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data for Hogan et al. 2023: "Functional consequences of animal community changes in managed grasslands: An application of the CAFE approach"
<p>Data to accompany the following publication:</p> <div> <div> <div> <div>Hogan, K. F. E., Jones, H. P., Savage, K., Burke, A. M., Guiden, P. W., Hosler, S. C., Rowland‐Schaefer, E., & Barber, N. A. (2023). Functional consequences of animal community changes in managed grasslands: An application of the CAFE approach. <em>Ecology</em>, e4192. <a href="https://doi.org/10.1002/ecy.4192">https://doi.org/10.1002/ecy.4192</a></div> </div> </div> </div> <p>Please see README for description of data. </p> <p>Paper abstract: In the midst of an ongoing biodiversity crisis, much research has focused on species losses and their impacts on ecosystem functioning. The functional consequences (ecosystem response) of shifts in communities are shaped not only by changes in species richness, but also by compositional shifts that result from species losses and gains. Species differ in their contribution to ecosystem functioning, so species identity underlies the consequences of species losses and gains on ecosystem functions. Such research is critical to better predict the impact of disturbances on communities and ecosystems. We used the ‘Community Assembly and the Functioning of Ecosystems’ (CAFE) approach, a modification of the Price equation to understand the functional consequences and relative effects of richness and composition changes in small non-volant mammal and dung beetle communities as a result of two common disturbances in North American prairie restorations – prescribed fire and reintroduction of large grazing mammals. Previous research in this system shows dung beetles are critically important decomposers, while small mammals modulate much energy in prairie food webs. We found that dung beetle communities were more responsive to bison reintroduction and prescribed fires than small non-volant mammals. Dung beetle richness increased after bison reintroduction, with higher dung beetle community biomass resulting from changes in remaining species (context-dependent component) rather than species turnover (richness components); prescribed fire caused a minor increase in dung beetle biomass for the same reason. For small mammals, bison reintroduction reduced energy transfer through the loss of species, while prescribed fire had little impact on either small mammal richness or energy transfer. The CAFE approach demonstrates how bison reintroduction controls small non-volant mammal communities by increasing prairie food web complexity, and increases dung beetle populations with possible benefits for soil health through dung mineralization and soil bioturbation. Prescribed fires, however, have little effect on small mammals and dung beetles, suggesting a resilience to fire. These findings illustrate the key role of re-establishing historical disturbance regimes when restoring endangered prairie ecosystems and their ecological function.</p>
Interlaboratory testing of CuSO4 toxicity in the “Standardized Aquatic Microcosm” protocol consisting of multiple phytoplankton and animals in a chemically defined medium.
Four different laboratories conducted a total of ten experiments of the “Standardized Aquatic Microcosm” to test the reproducibility of results to control, low, medium, and high concentrations of CuSO4. In nine experiments, treatments consisted of six replicates of 0, 500, 1000, and 2000 ppb Cu++. One experiment, ME74, used 0, 127, 255, 509 ppb. The purpose was to test a chemically defined medium (thus negating differences due to local water supplies) and the same 10 species of phytoplankton and 5 animals, including Daphnia. Microbes were undefined. The protocol included the weekly re-introduction of small numbers of each species to allow potential recovery from toxicity. Control microcosms had a “spring algal bloom” terminated by zooplankton grazing and multiple competitive interactions. The copper inhibited some phytoplankton more than others and killed many grazers, especially Daphnia. The data set presented several interesting statistical properties that would yield new insights. (a) The results were very similar, but the timing varied— the higher the concentration of copper, the longer the inhibition and mortality of organisms, so those at 500 ppb recovered earlier, the 1000 ppb recovered later, and at 2000 ppb most never recovered. But if compared on each sampling day, e.g., 10, 14, … to 64, results appear highly variable. (b) In at least one experiment, the toxicity of copper was challenging to demonstrate statistically because high variability in the timing of recovery of the intermediate concentration increased pooled variances. (c) The elimination of highly-sensitive dominant organisms allowed less-sensitive organisms to increase in abundance. Within natural environments, the observation that some species increase in the presence of toxic substances has been used to discredit toxicity testing without considering the relative sensitivities of competing or predatory species. (d) The competitive interactions among organisms, e.g., cyanobacteria and green alga
Animal Gut Microbiome (AGM) Data from 91 Published Studies for 224 Animal Species
