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Text-fig. 9.—Anterior portion of maxilla of the Jordan theropod (stippled) compared with the corresponding region of the maxillae of other tyrannosaurs. All reproduced to equal length from anterior end of maxilla to anterior margin of antorbital recess (this length in the actual specimens differs by less than 15 percent). A, Juvenile Tyrannosaurus rex. B, Albertosaurus lancensis. C, Juvenile Albertosaurus libratus (AMNH 5664). (A, redrawn from Lawson, 1976; B and C, redrawn from Gilmore, 1946). in A new Theropod Dinosaur from the Upper Cretaceous of Central Montana
Text-fig. 9.—Anterior portion of maxilla of the Jordan theropod (stippled) compared with the corresponding region of the maxillae of other tyrannosaurs. All reproduced to equal length from anterior end of maxilla to anterior margin of antorbital recess (this length in the actual specimens differs by less than 15 percent). A, Juvenile Tyrannosaurus rex. B, Albertosaurus lancensis. C, Juvenile Albertosaurus libratus (AMNH 5664). (A, redrawn from Lawson, 1976; B and C, redrawn from Gilmore, 1946).
Cloud-Repro: Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics
<p>In a new effort to make our research transparent and reproducible by others, we developed a workflow to run computational studies on a public cloud. It uses Docker containers to create an image of the application software stack. We also adopt several tools that facilitate creating and managing virtual machines on compute nodes and submitting jobs to these nodes. The configuration files for these tools are part of an expanded "reproducibility package" that includes workflow definitions for cloud computing, in addition to input files and instructions. This facilitates re-creating the cloud environment to re-run the computations under the same conditions.</p> <p>The present Zenodo dataset contains all secondary data required to reproduce the figures of the manuscript ("Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics") without running the CFD simulations again.</p>
Fig. 6 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig. 6. Nymphargus balionotus breeding behavior. (A) Calling male; (B) Male and female in amplexus; (C) Egg mass deposited on the bottom surface on a leaf. Photos by Ross J. Maynard (A) and Jaime Culebras (B–C).
Fig 5 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig 5. Iris variation in Nymphargus balionotus. Photos by Jaime Culebras (A–C) and Scott Trageser (D).
Fig. 4 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig. 4. Metamorphic life stage of Nymphargus balionotus; dorsal (A) and ventral (B) views (ZSFQ 3895). Photos by Ross J. Maynard.
Fig. 3 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig. 3. Pattern and color variation in adult Nymphargus balionotus. (A) Male, uncollected; (B) Male, ZSFQ 0531; (C) Male, uncollected; (D) Male, uncollected; (E–F) Male, ZSFQ 0532; (G–H) Gravid female, uncollected. Photos by Ross J. Maynard.
Fig. 2 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig. 2. Audio spectrogram (top), oscillogram (middle), and power spectrum (bottom) graphs of a single male Nymphargus balionotus advertisement call.
Fig. 1 in Discovery of a reproducing population of the Mindo Glassfrog, Nymphargus balionotus (Duellman, 1981), at the Río Manduriacu Reserve, Ecuador, with a literature review and comments on its natural history, distribution, and conservation status
Fig. 1. Distribution map of known localities for Nymphargus balionotus. Information tags to the left summarize data reported for each locality, including (in order): name of collection site, department/province, number of specimens reported, elevation, and year specimens were collected or observed. Blue circle represents the Río Manduriacu Reserve; red circle marks the type locality; yellow and gray circles represent remaining localities reported in the literature, with the gray (Campamento Chancos) population believed to be a distinct lineage by Cisneros-Heredia and McDiarmid (2006). Note: the "El Tambo" locality in the department of Cauca, Colombia, has been placed as accurately as possible based on available information (see Discussion).
