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F. 1 in A Long-term Demographic Study of a Spotted Salamander (Ambŋstoma maculatum) Population in Central Ohio
F. 1.—Numbers of (a) breeding Ambŋstoma maculatum adults (R2 ¼ IG 0.46, P ¼ 0.03), (b) split by sex (females, R2 ¼ 0.02, P ¼ 0.67; males, R2 ¼ 0.68, P ¼ 0.003), and (c) counts of breeding females plotted alongside egg mass counts and numbers of emerging juveniles from 2005 to 2014 in Taylor–Ochs Pond, Ohio. Numbers near the bottom of (b) reflect sex ratios (male:female), and numbers near the bottom of (c) reflect recruitment rates (emerging juveniles per breeding female) for each year. Significant negative linear relationships of overall breeding adults (a) and breeding males (b) over time are represented by the equations y ¼ 46.1x þ 93246.0 and y ¼ 41.6x þ 84148.9, respectively.
FIG. 2 in A Long-term Demographic Study of a Spotted Salamander (Ambŋstoma maculatum) Population in Central Ohio
FIG. 2.—Mean values (±1 SE) for mass (a) and snout–vent length (SVL, b) of inbound female and male Ambŋstoma maculatum sampled from 2005 to 2014 in Taylor–Ochs Pond, Ohio. See Table 3 for the sample size contributing to each depicted value. In all years, for both body-size indicators, females were larger than males (P <0.05). For simplicity, only differences among years (sexes combined) are reflected by different letter(s) above each pair of bars.
Social structure and individuals' behavior shaped by group composition in long-term tracking of rats
<p>Trajectory data and behavioral metrics from long-term tracking of groups of rats. Experiments performed in 2012 at Eötvös Loránd University, Budapest, Hungary.</p> <p>The data here is used in the following publication. See the publication for information on experimental structure and notation.<br> Nagy, Máté, Jacob Davidson, Gábor Vásárhelyi, Dániel Ábel, Enikő Kubinyi, Ahmed El Hady, and Tamás Vicsek. 2023. “Social Structure and Individuals’ Behavior Shaped by Group Composition in Long-Term Tracking of Rats.” bioRxiv. <a href="https://doi.org/10.1101/2023.03.18.533183">https://doi.org/10.1101/2023.03.18.533183</a>.</p> <p>Github repository to reproduce results in the paper: <a href="https://github.com/jacobdavidson/ratsocialgroups">github.com/jacobdavidson/ratsocialgroups</a><br> Github repositories used for tracking and computation of behavioral metrics: <a href="https://github.com/vasarhelyi">github.com/vasarhelyi</a></p> <p>The behavioral metrics, approach-avoidance networks, individual labels and group membership information, and behavioral assay data needed to reproduce the results in the publication are included in the zip file <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/metrics-networks-labels.zip">metrics-networks-labels.zip</a>. This includes the following files in .csv format:</p> <p><strong>Naming/labels:</strong></p> <ul> <li><strong>uidlabels.csv</strong>: References the ranked-mass labels used in the paper to the 3-color-code labels that identify each rat from the tracking. "Group" is the membership during the original groups (Period 1), and "GroupG" is membership during the remixed groups (Period 3). <table> <tbody> <tr> <td> </td> <td> <p><strong>masslabel</strong></p> </td> <td> <p><strong>GroupMassRank-Exp7</strong></p> </td> <td> <p><strong>Group</strong></p> </td> <td> <p><strong>GroupG</strong></p> </td> </tr> <tr> <td> <p><strong>GOB</strong></p> </td> <td> <p>a5</p> </td> <td> <p>5.0</p> </td> <td> <p>A1</p> </td> <td> <p>G3</p> </td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> </li> <li><strong>groupnames.csv</strong>: Group membership information for each rat, during each experimental period <table> <tbody> <tr> <td> <p><strong>Experiments</strong></p> </td> <td> <p><strong>GOB</strong></p> </td> <td> <p><strong>OBG</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>1-7</strong></p> </td> <td> <p>A1</p> </td> <td> <p>A1</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> </ul> <p><strong>Behavioral metrics and approach-avoidance networks:</strong></p> <ul> <li><strong>metrics.csv: </strong>Behavioral metrics for each rat, calculated for each experiment. <table> <tbody> <tr> <td> <p><strong>Experiment</strong></p> </td> <td> <p><strong>metric</strong></p> </td> <td> <p><strong>GOB</strong></p> </td> <td> <p><strong>OBG</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p>AA</p> </td> <td> <p>0</p> </td> <td> <p>0.690964</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>Networks-Period1+Period3.csv: </strong>Approach-avoidance interaction networks for the groups of 7 rats in Period 1 and Period 3. <table> <tbody> <tr> <td> <p><strong>Experiment</strong></p> </td> <td> <p><strong>DayNight</strong></p> </td> <td> <p><strong>Group</strong></p> </td> <td> <p><strong>index</strong></p> </td> <td> <p><strong>winner</strong></p> </td> <td> <p><strong>loser0</strong></p> </td> <td> <p><strong>loser1</strong></p> </td> <td> <p><strong>loser2</strong></p> </td> <td> <p><strong>loser3</strong></p> </td> <td> <p><strong>loser4</strong></p> </td> <td> <p><strong>loser5</strong></p> </td> <td> <p><strong>loser6</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p>Day</p> </td> <td> <p>A1</p> </td> <td> <p>0</p> </td> <td> <p>GOB</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>Networks-Experiment8.csv</strong> and <strong>Networks-Experiment9.csv: </strong>Approach-avoidance interaction networks for the larger combined groups in the experiments during Period 2. </li> </ul> <p> </p> <p><strong>Behavioral assays results:</strong></p> <ul> <li><strong>IndividualTests.csv</strong>: Individual behavioral assay test results for the black and white box, elevated plus-maze, and canopy tests. <table> <tbody> <tr> <td> <p><strong>ID</strong></p> </td> <td> <p><strong>BWB-All Frames</strong></p> </td> <td> <p><strong>BWB-Black</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>BGP</strong></p> </td> <td> <p>7500</p> </td> <td> <p>6860</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>PairedTests.csv: </strong>Pairwise social interaction behavioral assay test results for tests with unfamiliar and familiar rats <table> <tbody> <tr> <td> <p><strong>rat_ID</strong></p> </td> <td> <p><strong>rat_group_N</strong></p> </td> <td> <p><strong>freq_standup_unfam1</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>BGP</strong></p> </td> <td> <p>2</p> </td> <td> <p>28</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> </ul> <p> </p> <p><strong>Trajectory data</strong> is included as multiple text files for each experiment in TSV format. #t is frame number, Color_code_name identifies each rat, Real_recognition(1)_or_virtual(0) = 1 is a detection of the rat's color codes while 0 is an interpolated value, x,y, and Orientation are the position and orientation of the rat.</p> <table> <tbody> <tr> <td>#t</td> <td>Color_code_name</td> <td>Real_recognition(1)_or_virtual(0) </td> <td>x</td> <td>y</td> <td>Orientation</td> </tr> <tr> <td>0</td> <td>ORG</td> <td>0</td> <td>872.4000</td> <td>778.7000</td> <td>-79.5000</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p> </p> <p><strong>Time-lapse video of the entire experiment:</strong> <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/ratlab_timelapse_entire_exp.avi?versionId=a58df78d-1291-4301-9763-86a97b9e146c">ratlab_timelapse_entire_exp.avi</a></p>
FIGURE 1 in Earthworm communities in long-term no-tillage systems and secondary forest fragments in Paraná, Southern Brazil
FIGURE 1. Location of the municipalities of the sampling sites in Paraná, Brazil. Light grey = Faxinal (FX), Black = Mauá da Serra (MS), Dark grey = Palmeira (PL)