Diversity and heterogeneity often are conflated but are fundamentally different. An aphorism proposed by Shavit and Ellison (2021; J. Phil. 118: 525–548) for distinguishing them is that “a zoo is diverse whereas an ecosystem is heterogeneous.” That is, a zookeeper measuring diversity simply enumerates the different types of animals; interactions are not expected to occur between animals separated by fences or other barriers. In contrast, measures of heterogeneity ought to include both interspecific interactions and relationships between species and their heterogeneous habitats. Here, we use cross-scale, dual scaling-law analyses of heterogeneity and diversity of animal gut microbiomes (AGMs) to address three objectives: (i) estimate the spatial heterogeneity and diversity of animal-gut microbiomes; (ii) analyze influences of phylogeny and diets on scaling of diversity and heterogeneity; (iii) explore mechanistic differences between diversity and heterogeneity in AGMs. From 4903 AGM samples collected from 318 animal species covering all six classes of vertebrates and four major classes of invertebrates, we estimated that ≈640,000 operational taxonomic units (OTUs or “species”) make up the pool of microbial species that could inhabit animal guts, among which ≈8000 are relatively common and ≈800 are dominant. The gut of any single animal, however, includes only 0.01–0.5% of the total species pool. We extended Ma’s diversity-area relationship for scaling diversity and extend Taylor’s Power Law and Luna et al.’s (2020; Diversity 12: 86) interaction diversity for scaling heterogeneity. At the community scale, phylogeny significantly influenced heterogeneity, but diets did not. Phylogeny and diets had limited influence on diversity at both community and landscape scales. Although two common measures of diversity—beta diversity and unevenness—commonly are synonymized with heterogeneity, our data lead us to conclude that diversity and heterogeneity measure two very different
VR-Together Pilot 1: 3D Animated Character Meshes
<p>This dataset contains the 3D Animated Characters as used for the Pilot 1 experience of the VR-Together project. The animated characters come as a set of FBX files with associated diffuse and normal maps. </p>
Physiological parameters for three farm animal species (cattle, sheep, and swine) as the basis for the development of generic physiologically based kinetic models
<p><strong>IMPORTANT : PLEASE DISREGARD VERSION 1 OF THIS UPLOAD SINCE IT INCLUDES ERRONEOUS INFORMATION.</strong></p> <p>This excel file (DOI: 10.5281/zenodo.3433224) provides physiological parameters and their inter-individual variability (mean, coefficient of variation, sample size) for three farm animal species: cattle (<em>Bos taurus</em>), sheep (<em>Ovis aries</em>), and swine (<em>Sus scrofa domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020). This file is associated with R codes (DOI: 10.5281/zenodo.3432796) for generic PBK models, partition coefficient Quantitative Structure Activity Relationship (QSAR) models for each farm animal species and parameterisation of the model.</p> <p>The full data collection and implementation of the models using case studies are described in Lautz et al., 2020 (10.1016/j.toxlet.2019.10.008).</p>
Dataset associated with article "Robots mediating interactions between animals for interspecies collective behaviors"
<p>This dataset contains results and analysis described in the study "Robots mediating interactions between animals for interspecies collective behaviors", Bonnet, F., Mills, R., Szopek, M., Schönwetter-Fuchs, S., Halloy, J., Bogdan, S., Correia, L., Mondada, F. and Schmickl, T. (2019), <em>Science Robotics</em>, <em>4</em>(28), doi: 10.1126/scirobotics.aau7897</p> <p>Contents: </p> <ul> <li>experimental data (logs from robotic systems, example videos)</li> <li>animal tracking analysis output</li> </ul> <p>See the readme and summary files contained within the archives for further details.</p>
VR-Together Pilot 3: 3D Character Models and Animation Data
<p>VR-Together Pilot 3 Character and Animation Dataset.</p> <p>This dataset contains the 3D characters and animations as used in <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>. It contains the 4 characters of the associated experience and their post-processed motion capture animation data in the FBX format, as well as the texture data in the PNG format. </p> <p>The data contained in this dataset was prepared for the Unity game engine, but should be usable in other content creation systems without issue. </p> <p>VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.</p>
Height and density data for marine animal forest forming species
<p>Data file (.csv) of values of height and density for marine animal forest (MAF) forming species collected from the literature. Fields are for discrete observations:</p> <table> <tbody> <tr> <td>Species</td> <td>Species name</td> </tr> <tr> <td>Santavy</td> <td> <p>Morphology class using categories in </p> <p><span>Santavy DL, Lee A. Courtney, William S. Fisher, Robert L. Quarles, Stephen J. Jordan (2013) Estimating surface area of sponges and gorgonians as indicators of habitat availability on Caribbean coral reefs. Hydrobiologia 707:1-16. <span>https://doi.org/10.1007/s10750-012-1359-7</span><br></span></p> </td> </tr> <tr> <td>Height</td> <td>Mean colony height (cm)</td> </tr> <tr> <td>Density</td> <td>Mean colony density m-2</td> </tr> <tr> <td>Btemp</td> <td>Average bottom temperature for species based on OBIS records (K)</td> </tr> <tr> <td>Depth</td> <td>Average depth for species based on OBIS records (m)</td> </tr> <tr> <td>Phylum</td> <td>Taxonomy</td> </tr> <tr> <td>Class</td> <td>Taxonomy</td> </tr> <tr> <td>Order</td> <td>Taxonomy</td> </tr> <tr> <td>Family</td> <td>Taxonomy</td> </tr> <tr> <td>Genus</td> <td>Taxonomy</td> </tr> <tr> <td>Source</td> <td>Publication source for data</td> </tr> <tr> <td>Title</td> <td>Publication title</td> </tr> <tr> <td>DOI/link</td> <td>DOI for source data</td> </tr> </tbody> </table>
