>4. Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model-fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model fit.</p>
data for "Plant genetic effects on microbial hubs impact host fitness in repeated field trials"
<p>These are data tables required for the analysis of the paper "Plant genetic effects on microbial hubs impact host fitness in repeated field trials". <br> All scripts are available at https://forgemia.inra.fr/bbrachi/microbiota_paper.git</p> <p>The folder architecture in the zip files is the same as in the repository: https://forgemia.inra.fr/bbrachi/microbiota_paper.git</p> <p>The dataset includes: </p> <p>- OTU count tables for 16S and ITS</p> <p>- taxonomic assignation</p> <p>- plant seed-set estimates</p> <p>- plant growth data from the B38 experiment. </p> <p>- Metabolomics datasets</p>
Band structure of KTaO3 two dimensional electron gas: ARPES data and tight binding fits
<p>The dataset contains the angle resolved photoemission spectroscopy measurements of the band structure of the two dimensional electron gas generated at the KTaO3/Al interface. Both dispersion and constant energy maps near the Fermi level are provided.</p> <p>The experimental data are complemented with tight binding fits (eight bands).</p>
Ellipse of best fit parameters for MARCI mosaics from MY 29 to MY 35
<p>Datasets containing the ellipse of best-fit parameters were used to track the Northern Polar Seasonal Cap using MARCI mosaics from MY 29 to MY 35. Datasets have been cleaned to remove any outliers. </p>
Fitness of evolving bacterial populations is contingent on deep and shallow history but only shallow history creates predictable patterns
<p><span>Long term evolution experiments have tested the importance of genetic and environmental factors in influencing evolutionary outcomes. Differences in phylogenetic history, recent adaptation to distinct environments, and chance events all influence the fitness of a population. However, the interplay of these factors on a population's evolutionary potential remains relatively unexplored. We tracked the outcome of 2,000 generations of evolution of four natural isolates of Escherichia coli bacteria that were engineered to also create differences in shallow history by adding previously identified mutations selected in a separate long-term experiment. Replicate populations started from each progenitor were evolved in four environments. We found that deep and shallow phylogenetic histories both contributed significantly to differences in evolved fitness, though by different amounts in different selection environments. With one exception, chance effects were not significant. Whereas the effect of deep history did not follow any detectable pattern, effects of shallow history followed a pattern of diminishing-returns whereby fitter ancestors had smaller fitness increases. These results are consistent with adaptive evolution being contingent on the interaction of several evolutionary forces but demonstrate that the nature of these interactions is not fixed and may not be predictable even when the role of chance is small.</span></p>
Data from: Sex-dependent effects of parental age on offspring fitness in a cooperatively breeding bird
<p>Parental age can have considerable effects on offspring phenotypes and health. However, intergenerational effects may also have longer-term effects on offspring fitness. Few studies have investigated parental age effects on offspring fitness in natural populations while also testing for sex- and environment-specific effects. Further, longitudinal parental age effects may be masked by population-level processes such as the selective disappearance of poor-quality individuals. Here, we used multi-generational data collected on individually marked Seychelles warblers (<em>Acrocephalus</em> <em>sechellensis</em>) to investigate the impact of maternal and paternal age on offspring lifespan and lifetime reproductive success. We found negative effects of maternal age on female offspring lifespan and lifetime reproductive success, which were driven by within-mother effects. There was no difference in annual reproductive output of females born to older versus younger mothers, suggesting that the differences in offspring lifetime reproductive success were driven by effects on offspring lifespan. In contrast, there was no association between paternal age and female offspring lifespan or either maternal or paternal age and male offspring lifespan. Lifetime reproductive success, but not annual reproductive success, of male offspring increased with maternal age, but this was driven by between-mother effects. No paternal age effects were found on female offspring's lifetime reproductive success but there was a positive between-father effect on male offspring's lifetime reproductive success. We did not find strong evidence for environment-dependent parental age effects. Our study provides evidence for parental age effects on the lifetime fitness of offspring and shows that such effects can be sex-dependent. These results add to the growing literature indicating the importance of intergenerational effects on long-term offspring performance and highlight that these effects can be an important driver of variation in longevity and fitness in the wild.</p>
Table 5: Model Fit Summary (b) for Grade Three