Replication package for: Optimal Long-Term Health Insurance Contracts: Characterization, Computation, and Welfare Effects
<p>This is a replication package for: </p> <p>Ghili, S., Handel, B., Hendel, I. and Whinston, M. "Optimal Long-Term Health Insurance Contracts: Characterization, Computation, and Welfare Effects." </p> <p>The replication package .zip file contains a ReadMe, which provides instructions, and data matrices and code for the paper. </p>
Soil properties 0-60 cm from long-term forest biomass harvesting experiment in Quebec
<p>This is the data set that comes from the compilation from 0 to 60 cm in forest soils subjected to either stem-only harvesting or whole-tree harvesting in the boreal forest in Quebec in te 1980's. Soil sampling was made about 30 years after harvesting treatments. Further details on the sampling methodology and compilation can be found in DOI:10.3390/f12050583 and DOI: 10.3390/soilsystems7020039 .</p>
The NO2 monitoring data for "Satellite-based long-term spatiotemporal trends in ambient NO2 concentrations and attributable health burdens in China from 2005 to 2020"
<p>The current dataset is about NO2 monitoring data used in Geohealth manuscript entitled "Satellite-based long-term spatiotemporal trends in ambient NO2 concentrations and attributable health burdens in China from 2005 to 2020". It was collected from the air quality monitoring stations administered by the China National Environmental Monitoring Center. </p>
FIG. 6. Standardized Precipitation Index for the 6 in A Long-term Study of Home Range of Coachella Fringe-Toed Lizards, Uma inornata
FIG. 6. Standardized Precipitation Index for the 6-mo interval that encompasses winter rainfall at Palm Springs during the course of the study. Rainfall intervals were standardized against all other November through April intervals since 1927/28. An index of 0 to -0.99 indicates mild drought, -1.00 to -1.49 moderate drought, -1.50 to -1.99 severe drought, and -2.00 to -3.00 extreme drought. Positive index values indicate wet years and horizontal lines designate severe drought-year and severe wet-year thresholds.
FIG. 5 in A Long-term Study of Home Range of Coachella Fringe-Toed Lizards, Uma inornata
FIG. 5. Home range exhibits logistic growth over time for both sexes of Coachella Fringe-Toed Lizards (U. inornata). The asymptote estimated from a logistic growth equation is 1,025 m2 for females and 4,523 m2 for males.
FIG. 2 in A Long-term Study of Home Range of Coachella Fringe-Toed Lizards, Uma inornata
FIG. 2. Age-size relationship for the Coachella Fringe-Toed Lizard (Uma inornata) showing the mean length (SVL) at each age with sample sizes for all individuals measured during the study. Vertical bars are at 95% confidence interval (CI). Data include individuals that were measured multiple times during the study (n = 3,316).
FIG. 1 in A Long-term Study of Home Range of Coachella Fringe-Toed Lizards, Uma inornata
FIG. 1. The study site showing sparse vegetation and aeolian sand; lizards use the accretion dunes that form on the leeward side of vegetation. An adult female Coachella Fringe-Toed Lizard (Uma inornata) is shown in the inset.
FIG. 3 in A Long-term Study of Home Range of Coachella Fringe-Toed Lizards, Uma inornata
FIG. 3. Sighting frequency of all individual Coachella Fringe-Toed Lizards (Uma inornata) between 1985 and 2015.
FIG. 4 in A Long-term Study of Home Range of Coachella Fringe-Toed Lizards, Uma inornata
FIG. 4. Example of changes in HR size and location over the lifetime of an individual male Coachella Fringe-Toed Lizard (U. inornata) from the 1995 hatchling cohort.
Repeated failures of the giant Beshkiol landslide and its impact on the long-term Naryn Basin floodings, Kyrgyz Tien Shan.