Number of bovine animals tested by age group, reporting country and target group, 2022
<p>The tables contain the number of bovine animals tested by age group, reporting country and target group, 2020.</p>
Dataset: Features of animal babbling in the vocal ontogeny of the gray mouse lemur
<p>Dataset used in the unsupervised cluster analysis of the publication "Features of animal babbling in the vocal ontogeny of the gray mouse lemur (<em>Microcebus murinus</em>)"</p> <p><strong>Abstract</strong></p> <p>In human infants babbling is an important developmental stage of vocal plasticity to acquire maternal language. To investigate parallels in the vocal development of human infants and non-human mammals, seven key features of human babbling were defined, which are up to date only shown in bats and marmosets. This study will explore whether these features can also be found in gray mouse lemurs by investigating how infant vocal streams gradually resemble the structure of the adult trill call, which is not present at birth. Using unsupervised clustering, we distinguished six syllable types, whose sequential order gradually reflected the adult trill. A subset of adult syllable types was produced by several infants, with the syllable production being rhythmic, repetitive, and independent of the social context. The temporal structure of the calling bouts and the tempo-spectral features of syllable types became adult-like at the age of weaning. The age-dependent changes in the acoustic parameters differed between syllable types, suggesting that they cannot solely be explained by physical maturation of the vocal apparatus. Since gray mouse lemurs exhibit five features of animal babbling, they show parallels to the vocal development of human infants, bats, and marmosets.</p> <p> </p> <p>For details concerning the recording of the calling bouts confer to the publication at doi:10.1038/s41598-023-47919-7</p>
Plant metabolites modulate animal social networks and lifespan
<p><span>Social interactions influence disease spread, information flow, and resource allocation across species, yet heterogeneity in social interaction frequency and its fitness consequences remain poorly understood. Additionally, animals can utilize plant metabolites for purposes beyond nutrition, but whether that shapes social networks is unclear. Here, we investigated how non-nutritive plant metabolites impact social interactions and the lifespan of the turnip sawfly, <em>Athalia rosae</em>. Adult sawflies acquire neo-clerodane diterpenoids ('clerodanoids') from non-food plants, showing intraspecific variation in natural populations and laboratory-reared individuals. Clerodanoids can also be transferred between conspecifics, leading to increased agonistic social interactions. Network analysis indicated increased social interactions <span>in sawfly groups where some or all individuals had prior access to clerodanoids</span>. Social interaction frequency varied with clerodanoid status, with fitness costs including reduced lifespan resulting from increased interactions. Our findings highlight the role of intraspecific variation in the acquisition of non-nutritional plant metabolites in shaping social networks, with fitness implications on individual social niches.</span></p>
Bio-logger Ethogram Benchmark: A benchmark for computational analysis of animal behavior, using animal-borne tags
<p>This repository contains the datasets and experiment results presented in our <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a>:</p> <blockquote> <p>B. Hoffman, M. Cusimano, V. Baglione, D. Canestrari, D. Chevallier, D. DeSantis, L. Jeantet, M. Ladds, T. Maekawa, V. Mata-Silva, V. Moreno-González, A. Pagano, E. Trapote, O. Vainio, A. Vehkaoja, K. Yoda, K. Zacarian, A. Friedlaender, "A benchmark for computational analysis of animal behavior, using animal-borne tags," 2023.</p> </blockquote> <p>Standardized code to implement, train, and evaluate models can be found at <a href="https://github.com/earthspecies/BEBE/">https://github.com/earthspecies/BEBE/</a>. </p> <p>Please note the licenses in each dataset folder.</p> <p><strong>Zip folders beginning with "formatted":</strong> These are the datasets we used to run the experiments reported in the benchmark paper. </p> <p><strong>Zip folders beginning with "raw": </strong>These are the unprocessed datasets used in BEBE. Code to process these raw datasets into the formatted ones used by BEBE can be found at <a href="https://github.com/earthspecies/BEBE-datasets/">https://github.com/earthspecies/BEBE-datasets/</a>.</p> <p><strong>Zip folders beginning with "experiments": </strong>Results of the cross-validation experiments reported in the paper, as well as hyperparameter optimization. Confusion matrices for all experiments can also be found here. Note that dt, rf, and svm refer to the feature set from Nathan et al., 2012.</p> <p><em>Results used in Fig. 