<p>The model summary in Table 5 reports the strength of the relationship between the model and the<br> dependent variable, i.e., ECA exam. The multiple correlation coefficient, R, which is the linear<br> correlation between the observed and the model-predicted values of the dependent variable is .551.<br> R Square, which is also called the coefficient of determination, is .303 showing that approximately<br> 30 percent of the variation in ECA exam is explained by the model. Its interpretation is that 30<br> percent of the variation in the ECA test scores is common with the vocabulary scores. The Standard<br> Error of the Estimate of the model is approximately 3.16 out of a total of 30, meaning that the<br> prediction model produces an error range between ± [3.16]. Therefore, the prediction formula must<br> be rewritten as (AVERAGE VOCAB × 0.713) + 2.871± [3.16].</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 13. Average fitness at generation progressing
<p>Figure 13 compares the average fitness at generations in one typical program running. As can be seen from this figure, the average of fitness grows as the number of generations increases.</p>
Data for 'VespaG: Expert-guided protein language models enable accurate and blazingly fast fitness prediction'
<div>Datasets used for development of VespaG and VespaG predictions generated with <a href="https://github.com/JSchlensok/VespaG">https://github.com/JSchlensok/VespaG</a>. </div> <div> </div> <div>Uploads contain:</div> <div> <ol> <li><strong>Performance</strong> summaries for ProteinGym [1]:<br>- Spearman and Pearson correlation for VespaG: <em>proteingym_performance_vespag.csv </em>(columns: 'DMS_id', 'Spearman', 'Pearson')<br>- Spearman correlation for evaluated methods VespaG, GEMME [2], VESPA [3], TranceptEVE [4], AlphaMissense [5], PoET [6]: <em>proteingym_spearman_allmethods.csv </em>(columns: 'DMS_id', 'Trancept EVE-L', 'VESPA', 'VespaG', 'GEMME', 'AlphaMissense', 'PoET', 'UniProt_ID', 'coarse_selection_type' (function), 'taxon')</li> <li><strong>Fasta</strong> files with sequences for all train sets (<em>vespag_fasta_training_datasets.zip</em> with seq_all9k.fasta, seq_human5k.fasta, seq_droso4k.fasta, seq_ecoli2k.fasta, seq_virus1k.fasta) and test set (<em>proteingym_217.fasta</em>)</li> <li><strong>VespaG</strong> <strong>Predictions</strong> for test set: <em>vespag_proteingym_rawpreds_by_training_dataset.zip</em> with raw_preds_ecoli.csv, raw_preds_human.csv, raw_preds_virus.csv, raw_preds_all.csv, raw_preds_droso.csv (columns: 'DMS_id', 'mutation', 'DMS_score', 'VespaG'). Predictions are based on different training data, the final model VespaG was trained on a subset of the human proteome and <strong>raw VespaG predictions</strong> <strong>for</strong> <strong>the</strong> <strong>ProteinGym benchmark are in raw_preds_human.csv </strong>(used to calculate the performances above).</li> <li><strong>GEMME predictions</strong> for train sets: <em>vespag_proteingym_rawpreds_by_training_dataset.zip </em>with folders 'human', 'droso', 'ecoli', 'virus', 'all' for respective fasta file (each containing GEMME mutational landscape output files named '<em>ID' + '</em>_normPred_evolCombi.txt')</li> <li><strong>ESM-2</strong> <strong>embeddings</strong> [7] for test set (<em>proteingym_217_esm2.h5</em>)</li> </ol> </div> <div>For details on VespaG see:</div> <div> <div> <div>VespaG: Expert-guided protein Language Models enable accurate and blazingly fast fitness prediction</div> </div> <div>Celine Marquet, Julius Schlensok, Marina Abakarova, Burkhard Rost, Elodie Laine</div> <div>bioRxiv 2024.04.24.590982; doi: https://doi.org/10.1101/2024.04.24.590982</div> <div> </div> <div>For more information on data usage and generation please see <a href="https://github.com/JSchlensok/VespaG">https://github.com/JSchlensok/VespaG</a>.</div> <div> </div> <div>Abstract:</div> <div>Exhaustive experimental annotation of the effect of all known protein variants remains daunting and expensive, stressing the need for scalable effect predictions. We introduce VespaG, a blazingly fast single amino acid variant effect predictor, leveraging embeddings of protein Language Models as input to a minimal deep learning model. To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from the human proteome applying the multiple sequence alignment-based effect predictor GEMME as a pseudo standard-of-truth. Assessed against the ProteinGym Substitution Benchmark (217 multiplex assays of variant effect with 2.5 million variants), VespaG achieved a mean Spearman correlation of 0.48 +/- 0.01, matching state-of-the-art methods such as GEMME, TranceptEVE, PoET, AlphaMissense, and VESPA. VespaG reached its top-level performance several orders of magnitude faster, predicting all mutational landscapes of the human proteome in 30 minutes on a consumer laptop (12-core CPU, 16 GB RAM).</div> <div> </div> <div>[1] Notin, Pascal, et al. "ProteinGym: large-scale benchmarks for protein fitness prediction and design." <em>Advances in Neural Information Processing Systems</em> 36 (2024).<br>[2] Laine, Elodie, Yasaman Karami, and Alessandra Carbone. "GEMME: a simple and fast global epistatic model predicting mutational effects." <em>Molecular biology and evolution</em> 36.11 (2019): 2604-2619.