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9,882 results for “Scale”
Database Manuscript Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics - Gonzalez Dominguez et al
<p>The database contained the data used in the manuscript <strong>Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics, by Gonzalez Dominguez et al. </strong></p>
Dataset for paper "Sharing emotions at scale: The Vent dataset"
<p>This dataset is linked to the paper "<strong>Sharing emotions at scale: The Vent dataset</strong>" (<a href="https://arxiv.org/abs/1901.04856">arXiv:1901.04856</a> [cs.SI]) , and contains data collected from the Vent social network (<a href="https://www.vent.co/">https://www.vent.co/</a>).</p> <p>It is structured in files each containing a different entity (i.e. emotion categories, emotions, vent metadata and social links). Entities external to each file are cross-referenced via the anonymized universally unique identifiers (UUIDs).</p> <p>The dataset consists of the following files and data:</p> <p><strong>emotion_categories.csv</strong></p> <ul> <li>id (string): a UUID associated with a specific emotion category.</li> <li>name (string): The name of the emotion category.</li> </ul> <p><strong>emotions.csv</strong></p> <ul> <li>id (string): a UUID associated with a specific emotion.</li> <li>emotion_id (string): a UUID associated with a specific emotion (cross-reference to emotions.csv).</li> <li>name (string): The name of the emotion.</li> <li>enabled (boolean): Whether the emotion was enabled at the time of crawling.</li> </ul> <p><strong>vents_metadata.csv</strong></p> <ul> <li>emotion_id (string): a UUID associated with a specific emotion (cross-reference to emotions.csv).</li> <li>user_id (string): a UUID associated with a specific user.</li> <li>created_at (string): Date when the vent was posted, in UTC. Provided as a string in the format of “YYYY-MM-DD hh:mm:ss.sss''.</li> <li>reactions (integer): Total number of reactions to a vent.</li> </ul> <p><strong>vent.edgelist</strong></p> <p>The social graph of Vent, containing the directed friendship links between users.</p> <p><em>Researchers who want to access the text content for research and non-commercial use are welcome to contact us.</em></p>
Large-scale and fine-grained phenological stage annotation of herbarium specimens datasets
<p>This upload is constituted of four datasets of specimens from American herbaria covering different levels of information precision and different floras - from temperate to equatorial.</p> <p>Three of these datasets consist of selected specimens from herbaria located in different geographic and environmental regions. Each specimen of these three datasets was annotated with the following fields: family, genus, species name, fertile / non-fertile, presence / absence of flower(s), presence / absence of fruit(s). The resulting dataset was composed of 163,233 herbarium specimens belonging to 7,782 species, 1,906 genera, and 236 families. Specimens were annotated as “fertile” if any reproductive structures were present, such as sporangia (ferns), cones (gymnosperms), flowers, or fruits (angiosperms). Non-fertile specimens were those that lacked any reproductive structures.</p> <p>The fourth dataset consists of 20,371 herbarium specimens from 11 genera in the sunflower family (<em>Asteraceae</em>). The main difference in this dataset is that it is annotated with fine-grained phenophase scores rather than presence/absence attributes (see description below).</p> <p>Each of these datasets is described below:</p> <ul> <li> <p>NEVP: this dataset of New England vascular plant (NEVP) specimens was produced by members of the Consortium of Northeastern Herbaria. The dataset comprises 42,658 digitized specimens that belong to 1,375 species and come from several North American institutions. Most of the specimens in this dataset are from the north-temperate region of the northeastern United States.</p> </li> <li> <p>FSU: this dataset was produced by the Florida State University's Robert K. Godfrey Herbarium (FSU), a collection that focuses on northern Florida and the U.S. Southeast Coastal Plain, one of North America's biodiversity hotspots. This dataset contains 54,263 digitized herbarium specimen records that belong to 3,870 species, making it the taxonomically richest dataset in this study. Most species in this dataset grow under subtropical or warm temperate conditions in the southeastern region of the United States.</p> </li> <li> <p>CAY: this dataset comes from the IRD’s Herbarium of French Guiana (CAY). CAY is dedicated to the Guayana Shield flora, with a strong focus on tropical tree species. This dataset is composed of 66,312 herbarium specimens that belong to 3,024 species. All digitized specimens of this herbarium are accessible online. Most specimens were collected in the tropical rainforests of French Guiana, with the remaining specimens coming mostly from Suriname and Guyana.