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (deep neural networks vs. classical models)</em><br>{dataset}_ harnet_nogyr<br>{dataset}_CRNN<br>{dataset}_CNN<br>{dataset}_dt<br>{dataset}_rf<br>{dataset}_svm<br>{dataset}_wavelet_dt<br>{dataset}_wavelet_rf<br>{dataset}_wavelet_svm</p> <p><em>Results used in Fig. 5D of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (full data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN_nogyr<br>{dataset}_rf_nogyr<br><br>Otherwise:<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN<br>{dataset}_rf</p> <p><em>Results used in Fig. 5E of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (reduced data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data_nogyr<br>{dataset}_rf_low_data_nogyr</p> <p>Otherwise:<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data<br>{dataset}_rf_low_data<br><br></p> <p><strong>CSV files</strong>: we also include summaries of the experimental results in experiments_summary.csv, experiments_by_fold_individual.csv, experiments_by_fold_behavior.csv. </p> <p><em>experiments_summary.csv - results averaged over individuals and behavior classes<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>f1_mean (float): mean of macro-averaged F1 score, averaged over individuals in test folds<br>f1_std (float): standard deviation of macro-averaged F1 score, computed over individuals in test folds<br>prec_mean, prec_std (float): analogous for precision<br>rec_mean, rec_std (float): analogous for recall<em><br><br>experiments_by_fold_individual.csv - results per individual in the test folds<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>individual (int): individuals are numbered zero-indexed, starting from fold 1<br>f1 (float): macro-averaged f1 score for this individual<br>precision (float): macro-averaged precision for this individual<br>recall (float): macro-averaged recall for this individual<em><br></em></p> <p><em>experiments_by_fold_behavior.csv - results per behavior class, for each test fold<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>behavior_class (str): name of behavior class<br>f1 (float): f1 score for this behavior, averaged over individuals in the test fold<br>precision (float): precision for this behavior, averaged over individuals in the test fold<br>recall (float): recall for this behavior, averaged over individuals in the test fold<br>train_ground_truth_label_counts (int): number of timepoints labeled with this behavior class, in the training set<em><br></em></p>
Systems-level design principles of metabolic rewiring in an animal
<p><strong>Description: </strong></p> <p>This repository contains all source codes, necessary input and output data, and raw figures and tables for reproducing all figures and results published in the following study: </p> <p>Xuhang Li#, Hefei Zhang#, Thomas Hodder, Wen Wang, L. Safak Yilmaz, Chad L. Myers, Albertha J.M. Walhout. Systems-level Worm Perturb-Seq reveals design principles of metabolic rewiring. (2025)<em> Nature, </em>in press (# equal contribution)</p> <p><strong>Files:</strong></p> <p>This repository contains the entire project working directory for this publication. To deposit into Zenodo, we have individually zipped each subfolder of the root directory. To reproduce our study, it is advised that one should download the entire Zenodo repository and unzip every zip file to produce the corresponding subfolders. The "root.zip" should be directly unzipped into the root directory of this project (not your system root!!). </p> <p>The recovered working directory should be like this: </p> <ul> <li><strong>10_revision</strong>/</li> <li><strong>1_QC_dataCleaning</strong>/</li> <li><strong>2_DE</strong>/</li> <li><strong>3_imageAnalysis</strong>/</li> <li><strong>4_networkAnalysis</strong>/</li> <li><strong>5_GSA</strong>/</li> <li><strong>7_FBA_modeling</strong>/</li> <li><strong>data_and_tables_to_publish</strong>/</li> <li><strong>input_data</strong>/</li> <li>generate_all_tables_for_publication.R</li> <li>prepareTbls_met12.R</li> <li>prepareTbls_metVali1_OCC.R</li> <li>prepareTbls_metVali2_b12.R</li> <li>prepareTbls_metVali3_detox.R</li> <li>prepareTbls.R</li> <li>prepareTbls_SPECIAL.R</li> <li>REWIRING_FIGURE_LOOKUP.xlsx</li> </ul> <p>Please be advised that this repository contains raw codes and data that are not directly related to a figure in our paper. However, they may be useful to generate input used in the analysis of a figure, or to reproduce tables in our manuscript. It may also contain unpublished analyses and figures, which we did not intentionally delete and kept for records. </p> <p><strong>Usage: </strong></p> <p>Please refer to the table in below to locate a specific file for reproducing a figure of interest (also availabe in the <em>REWIRING_FIGURE_LOOKUP.xlsx </em>under the root directory). </p> <table> <tbody> <tr> <td>Figure</td> <td>File</td> <td>Lines<sup>a</sup></td> <td>Notes</td> </tr> <tr> <td>Fig. 1b</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>1-190</td> <td> </td> </tr> <tr> <td>Fig. 1c</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-83</td> <td> </td> </tr> <tr> <td>Fig. 1d</td> <td>11_additional analysis/special RNAi.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 1e</td> <td>2_DE/output/metabolic_GRN_layout2.cys</td> <td>open in Cytoscape</td> </tr> <tr> <td>Fig. 1f</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-71</td> <td> </td> </tr> <tr> <td>Fig. 1g</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-145</td> <td> </td> </tr> <tr> <td>Fig. 2a</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>1-235</td> <td> </td> </tr> <tr> <td>Fig. 2b</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>232-287</td> <td> </td> </tr> <tr> <td>Fig. 2c</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>468-765</td> <td>For producing the embedding, see "2_DE/clustering_python/cluster_RNAi_UMAP_DBSCAN.ipynb"</td> </tr> <tr> <td>Fig. 2d</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-181</td> <td> </td> </tr> <tr> <td>Fig. 2e</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-338</td> <td> </td> </tr> <tr> <td>Fig. 2f</td> <td>2_DE/6_2_rewiring_summary_map_PCCtree.