</div> <div>[3] Marquet, Céline, et al. "Embeddings from protein language models predict conservation and variant effects." <em>Human genetics</em> 141.10 (2022): 1629-1647.</div> <div>[4] Notin, Pascal, et al. "TranceptEVE: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction." <em>bioRxiv</em> (2022): 2022-12.</div> <div>[5] Cheng, Jun, et al. "Accurate proteome-wide missense variant effect prediction with AlphaMissense." <em>Science</em> 381.6664 (2023): eadg7492.</div> <div>[6] Truong Jr, Timothy, and Tristan Bepler. "PoET: A generative model of protein families as sequences-of-sequences." <em>Advances in Neural Information Processing Systems</em> 36 (2024).</div> <div>[7] Lin, Zeming, et al. "Evolutionary-scale prediction of atomic-level protein structure with a language model." <em>Science</em>379.6637 (2023): 1123-1130.</div> </div>
Data from: Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signaling networks
<p>Many genes and signaling pathways within plant and animal taxa drive the expression of multiple organismal traits. This form of genetic pleiotropy instigates trade-offs among life-history traits if a mutation in the pleiotropic gene improves the fitness contribution of one trait at the expense of another. Whether or not pleiotropy gives rise to conflict among traits, however, likely depends on the resource costs and timing of trait deployment during organismal development. To investigate factors that could influence the evolutionary maintenance of pleiotropy in gene networks, we developed an agent-based model of co-evolution between parasites and hosts. Hosts comprise signaling networks that must faithfully complete a developmental program while also defending against parasites, and trait signaling networks could be independent or share a pleiotropic component as they evolved to improve host fitness. We found that hosts with independent developmental and immune networks were significantly more fit than hosts with pleiotropic networks when traits were deployed asynchronously during development. When host genotypes directly competed against each other, however, pleiotropic hosts were victorious regardless of trait synchrony because the pleiotropic networks were more robust to parasite manipulation, potentially explaining the abundance of pleiotropy in immune systems despite its contribution to life history trade-offs.</p>
Data from: Trait-fitness associations via fecundity and competition in a two-million-year-long fossil record
<p>The evolution of phenotypic traits is usually studied on generational times or across species on million-year timescales. We bridge this conceptual gap by using high density sampling of a species lineage, <em>Microporella agonistes </em>(Bryozoa, Cheilostomatida), over 2 million years of its evolutionary history, to ask if trait-fitness associations are consistent with evolutionary trait models often applied to phenotypic time series. We use average fecundity and competitive outcome as two different fitness components, where competitive outcome is a proxy for partial survival. Examining three quantitative traits in multivariate analyses, we present evidence that some traits experienced substantial selective pressures, in part controlled by past environments. A complex interplay of resource competition with an altering set of competitors and past temperatures, has contributed to the changing patterns of phenotypes within the focal species. A comparison with congeneric species living in the same regional community suggests that size traits are more temporally variable and less constrained than shape traits. Our analyses also show that while controls on phenotypes are complex and varied in time, ecological and evolutionary processes that unfold on shorter time scales are not inconsistent with macroevolutionary patterns observed on longer timescales.</p>
Best-fit SED templates of each CAMIRA member galaxy (Table 3)
<p>This upload includes individual SED templates as described in Table 3 of the related manuscript, "<em>Active Galactic Nucleus Properties of ~1 Million Member Galaxies of Galaxy Groups and Clusters at z < 1.4 Based on the Subaru Hyper Suprime-Cam Survey</em>", by Yoshiki Toba et al., published in the Astrophysical Journal (Accepted: 15-Feb-2024; published: 17-May-2024, doi:<a href="https://doi.org/10.3847/1538-4357/ad32c6">10.3847/1538-4357/ad32c6</a>).</p> <p>This archive is compressed with xz, part of <a href="https://github.com/tukaani-project/xz">XZ Utils</a>. </p> <p>A description of these files is given below.</p> <p>The "ID" field is captured in the file naming in the compressed archive:</p> <ul> <li> <p>1_SED.eps [EPS plot of SED, SED fit, for Object ID = 1]</p> </li> <li> <p>1_SED_data.fits [Binary Table FITS (HDU=1) where the contents of HDU=1 are given in the table below]</p> </li> </ul> <p> </p> <table> <tbody> <tr> <td> <div> <div> <div> <p>Column name</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Format</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Unit</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Description</p> </div> </div> </div> </td> </tr> <tr> <td> <div> <div> <div> <p>Wavelength</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>DOUBLE</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>μm</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Wavelength (observed frame)</p> </div> </div> </div> </td> </tr> <tr> <td> <div> <div> <div> <p>FNU</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>DOUBLE</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>mJy</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Flux density at each wavelength</p> </div> </div> </div> </td> </tr> <tr> <td> <div> <div> <div> <p>log_L</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>DOUBLE</p> </div> </div> </div> </td> <td>erg/s</td> <td> <div> <div> <div> <p>Luminosity at each wavelength</p> </div> </div> </div> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Repository 2 of 2 of the Level 0 (raw FITS) images from the DMI Mauna Loa Earthshine telescope experiment (2010-2012)