</p> </li> <li> <p>PHENO: this dataset includes 20,371 herbarium specimens of 139 species in the <em>Asteraceae</em> produced in a study of phenological trends in the U.S. Southeast Coastal Plain. The dataset is composed of specimen records from 57 herbaria. Each recorded specimen was annotated for quartile percentages (0, 25, 50, 75, or 100%) of (i) closed buds, (ii) buds transformed into flowers, and (iii) fruits. According to the distribution of these three categories for each specimen, a phenophase code was computed.</p> </li> </ul> <p> </p> <p><strong>Datasets format</strong></p> <p>These datasets are grouped in 3 tasks:</p> <ol> <li>fertility detection</li> <li>flowers and/or fruit detection</li> <li>phenophase classification</li> </ol> <p>The first 2 tasks are carried on the first 3 previous datasets and thus are based on the same set of images, unlike the third task which has its own disjoint set of images. This is why the dataset is presented into two separated files, one for each set of images.</p> <p><em>Fertility detection & flower/fruit detection</em></p> <p>These tasks are contained into the <em>herbarium_fertility_annotations.zip</em> archive. It consists of 3 files:</p> <ul> <li><em>metadata.csv</em>: general information about all the herbarium specimens for these tasks <ul> <li><em>id</em>: specimen identifier</li> <li><em>collection</em>: which of NEVP, FSU or CAY does the specimen come from</li> <li><em>herbarium</em>: institution of origin of the specimen, especially for NEVP collection</li> <li><em>clade</em>,<em> family</em>,<em> genus</em>,<em> species</em>: classification of the specimen</li> <li><em>URL</em>: URL of the scan</li> </ul> </li> <li><em>fertility_task.csv</em>: specific information regarding the fertility detection task <ul> <li><em>id</em>: specimen identifier</li> <li><em>is_fertile</em>: <em>True</em> if the specimen has an expression of fertility, <em>False</em> otherwise</li> <li><em>train_test_set</em>: which subset does the specimen belong to; possible values are: <em>train</em>, <em>random_test</em>, <em>species_test</em> and <em>herbarium_test</em></li> </ul> </li> <li><em>flower_fruit_task.csv</em>: specific information regarding the flower/fruit detection task <ul> <li><em>id</em>: specimen identifier, note that in this case not all the specimen described in <em>metadata.csv</em> are included in this task</li> <li><em>has_flower</em>: <em>True</em> if the specimen has at least one flower, <em>False</em> otherwise</li> <li><em>has_fruit</em>: <em>True</em> if the specimen has at least one fruit, <em>False</em> otherwise</li> <li><em>train_test_set</em>: which subset does the specimen belong to; possible values are: <em>train</em>, <em>random_test</em>, <em>species_test</em> and <em>herbarium_test</em></li> </ul> </li> </ul> <p><em>Phenophase classification</em></p> <p>These tasks are contained into the <em>herbarium_asteraceae_phenophase_annotations.zip</em> archive. It consists of a single file:</p> <ul> <li><em>annotations.csv</em>: <ul> <li><em>id</em>: specimen identifier</li> <li><em>URL</em>: URL of the scan</li> <li><em>genus</em>: genus of the specimen</li> <li><em>phenophase</em>: integer from 1 to 9 describing the phenophase of the specimen</li> <li><em>train_test_set</em>: which subset does the specimen belong to; possible values are: <em>train</em> and <em>test</em></li> </ul> </li> </ul> <p> </p> <p><strong>Additional ressources</strong></p> <p>More information can be found in the related paper:<br> <em>Lorieul, T., K. D. Pearson, E. R. Ellwood, H. Goëau, J.-F. Molino, P. W. Sweeney, J. M. Yost, J. Sachs, E. Mata-Montero, G. Nelson, P. S. Soltis, P. Bonnet, and A. Joly. 2019. Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences 7(3): e1233.</em></p> <p>For an example of usage of these datasets as well as a baseline, see: <a href="http://doi.org/10.5281/zenodo.2549996">http://doi.org/10.5281/zenodo.2549996</a></p> <p> </p>
Dataset of the article: "Technology validation of photosynthetic biogas upgrading in a semi-industrial scale algal-bacterial photobioreactor".
<p>Excel document that contains the data of the article: ‘Technology validation of photosynthetic biogas upgrading in a semi-industrial scale algal-bacterial photobioreactor’. This dataset shows the values obtained during the experimental period and it complements the corresponding article.</p>
FIG. 3. — Microphotina viridula n in Les mantes (Dictyoptera, Mantodea) du massif du Mitaraka (Guyane), in Touroult J. (ed.), "Our Planet Reviewed" 2015 large-scale biotic survey in Mitaraka, French Guiana.
FIG. 3. — Microphotina viridula n. sp., holotype mâle: A, vue dorsale; B, vue ventrale de l'avant-corps; C, étiquettes. Échelles: A, 1 cm; B, 5 mm. Photos M. Depraetere.
FIG. 4. — Microphotina viridula n in Les mantes (Dictyoptera, Mantodea) du massif du Mitaraka (Guyane), in Touroult J. (ed.), "Our Planet Reviewed" 2015 large-scale biotic survey in Mitaraka, French Guiana.