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 3b</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2539</td> <td> </td> </tr> <tr> <td>Fig. 3c</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2506</td> <td>the published figure uses level 4 and up direction (2_DE/figures/1_pathway_level_analysis/rxn_and_pathway_rewiring_LEVEL4_up.pdf)</td> </tr> <tr> <td>Fig. 3d</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2183-2235</td> <td> </td> </tr> <tr> <td>Fig. 3e</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>1751-2159</td> <td> </td> </tr> <tr> <td>Fig. 3g</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>803-807</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Fig. 3h</td> <td>11_additional analysis/glycine tracing/glycine RNAi tracing.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Fig. 3j</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>1-601</td> <td> </td> </tr> <tr> <td>Fig. 3k</td> <td>4_networkAnalysis/1_rewiring_network_modules.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 4a</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>853-881</td> <td>run lines 1-100 to load dependency; Euler plot depends on a random seeds; we manually picked one output figure that has best visual representations; see the corresponding Github or "7_FBA_modeling/CR_model_final_run/a1_gene_obj_classification.m" for generating the input matrix (core function matrix)</td> </tr> <tr> <td>Fig. 4b</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_gspd_1_example_objHeatmap.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 4c</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>1-270</td> <td> </td> </tr> <tr> <td>Fig. 4d</td> <td>7_FBA_modeling/CR_model_final_run/3_DEG_modeling_edge_direction_test_randFBA.R, 7_FBA_modeling/CR_model_final_run/3_DEG_modeling_edge_direction_test_randGRN.R</td> <td>entire file</td> <td>Reproducing this figure is complex because it integrates multiple randomization results; search "fitted_CR_interaction_diagram.pdf" or "fitted_CR_interaction_diagram_randGRN.pdf" to locate the code for making the figure (two randomizations, respectively), and track up for a few lines to load the dependency. To fully reproduce the analysis, follow the instructions in the Github and you will need to execute all four files named "3_DEG_modeling_edge_*.R".</td> </tr> <tr> <td>Fig. 4f</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-154</td> <td>numbers are in variable "model_explained"</td> </tr> <tr> <td>Fig. 4g (left)</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 342-345</td> </tr> <tr> <td>Fig. 4g (right)</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_parameter_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 5a</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Fig. 5b</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/3_DEG_modeling_edge_direction_test_randFBA.R</td> <td>entire file and see notes</td> <td>Reproducing this figure is complex because it integrates multiple randomization results; search "fitted_CR_interaction_diagram.pdf" to locate the code for making the figure, and track up for a few lines to load the dependency. To fully reproduce the analysis, follow the instructions in the Github and you will need to execute all two files named "3_DEG_modeling_edge_*.R".</td> </tr> <tr> <td>Fig. 5c</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 343-346</td> </tr> <tr> <td>Fig. 5d</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_DEG_cutoff_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1a</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>193-219</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1b</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>1-91</td> <td>the published figure was remade manually for prettier representations, the plotting function for which is not provided in this code. However, the raw data stays the same.</td> </tr> <tr> <td>Extended Data Fig. 1c</td> <td>2_DE/SUPP_extra_figures_for_rewiring.R</td> <td>280-379</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1d</td> <td>4_networkAnalysis/s2_rewiring_and_network_GPR.R</td> <td>387-555</td> <td>some inputs were generated by "4_networkAnalysis/s2_rewiring_and_network_GPR.m"</td> </tr> <tr> <td>Extended Data Fig. 1e</td> <td>4_networkAnalysis/s2_rewiring_and_network_GPR.R</td> <td>387-555</td> <td>some inputs were generated by "4_networkAnalysis/s2_rewiring_and_network_GPR.m"</td> </tr> <tr> <td>Extended Data Fig. 1f</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>170-257</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1g</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>170-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1h</td> <td>11_additional analysis/Wormsize/plot for publishing.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1i</td> <td>11_additional analysis/Wormsize/plot for publishing.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2a</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-61</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2b</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-180</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2c</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-152</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2d</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>364-397</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2e</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>364-383</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2f</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-264</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2g</td> <td>5_GSA/1_DE_annotation_pipeline.R</td> <td>1389-1522</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 3</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>468-765</td> <td>For producing the embedding, see "2_DE/clustering_python/cluster_RNAi_UMAP_DBSCAN.ipynb"</td> </tr> <tr> <td>Extended Data Fig. 4a</td> <td>4_networkAnalysis/s3_rewiring_and_network_connections.R</td> <td>1-254</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4b</td> <td>4_networkAnalysis/s3_rewiring_and_network_connections.R</td> <td>258-303</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4c</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-283</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4d</td> <td>2_DE/SUPP_extra_figures_for_rewiring.R</td> <td>725-813</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4e</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-589</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4f</td> <td>11_additional analysis/regulators_nhr_sbp_tor_GEP_mGRN.