<p>Drone video taken in 2022 showing the different study sites (Kyrgyz Tien Shan) :</p> <p>From 00:00:00 to 00:00:30 min, seconds : Beshkiol area with view on the basal contact of one of the secondary landslides and the upstream lake levels (shown in figure 4 and 5).</p> <p>From 00:00:31 to 00:01:27 min, seconds: Section overview on the cliff of Kok-Dhzar presented in figure 3.</p> <p>From 00:01:27 to 00:01:56 min, seconds: Kok-Dhzar village and landscape view with the sedimentary sequence and the alluvial fans.</p>
Replication Package for ESEC/FSE 2023 Paper "How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?"
<p>This replication package can be used for replicating results in the paper. It contains 1) a dataset of 290,255 repositories; and 2) Python scripts for training and interpreting models. The GitHub repository of the paper is available at <a href="https://github.com/mcxwx123/Sustainable_projects">https://github.com/mcxwx123/Sustainable_projects</a>.</p> <p>We recommend manually setup the required environment in a commodity Linux machine with at least 1 CPU Core, 8GB Memory and 100GB empty storage space. We conduct development and execute all our experiments on a Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage.</p> <p>We use GHTorrent to restore historical states of 290,255 repositories with more than 57 commits, 4 PRs, 1 issue, 1 fork and 2 stars. The raw data of repositories (collected in their first 1,3,5 months(s)) are stored in `Replication Package/data/prodata_1.pkl`, `Replication Package/data/prodata_3.pkl`, and `Replication Package/data/prodata_5.pkl`. The contribution of features resulting from LIME model is stored in `Replication Package/data/limeres_m3_t2_k1.pkl`.<br> `Replication Package/data/X_test_m3_t2_k1.pkl` and `Replication Package/data/y_test_m3_t2_k1.pkl` store the test dataset for the LIME model. You can run `Replication Package/fitdata.py` to get the results in Table 3 and 4, run `Replication Package/draw_compare_variable.py` to get Figure 2 and run `Replication Package/allvari_statistics.py` to get Table 5. In `Replication Package/Variable_comparison_with_different_parameter.pdf`, we show the LIME results under different parameters. In `Replication Package/sample_pros.csv`, we also provide the list of randomly selected repositories in Section 3.1.<br> </p> <p>The explanations for collecting the variables, the examples of variable effects on project sustainability, and the hyperparameter setting of the machine learning models are provided in the README.md file.</p>
A novel immune-related prognostic signature based on Chemoradiotherapy sensitivity predicts long-term survival in patients with esophageal squamous cell carcinoma.
<p>Raw Data for PeerJ</p>
Thermal adaptation of microbial respiration persists throughout long-term soil carbon decomposition
<p>The microbial respiration data supporting the main findings of the study titled 'Thermal adaptation of microbial respiration persists throughout long-term soil carbon decomposition'. Mass-specific microbial respiration was consistently lower under higher long-term incubation temperatures, suggesting the occurrence and persistence of microbial thermal adaptation in long-term soil carbon decomposition.</p>
Fig. 3 in Transcriptional response of giant reed (Arundo donax L.) low ecotype to long-term salt stress by unigene-based RNAseq
Fig. 3. Distribution of transcription factors responsive to salt stress. Data are sorted by number of G34-S3 vs G34-CK DEGs.
Fig. 2 in Transcriptional response of giant reed (Arundo donax L.) low ecotype to long-term salt stress by unigene-based RNAseq
Fig. 2. GO enrichment analysis for the DEGs in A. donax (G34-S3 vs G34-CK) The X-axis indicates the numbers related to the total number of GO terms, and the Y-axis indicates the subcategories. BP, biological processes; CC, cellular components; MF, molecular functions.
Fig. 1 in Transcriptional response of giant reed (Arundo donax L.) low ecotype to long-term salt stress by unigene-based RNAseq
Fig. 1. Volcano plot showing the DEGs of G34-S3 vs G34-CK comparison. The up-regulated genes with statistically significance are represented by blue dots, the green dots represent the down-regulated genes and the red dots are DEGs with -log10padj <1.3, adopting log2FoldChange threshold of 0.58 (1.5 fold change). The X-axis is the gene expression change, and the Y-axis is the pvalue adjusted after normalization. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.