FIG. 4. — Microphotina viridula n. sp.: A, plaque sous-génitale d'un paratype; B, genitalia de l'holotype en vue ventrale avec en plus grand l'apex de l'épiphallus gauche (D) et sa variabilité chez un paratype (C). Échelles: A, B, 1 mm; C, D, 0,5 mm.
Dataset - Nutrient Removal from Agricultural Run-off in demonstrative full scale tubular photobioreactors for microalgae growth
<p>The data set attached consists of different files of the software SIGMAPLOT where the data from the article “Nutrient Removal from Agricultural Run-off in demonstrative full scale tubular photobioreactors for microalgae growth<strong>”</strong>, published in Ecological Engineering (vol. 120, 17<sup>th</sup> July 2018, 513-521.) can be found, as well as the different equations and formulae that were used to obtain the published results.</p>
Dataset - Use of full-scale hybrid horizontal tubular photobioreactors to process agricultural runoff
<p>The data set attached consists of three excel files where the data from the article “<strong>Use of full-scale hybrid horizontal tubular photobioreactors to process agricultural runoff”</strong>, published in Biosystems Engineering (vol. 166, 4<sup>th</sup> January 2018, 138-149.), can be found.</p>
Large scale experiments for an alternative erosion control measure using sand-filled geosystems. Data set produced at the CIEM flume, Hydralab+
<p>Sand-filled geosystems have the potential to mimic aspects of natural and nature-based features that can enhance the resilience of coastal areas challenged by climate, with additional (structural) reinforcement.</p> <p>Knowledge gaps can be identified. For instance, (i) the sediment transport mechanisms around the geosystem; (ii) the amount of erosion in the leeside when the system is overtopped; (iii) quantitative contribution the geosystem for the wave overtopping reduction; and (iv) failure mechanisms of the geosystem under extreme conditions. Specific tests are proposed in order to fill the defined knowledge gap and answer the following research questions:</p> <ol> <li>How do nearshore coastal processes (wave transformation, sediment transport) and wave structure interactions during extreme events differ from those during more usual big storm conditions for situations with and without the geosystem?</li> <li>How do feedbacks between the hydrodynamics and morphology of natural and nature-based features affect flooding, erosion, and recovery of coastal areas when erosion is limited by the 'geosystem'?</li> <li>How to conceive a dynamic coastal protection that can easily adapt to climate change in areas experiencing coastal squeeze (i.e. dense urban environment and human infrastructure with sea encroaching land) and vulnerable to coastal erosion and flooding risks?</li> </ol> <p>The set of experiments, done at the large wave Flume (CIEM) in Barcelona, are here described in order to answer the previous questions. These experiments started on October 2018 and ended at the end of November 2018. These tests include different configurations:<br>an initial benchmark tests in order to test the wave conditions were no geosystem protection is used, a second layout with a geotube used as a geosystem protection and finally a third layout were geobags are used as a protection.</p>
Large scale experiments for an alternative erosion control measure using sand-filled geosystems. Data set produced at the CIEM flume, Hydralab+
<p>Sand-filled geosystems have the potential to mimic aspects of natural and nature-based features that can enhance the resilience of coastal areas challenged by climate, with additional (structural) reinforcement.</p> <p>Knowledge gaps can be identified. For instance, (i) the sediment transport mechanisms around the geosystem; (ii) the amount of erosion in the leeside when the system is overtopped; (iii) quantitative contribution the geosystem for the wave overtopping reduction; and (iv) failure mechanisms of the geosystem under extreme conditions. Specific tests are proposed in order to fill the defined knowledge gap and answer the following research questions:</p> <ol> <li>How do nearshore coastal processes (wave transformation, sediment transport) and wave structure interactions during extreme events differ from those during more usual big storm conditions for situations with and without the geosystem?</li> <li>How do feedbacks between the hydrodynamics and morphology of natural and nature-based features affect flooding, erosion, and recovery of coastal areas when<br> erosion is limited by the 'geosystem'?</li> <li>How to conceive a dynamic coastal protection that can easily adapt to climate change in areas experiencing coastal squeeze (i.e. dense urban environment and human infrastructure with sea encroaching land) and vulnerable to coastal erosion and flooding risks?</li> </ol> <p>The set of experiments, done at the large wave Flume (CIEM) in Barcelona, are here described in order to answer the previous questions. These experiments started on October 2018 and ended at the end of November 2018. These tests include different configurations: an initial benchmark tests in order to test the wave conditions were no geosystem protection is used, a second layout with a geotube used as a geosystem protection and finally a third layout were geobags are used as a protection.</p>
Grazing-related nitrous oxide emissions: from patch scale to field scale - Dataset
<p>Dataset presented and referenced in the corresponding Biogeosciences publication by Voglmeier et al. (2019) [see Related identifiers]. The dataset results from a field experiment in Posieux, Switzerland. During that experiment N2O emissions of two pasture systems were measured over an entire grazing season in 2016. Field scale emissions were quantified using the eddy covariance method while small scale emissions from excreta and background patches were measured with the fast-box technique. The spreadsheet file contains half-hourly values of measured soil and meteo data. A second sheet contains information about the processed fast-box fluxes. Detailed information on the measurement methods and the data evaluation is presented in Voglmeier et al. (2019).</p>
Data from: Defensive fruit metabolites obstruct seed dispersal by altering bat behavior and physiology at multiple temporal scales.