Data to reproduce the results presented in Lake et al. 2024. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131930. ("High-frequency spatial sediment source fingerprinting using in situ absorbance data")
<p>This repository contains data on the used absorbance data, measured at the field site, as described in Lake et al., 2024 (<span>h</span><span>t</span><span>t</span><span>p</span><span>s</span><span>:</span><span>/</span><span>/</span><span>d</span><span>o</span><span>i</span><span>.</span><span>o</span><span>r</span><span>g</span><span>/</span><span>1</span><span>0</span><span>.</span><span>1</span><span>0</span><span>1</span><span>6</span><span>/</span><span>j</span><span>.</span><span>j</span><span>h</span><span>y</span><span>d</span><span>r</span><span>o</span><span>l</span><span>.</span><span>2</span><span>0</span><span>2</span><span>4</span><span>.</span><span>1</span><span>3</span><span>1</span><span>9</span><span>3</span><span>0).</span> Furthermore, data on the turbidity, used calibration curves and R code to prepare the input data for the MixSIAR model are included in the data repository.</p>
Validity and reproducibility of a food frequency questionnaire to determine dietary intakes among Lebanese athletes
<p><a name="_Hlk166496350"></a><span><strong>Background</strong> </span><strong><span>and objective:</span></strong><span><strong><span> </span></strong></span><span><span>Nutrition is a basic need for athletes; thus, adequate dietary intake is crucial for maintaining overall health, facilitating training adaptations and boosting athletic performance. Accurate dietary assessment tools are required to minimize the challenges faced by athletes. This study verifies the validity and reproducibility of a 157 item semi-quantitative food frequency questionnaire (FFQ) among Lebanese athletes. This is the only Arabic questionnaire in Lebanon that estimates food consumption for athletes which can also be used in Arabic speaking countries. There has been no previous validated food frequency questionnaire that estimated food consumption for athletes in Lebanon. </span></span><span><strong><span>Methods:</span></strong></span><span><strong><span> </span></strong></span><span><span>A total of 194 athletes were included in the study to assess the validity of the food frequency questionnaire against four days dietary recalls by comparing the total nutrient intake values from the food frequency questionnaire with the mean values of four 24-hour dietary recalls using Spearman correlation coefficient and Bland Altman plots. In order to measure the reproducibility, the intra class correlation coefficients were calculated by repeating the same food frequency questionnaire after one month. </span></span><span><strong><span>Results:</span></strong></span><span><strong><span> </span></strong></span><span><span>The intra-class correlation coefficient between the two-food frequency questionnaires ranged from average (0.739 for carbohydrates) to good (0.870 for energy (Kcal)), to excellent (0.919 for proteins) concerning macronutrients and ranged from average (0.688 for vitamin D), to excellent (0.952 for vitamin B12), indicating </span></span><span><span>an acceptable reproducibility. </span></span><span><span>Spearman’s correlation coefficients of dietary intake estimate from the food frequency questionnaire and the four dietary recalls varied between 0.304 for sodium, 0.469 for magnesium to 0.953 for caloric intake (kcal). Bland-Altman plots illustrated a percentage of agreement ranging between 94.3% for fats to 96.4% for proteins. </span></span><span><strong><span>Conclusion:</span></strong></span><span><strong><span> </span></strong></span><span><span>This food frequency questionnaire has a reliable validity and reproducibility to evaluate dietary assessments and is an appropriate tool for future interventions to ensure the adoption of adequate eating strategies by athletes.</span></span></p>
Data to Reproduce "Robust Automated Equilibration Detection for Molecular Simulations"
<p>Data to reproduce the results from "Robust Automated Equilibration Detection for Molecular Simluations" (see <a href="https://github.com/michellab/Robust-Equilibration-Detection-Paper">https://github.com/michellab/Robust-Equilibration-Detection-Paper</a> and the work linked there). These data are too large to host on GitHub, but are automatically downloaded by the workflow supplied at the above GitHub repository. </p> <p>All data were generated using the code given in the <a href="https://github.com/michellab/Robust-Equilibration-Detection-Paper">GitHub repository</a>, other than the original free energy gradient data <code>gradient_arrays_30ns.pkl</code> which were generated as described in <a title="DOI URL" href="https://doi.org/10.1021/acs.jctc.4c00806">https://doi.org/10.1021/acs.jctc.4c00806</a> (to regenerate, see the code available at: <a href="https://github.com/michellab/Automated-ABFE-Paper">https://github.com/michellab/Automated-ABFE-Paper</a>).