<h2>Raw data upload</h2> <p>These are tar files of the raw data obtained with the earthshine refractor conceived, designed and built by Danish Meteorological Institute and Lunds Observatory and operated on Mauna Loa at the NOAA observatory during 2011-2012.</p> <p>The present doi relates to one of two - each one holds roughly 50Gb tar file. Get all for the complete dataset. The other doi is: 10.5281/zenodo.11442378</p> <p>The data are in the form of FITS files. There are data for 5 different photometric filters - Johnson B and V and then 3 red or nIR filters - IRCUT, VE1 and VE2. Some frames are without filters.</p> <p>Examples:</p> <p>2455945.1760145MOON_B_AIR.fits.gz - image of the Moon through the B filter</p> <p>2455945.0413946DARK_DARK.fits.gz - a DARK frame image taken with same exposure time as the adjacent MOON image. <br>A DARK is typically taken before and after all MOON images.</p> <p>The file-naming strategy for the file inside the archive files is simple - names start with the (fractional) Julian day of the observation, followed by target name and two indicators of the filters or other light-path devices that were used, e.g.: _VE1_AIR indicates the VE1 colour filter with just 'air' (ie nothing) in the second option. The second option may include names of knife-edge devices or neutral density filters that could be inserted.</p> <p>There are also some FLAT images taken on inside of dome or of the sky or of the Hohlraum source.</p> <p>The MOON images may well be 100-image 3D-cubes taken within a minute or two.</p> <p>Future upgrades (in new dois, but in this Community) will include dark-frame subtracted, aligned and averaged images (Level 1). A Level 2 may include actual model fits of the full lunar disks, in which terrestrial albedo is a model-parameter.</p> <p>The file called "index_justJD.csv" lists which Julian days are present in which archive file. Note that some days are spread over several tar files.</p> <p>The images were acquired with the remotely-operated telescope by Peter Thejll, Hans Gleinser, Chris Flynn, as well as Henriette Schwarz.</p> <p>Notes updated May 3 2024.</p> <p>Peter Thejll</p> <p>write pth@dmi.dk for queries</p>
Repository 1 of 2 of the Level 0 (raw FITS) images from the DMI Mauna Loa Earthshine telescope experiment (2010-2012)
<h2>Raw data upload</h2> <p>These are tar files of the raw data obtained with the earthshine refractor conceived, designed and built by Danish Meteorological Institute and Lunds Observatory and operated on Mauna Loa at the NOAA observatory during 2011-2012.</p> <p>The present doi relates to one of two - each one holds roughly 50Gb tar file. Get all for the complete dataset. The other doi is: 10.5281/zenodo.11500335</p> <p>The data are in the form of FITS files. There are data for 5 different photometric filters - Johnson B and V and then 3 red or nIR filters - IRCUT, VE1 and VE2. Some frames are without filters.</p> <p>Examples:</p> <p>2455945.1760145MOON_B_AIR.fits.gz - image of the Moon through the B filter</p> <p>2455945.0413946DARK_DARK.fits.gz - a DARK frame image taken with same exposure time as the adjacent MOON image. <br>A DARK is typically taken before and after all MOON images.</p> <p>The file-naming strategy for the file inside the archive files is simple - names start with the (fractional) Julian day of the observation, followed by target name and two indicators of the filters or other light-path devices that were used, e.g.: _VE1_AIR indicates the VE1 colour filter with just 'air' (ie nothing) in the second option. The second option may include names of knife-edge devices or neutral density filters that could be inserted.</p> <p>There are also some FLAT images taken on inside of dome or of the sky or of the Hohlraum source.</p> <p>The MOON images may well be 100-image 3D-cubes taken within a minute or two.</p> <p>Future upgrades (in new dois, but in this Community) will include dark-frame subtracted, aligned and averaged images (Level 1). A Level 2 may include actual model fits of the full lunar disks, in which terrestrial albedo is a model-parameter.</p> <p>The file called "index_justJD.csv" lists which Julian days are present in which archive file. Note that some days are spread over several tar files.</p> <p>The images were acquired with the remotely-operated telescope by Peter Thejll, Hans Gleinser, Chris Flynn, as well as Henriette Schwarz.</p> <p>Notes updated May 3 2024.</p> <p>Peter Thejll</p> <p>write pth@dmi.dk for queries</p>
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
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.