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 5a</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-224</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 5b</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-92</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6a</td> <td>2_DE/6_1_biCluster_heatmaps_PCCtree.R (top row); 2_DE/6_2_rewiring_summary_map_PCCtree.R (bottom row)</td> <td>top row: 210-219; 229-238; bottom row: 1-162; 162-229</td> <td>These two scripts produced the last two figures in the first row and the first two figures in the second row. The remaining two figures are repetitive with other main/extended data figures, whose source code were specified there</td> </tr> <tr> <td>Extended Data Fig. 6b</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>1-184</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6c</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>189-319</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6d</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>326-480</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 7a-e</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-189</td> <td>The annotation bar plot is in 2_DE/SUPP_extra_figures_for_rewiring.R lines 394-715</td> </tr> <tr> <td>Extended Data Fig. 8a</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>649-753</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8b</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>649-785</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8c</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>759-764</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8d</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2506</td> <td>the published figure uses level 4 and up direction (2_DE/figures/1_pathway_level_analysis/rxn_and_pathway_rewiring_LEVEL4_up.pdf)</td> </tr> <tr> <td>Extended Data Fig. 8e</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>803-807</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 8f</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>870-874</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 8g</td> <td>11_additional analysis/uPhe tracing/uphe RNAi.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Extended Data Fig. 9a</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>1-601</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 9b</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>691-714</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 9c</td> <td>11_additional analysis/uGlucose tracing/uGlu rewiring.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Extended Data Fig. 9d</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>607-629</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 9e</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>636-685</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 9f</td> <td>4_networkAnalysis/1_rewiring_network_modules.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 10a</td> <td>2_DE/6_2_rewiring_summary_map_PCCtree.R</td> <td>entire file</td> <td>This figure is a subset of fig. 2f</td> </tr> <tr> <td>Extended Data Fig. 10c</td> <td>7_FBA_modeling/REVISION/1_cluster_CR_model.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11a</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_excludeMultiObjGenes.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11b</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-225</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11c</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 337-340</td> </tr> <tr> <td>Extended Data Fig. 11d</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 352-355</td> </tr> <tr> <td>Extended Data Fig. 11e</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 362-365</td> </tr> <tr> <td>Extended Data Fig. 11f</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>486-742</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12a</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>300-329</td> <td>run lines 1-101 to load dependency; Euler plot depends on a random seeds; we manually picked one output figure that has best visual representations; see the corresponding Github or "7_FBA_modeling/REVISION/human_perturb_seq/a1_gene_obj_classification.m" for generating the input matrix (core function matrix)</td> </tr> <tr> <td>Extended Data Fig. 12b</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/1_EDA_of_human_perturb_seq.R</td> <td>1-176</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12c</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12d</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12e</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-224</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12f</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 337-341</td> </tr> <tr> <td>Extended Data Fig. 12g</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 353-356</td> </tr> <tr> <td>Extended Data Fig. 12h</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 363-366</td> </tr> <tr> <td>Extended Data Fig. 12i</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_parameter_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2a</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>467-572</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2b</td> <td>2_DE/6_4_Wald_heatmap_each_RNAi_Cluster.