</p> <p>The synthetic data used to test all equilibration detection heuristics are given in the <code>compute_equil_times</code> output directories (for example <code>synthetic_data_bound_vanish_with_equil_times.pkl</code>. These are supplied as pickled Python dictionaries with the structures <code>data[dataset_type][system][trace_index]["data"]</code>. For example, to access the first synthetic trace for the T4L system from the "standard" synthetic ensemble, use <code>data["standard"]["T4L"][0]["data"]</code>. For all directories, <code>_free</code> denotes the free vanish multi-window data and <code>_single</code> denotes the bound vanish single-window data - otherwise these are the standard bound vanish multi-window data. However, it is recommended that these data are used as part of the workflow given at <a href="https://github.com/michellab/Robust-Equilibration-Detection-Paper">https://github.com/michellab/Robust-Equilibration-Detection-Paper</a>, which allows the study to be reproduced from scratch.</p>
Dataset to reproduce the figures of "Clouds are crucial to capture Antarctic sea ice variability"
<p>These files, further described below, contain the necessary variables to reproduce the figures of Cesana et al. (submitted to GRL), which are not all publically available.</p> <p> </p> <p><strong>Observations</strong></p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/CERES_EBAF-TOA_Ed4.2_Subset_200101-202112_2x2.nc/content" target="_blank" rel="noopener noreferrer">CERES_EBAF-TOA_Ed4.2_Subset_200101-202112_2x2.nc</a> This file contains the TOA SW CRE (= toa_sw_clr_c_mon - toa_sw_all_mon) and total cloud fraction (cldarea_total_daynight_mon) variables from CERES-EBAF observations (Ed4.2, Loeb et al., 2018) from 2001 to 2021 and can be downloaded directly from the CERES ordering tool website here https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAFTOA42Selection.jsp.</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/seaice_conc_monthly_sh_200101-202112_2x2.nc/content" target="_blank" rel="noopener noreferrer">seaice_conc_monthly_sh_200101-202112_2x2.nc</a> This file contains the sea ice area variable (cdr_seaice_conc) for the Southern Hemisphere (SH) from NSDIC CDR observations (Meier et al., 2021) and can be downloaded directly here https://noaadata.apps.nsidc.org/NOAA/G02202_V4/.</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/sia_sh_obs_2001-2021.nc/content" target="_blank" rel="noopener noreferrer">sia_sh_obs_2001-2021.nc</a> This file contains the yearly mean of SH sea ice area (sia_sh) from the NSDIC CDR observations and from 2001 to 2021. </p> <p> </p> <p><strong>Simulations</strong></p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/cloud_analysis_cesm1_maps_annmean_50-90S_2001-2018.nc/content" target="_blank" rel="noopener noreferrer">cloud_analysis_cesm1_maps_annmean_50-90S_2001-2018.nc</a> This file contains the total cloud fraction (CLDTOT) and TOA SW CRE (SWCF) variables over 50-90˚S from 2001 to 2018 for CESM1 free-running and wind-nudged simulations (Blanchard-Wrigglesworth et al., 2021).</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/cloud_analysis_cesm1_conc_annmean_50-90S_2001-2018.nc/content" target="_blank" rel="noopener noreferrer">cloud_analysis_cesm1_conc_annmean_50-90S_2001-2018.nc</a> This file contains the sea ice area (aice) variable over 50-90˚S from 2001 to 2018 for CESM1 free-running and wind-nudged simulations (Blanchard-Wrigglesworth et al., 2021).</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/cloud_analysis_GISS-E2-1_maps_conc_annmean_50-90S_2001-2021.nc/content" target="_blank" rel="noopener noreferrer">cloud_analysis_GISS-E2-1_maps_conc_annmean_50-90S_2001-2021.nc</a> This file contains total cloud fraction (clt), TOA SW CRE (= rsutcs - rsut) and sea ice area (siconca) variables over 50-90˚S from 2001 to 2021 for NASA-GISS-E2-1 free-running and wind-nudged simulations (Roach et al., 2023).</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/sia_sh_GISS-E2-1_2001-2021.nc/content" target="_blank" rel="noopener noreferrer">sia_sh_GISS-E2-1_2001-2021.nc</a> and <a href="https://zenodo.org/api/records/13974536/draft/files/sia_sh_cesm1_2001-2018.nc/content" target="_blank" rel="noopener noreferrer">sia_sh_cesm1_2001-2018.nc</a> contain the yearly mean of SH sea ice area (sia_sh) from NASA-GISS-E2-1 and CESM1 simulations, respectively. </p> <p> </p> <p><strong>References</strong></p> <p>Blanchard-Wrigglesworth, E., Roach, L. A., Donohoe, A., & Ding, Q. (2021, February). Impact of Winds and Southern Ocean SSTs on Antarctic Sea Ice Trends<span> </span>and Variability. Journal of Climate, 34 (3), 949–965. doi: 10.1175/JCLI-D-20-0386.1</p> <p><span>Cesana, G., Roach, L. A. and Blanchard-Wrigglesworth, E., 2024 Clouds are crucial to capture Antarctic sea ice variability, submitted to <em>GRL.</em></span></p> <p>Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., <span> </span>Kato, S. (2018). Clouds and the earth’s radiant energy system (ceres) energy<span> </span>balanced and filled (ebaf) top-of-atmosphere (toa) edition-4.0 data product.<span> </span>Journal of Climate, 31 , 895-918. doi: 10.1175/JCLI-D-17-0208.1</p> <p>Meier, W., Fetterer, F., Windnagel, A., & Stewart, S. (2021). NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4.<span> </span>NSIDC. doi: 10.7265/EFMZ-2T65</p> <p>Roach, L. A., Mankoff, K. D., Romanou, A., Blanchard-Wrigglesworth, E., Haine,<span> </span>T. W., & Schmidt, G. A. (2023, 12). Winds and meltwater together lead to<span> </span>southern ocean surface cooling and sea ice expansion. Geophysical Research<span> </span>Letters, 50 . doi: 10.1029/2023GL105948</p> <p> </p>