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2c</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-389</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2d</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-308</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2e</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-234</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2f</td> <td>12_FLEX_benchmark/run_FLEX_eval.sh</td> <td>entire file</td> <td>the bash scripts calls corresponding R scripts for reproducing the figures. You will need to install the FLEX package to run these codes. </td> </tr> <tr> <td>Supplementary Data Fig. 3, 4</td> <td>N/A</td> <td>N/A</td> <td>No coding involved as these figures were made manually</td> </tr> <tr> <td>Supplementary Data Fig. 5</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>1-270</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 6</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>274-578</td> <td>please load the dependency starting from line 1</td> </tr> </tbody> </table> <p><em><strong>a: The lines indicate the chunk of codes to reproduce the corresponding figure; The figure is reproduced at the end of the referred codes. Please note that you may have to run the codes above the referred chunk (i.e., from the first line) to load dependent variables to execute the referred codes. However, you should be able to reproduce the figure only with the codes within the referred script. </strong></em></p> <p><strong>Notice:</strong></p> <p>We advise you download the entire project working directory (including all zip files and unzip them into corresponding folders), for reproducing any analysis. If you only download the zip file relevant to your figure of interest, you may or may not run into issues due to the missing files in another folder. </p> <p><strong>Contact:</strong></p> <p><strong>For any questions, please contact Xuhang (Hang) Li at Xuhang.Li@umassmed.edu. </strong><strong>(<em>we plan to deposit an updated version with better code annotations, but cannot complete that currently due to time restirctions</em>)</strong></p> <p> </p>
MATEdb2, a Collection of High-Quality Metazoan Proteomes across the Animal Tree of Life to Speed Up Phylogenomic Studies
<p>Recent advances in high-throughput sequencing have exponentially increased the number of genomic data available for animals (Metazoa) in the last decades, with high-quality chromosome-level genomes being published almost daily. Nevertheless, generating a new genome is not an easy task due to the high cost of genome sequencing, the high complexity of assembly, and the lack of standardized protocols for genome annotation. The lack of consensus in the annotation and publication of genome files hinders research by making researchers lose time in reformatting the files for their purposes but can also reduce the quality of the genetic repertoire for an evolutionary study. Thus, the use of transcriptomes obtained using the same pipeline as a proxy for the genetic content of species remains a valuable resource that is easier to obtain, cheaper, and more comparable than genomes. In a previous study, we presented the Metazoan Assemblies from Transcriptomic Ensembles database (MATEdb), a repository of high-quality transcriptomic and genomic data for the two most diverse animal phyla, Arthropoda and Mollusca. Here, we present the newest version of MATEdb (MATEdb2) that overcomes some of the previous limitations of our database: (i) we include data from all animal phyla where public data are available, and (ii) we provide gene annotations extracted from the original GFF genome files using the same pipeline. In total, we provide proteomes inferred from high-quality transcriptomic or genomic data for almost 1,000 animal species, including the longest isoforms, all isoforms, and functional annotation based on sequence homology and protein language models, as well as the embedding representations of the sequences. We believe this new version of MATEdb will accelerate research on animal phylogenomics while saving thousands of hours of computational work in a plea for open, greener, and collaborative science.</p>
Data and Videos for Argos: a toolkit for tracking multiple animals in complex visual environments
<p>Original videos used and data generated for the article "Argos: a toolkit for tracking multiple animals in complex visual environments".</p> <p>The data contains original videos used as input to the Argos Tracking tool, the generated raw tracks in Pandas-HDF5 format, and the corrected tracks after processing with Argos Review tool.</p> <p>It also includes a zip archive with ground truth tracks along with tracks detected from two videos by Argos and several other tracking tools for comparison using the HOTA metric organized in a folder structure suitable for the TrackEval tool.</p>
Animation to visualize the effects of laser ablation on unhydrated cement clinker
<p>This animation illustrates the effect of the damage on the surface of unhydrated cement clinker caused by a pulsed laser for a LA-ICP-MS mapping. The dataset contains the raw data and the final animations.</p> <p>The images were acquired using a Thermofischer Scientific Helios G4 UX microscope at 2 kV/0.1 nA. The surface was tilted in two orientations by a few degree and an image was acquired after every tilt. The ablated area has a size of approx. 367 x 300 µm.</p> <p>A detailed description of the specimen and the parameters used for the analysis can be found in the <a href="https://doi.org/10.1016/j.cemconres.2022.106875">corresponding paper</a>.</p> <p><strong>Funding</strong></p> <p>The research was supported by the Deutsche Forschungsgemeinschaft (DFG), grant number <a href="https://gepris.dfg.de/gepris/projekt/344069666">344069666</a>.</p>
19th Century United States Newspaper images predicted as Photographs with labels for "human", "animal", "human-structure" and "landscape"
<p>The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (<a href="https://chroniclingamerica.loc.gov/">chroniclingamerica.loc.gov/</a>). </p> <blockquote> <p>[The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in <em>Chronicling America</em>. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the <a href="https://labs.loc.gov/work/experiments/beyond-words/">Beyond Words</a> crowdsourcing project.</p> <p>source:<a href="https://news-navigator.labs.loc.gov/"> https://news-navigator.labs.loc.gov/</a></p> </blockquote> <p>One of these categories is 'photographs'. This dataset contains a sample of these images with additional labels indicating if the photograph has one or more of the following labels: "human", "animal", "human-structure" and "landscape"</p> <p>The data is organised as follows:</p> <ul> <li>The images themselves can be found in `images.zip`</li> <li>`newspaper-navigator-sample-metadata.csv` contains metadata about each image drawn from the Newspaper Navigator Dataset.</li> <li>`multi_label.csv` contains the labels for the images as a CSV file</li> <li>`annotations.csv` conains the labels for the images with additional metadata</li> </ul> <p>This dataset was created for use in an under-review Programming Historian tutorial (<a href="http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt2">http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt2</a>) The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material. The data is shared here since it may be useful for others. <strong>This data documentation is a work in progress and will be updated when the Programming Historian tutorial is released publicly. </strong></p> <p>The metadata CSV file contains the following columns:</p> <p>- filepath<br> - pub_date<br> - page_seq_num<br> - edition_seq_num<br> - batch<br> - lccn<br> - box<br> - score<br> - ocr<br> - place_of_publication<br> - geographic_coverage<br> - name<br> - publisher<br> - url<br> - page_url<br> - month<br> - year<br> - iiif_url</p>
Physiological information in Canadian species at risk recovery strategies (animals)
<p>Summary of physiological information contained in Canadian (animal) species at risk recovery strategies (SARA). For each recovery strategy, the dataset includes the species covered, publication dates, taxonomic classification, the number of times physiology was mentioned, the types of physiological traits included, and the threats the associated organism faces.</p>
Data from: Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?
<p><strong>Abstract</strong></p> <p>Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals. Extracting information on survival and space use from observation data, in particular dead recovery data, requires explicitly identifying the observation process. We build a fully stochastic model for animals marked in populations of origin, which were found dead in spatially discrete destination areas. It acts on the population level and includes parameters for use of space, survival and recovery probability. The model is based on the division coefficient and the multinomial reencounter model. We use a likelihood-based approach, derive Restricted Maximum Likelihood-like estimates for all parameters and prove their existence and uniqueness. In a simulation study we demonstrate the performance of the model by using Bayesian estimators derived by the Markov chain Monte Carlo method. We obtain unbiased estimates for survival and recovery probability if the sample size is large enough. Moreover, we apply the model to real-world data of European robins <em>Erithacus rubecula</em> ringed at a stopover site. We obtain annual survival estimates for different spatially discrete non-breeding areas. Additionally, we can reproduce already known patterns of use of space for this species. We would like to thank the Greifswalder Oie Bird Observatory of the Verein Jordsand, Ahrensburg, and the Hiddensee Bird Ringing Centre, Güstrow, for providing the robin data.</p>
Supporting data for "Estimating animal density for a community of species using information obtained only from camera-traps"
<p>Data underlying a paper published in Methods in Ecology and Evolution (<a href="https://doi.org/10.1111/2041-210X.13930">https://doi.org/10.1111/2041-210X.13930</a>).</p> <p>These data are suitable for estimating animal density using the Random Encounter Model and include: i) detection counts for 35 species across 510 camera-trap locations; ii) movement speeds (estimated by tracking animal movements in camera-trap image sequences), iii) activity times (filtered so that records of the same species at the same location are > 60 minutes apart), and iv) measurements of the angular and radial distance from camera-traps for animals that were detected.</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.