Dataset for Reproduce "v2e: From Video Frames to Realistic DVS Events"
<p>This dataset release is meant for reproducing the results in our paper "v2e: From Video Frames to Realistic DVS Events".</p> <p>The paper is published in The Third International Workshop on Event-Based Vision.</p> <p>Please check out DATASET_README.md for more information. The code that accompanies the dataset is published <a href="https://github.com/SensorsINI/v2e_exps_public">here</a>.</p> <p>If you use this dataset, please cite:</p> <ul> <li>Y. Hu, S-C. Liu, and T. Delbruck. v2e: From Video Frames to Realistic DVS Events. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.</li> <li>Y. Hu, T. Delbruck, S-C. Liu, "Learning to Exploit Multiple Vision Modalities by Using Grafted Networks" in The 16th European Conference on Computer Vision (ECCV), Online, 2020.</li> </ul>
FIGURE 5 in Comparative Meristic Variability in Whiptail Lizards (Teiidae, Aspidoscelis): Samples of Parthenogenetic A. tesselata Versus Samples of Sexually Reproducing A. sexlineata, A. marmorata, and A. gularis septemvittata
FIGURE 5. Pattern of meristic variation between parthenogenetic Aspidoscelis tesselata E (○) and gonochoristic A. marmorata (□) depicted by the projection of principal component scores on PC1 and PC2 axes: A. Arroyo del Macho, Chaves County, New Mexico (N = 38 and N = 29, respectively); B. vicinity of Engle, Sierra County, New Mexico (N = 30 and N = 33, respectively). Percentages represent the proportion of meristic variation summarized by each principal component, and ellipses define the 95% confidence limits for score distributions.
FIGURE 3 in Comparative Meristic Variability in Whiptail Lizards (Teiidae, Aspidoscelis): Samples of Parthenogenetic A. tesselata Versus Samples of Sexually Reproducing A. sexlineata, A. marmorata, and A. gularis septemvittata
FIGURE 3. Pattern of meristic variation between parthenogenetic Aspidoscelis tesselata C (○) and gonochoristic A. sexlineata (◊): A. southeastern Colorado (N = 31 for each sample); B. Conchas Lake, New Mexico (N = 30 and N = 31, respectively). Percentages represent the proportion of meristic variation summarized by each principal component, and ellipses define the 95% confidence limits for score distributions.
FIGURE 2 in Comparative Meristic Variability in Whiptail Lizards (Teiidae, Aspidoscelis): Samples of Parthenogenetic A. tesselata Versus Samples of Sexually Reproducing A. sexlineata, A. marmorata, and A. gularis septemvittata
FIGURE 2. Representative specimens used in this study. Southeastern Colorado: A. Aspidoscelis tesselata C (RU 0198; 93 mm SVL); B. A. sexlineata (RU 0334; ♂, 71 mm SVL). Conchas Lake, New Mexico: C. A. tesselata C (RU 0003; 86 mm SVL); D. A. sexlineata (GM 236 [UADZ 7405]; ♀, 61 mm SVL).
FIGURE 4 in Comparative Meristic Variability in Whiptail Lizards (Teiidae, Aspidoscelis): Samples of Parthenogenetic A. tesselata Versus Samples of Sexually Reproducing A. sexlineata, A. marmorata, and A. gularis septemvittata
FIGURE 4. Representative specimens used in this study. Engle, New Mexico: A. Aspidoscelis tesselata E (RU 9546; 87 mm SVL); B. A. marmorata (RU 9262; ♀, 84 mm SVL). Arroyo del Macho, New Mexico: C. A. tesselata E (RU 0228; 84 mm SVL); D. A. marmorata (RU 0390; ♀, 79 mm SVL). Presidio County, Texas: E. A. gularis septemvittata (UADZ 8115; ♂, 90 mm SVL).
Dataset Literature Review Digital Forensic and Reproducibility of Result
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "<em>digital forensic" dan</em> "<em>reproducibility of result"</em></p>
Dataset Literature Review Digital Forensic AND Reproducibility of Results
<p>data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "Digital Forensic" dan "Reproducibility of Results"</p>
Data and code for reproducing analysis in 'Producing indicative allocations for Community Led Local Development funding in Scotland (2022-23)'
<p>The data and code in this folder can be used to reproduce work used to generate indicative allocations of Community Led Local Development funding (2022-23) to 21 Local Action Group (LAG) areas in Scotland. It accompanies a note ('Producing indicative allocations for Community Led Local Development funding in Scotland (2022-23)', <a href="https://doi.org/10.5281/zenodo.7418862">https://doi.org/10.5281/zenodo.7418862</a>) providing an overview of the analysis and its key outputs.</p>
ScienceDex guides
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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.