<p>These data support the publication "Defensive fruit metabolites obstruct seed dispersal by altering bat behavior and physiology at multiple temporal scales".</p>
Supplementary data for "Molecular-scale thermally activated fractures in methane hydrates: A molecular dynamics study"
<p>In this dataset you can find</p> <p>- a data sample that can be used to confirm the plots in the paper. This can be found in the folder "data_sample". Each folder inside "data_sample" is one simulation. It contains the thermodynamic output from the simulation (log.lammps), and the input script (mw_hydrate_pennycrack.in) and input data (s1_unit_cell_mw.data and water_methane_hydrate.sw) that enables rerunning the simulation using LAMMPS.</p> <p>- A custom LAMMPS region, region_ellipsoid. This has to be compiled into LAMMPS in order to create the systems that we simulate.</p> <p>- A python script, plot_data.py, that shows how to extract the relevant data from the lammps log files, which enables the partial reproduction of figure 3 in the paper. See instructions below for requirements to use this script.</p> <p> </p> <p>"region_ellipsoid" and "data_sample" are in zip containers. In order to use them, please unzip them and leave the resulting folders in the same directory as this README file.</p> <p> </p> <p>Installation instructions to make "plot_data.py" work (assuming you already have numpy and matplotlib):</p> <p>> pip3 install git+https://github.com/henriasv/regex-file-collector.git</p> <p>> pip3 install git+https://github.com/henriasv/lammps-logfile.git</p> <p> </p> <p>If this does not work, please contact Henrik Andersen Sveinsson, henriasv@fys.uio.no</p>
Pelvic arcade and vertebral structure of the second chief specimen of Tyrannosaurus rex, Amer. Mus. 5027, discovered in 1908. An orthogonal projection executed on a very large scale and reproduced one-twelfth natural size. C 1-C 10 cervical series, D 1-D 13 dorsal or thoracic series, S 1-S 5 sacral series, Cd 1- Cd 53 caudal series. The caudals actually preserved are shaded; those drawn in outline are conjectural and restored. The total number of caudals is conjectural. in Skeletal Adaptations of Ornitholestes, Struthiomimus, Tyrannosaurus
Pelvic arcade and vertebral structure of the second chief specimen of Tyrannosaurus rex, Amer. Mus. 5027, discovered in 1908. An orthogonal projection executed on a very large scale and reproduced one-twelfth natural size. C 1-C 10 cervical series, D 1-D 13 dorsal or thoracic series, S 1-S 5 sacral series, Cd 1- Cd 53 caudal series. The caudals actually preserved are shaded; those drawn in outline are conjectural and restored. The total number of caudals is conjectural.
The behaviour of copper at the nano-scale in an Al-Zn-Mg-Cu alloy, AA7010
<p>Data plots and images accompanying figures to paper.</p>
Supplementary data to Schmiester et al. *Efficient parameterization of large-scale dynamic models based on relative measurements*
<p>This archive contains Supplementary data to the manuscript <em>Efficient parameterization of large-scale dynamic models based on relative measurements</em> by Leonard Schmiester, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer and Daniel Weindl.</p>
Data used in "Utilizing the Heliophysics/Geospace System Observatory to Understand Particle Injections: Their Scale Sizes and Propagation Directions"
<p>These are the raw data files for the data used in the paper, "Utilizing the Heliophysics/Geospace System Observatory to Understand Particle Injections: Their Scale Sizes and Propagation Directions" by Gabrielse et al. They can be read using the SPEDAS software found here: http://themis.ssl.berkeley.edu/software.shtml. Tplot variables, which are specifically read and plotted by SPEDAS, are included for pertinent THEMIS data.</p>
Text-fig. 10.—A possible hunting set of Hell Creek theropods, drawn to scale. A, Tyrannosaurus rex. B, Albertosaurus lancensis. C, the Jordan theropod. D, Saurornithoides mongoliensis. S. mongoliensis is not present in the Hell Creek, but is used to represent those saurornithoidids and dromaeosaurids present and represented by isolated teeth. A fifth form, Paronychodon lacustris, also represented only by isolated teeth has not been included but was probably intermediate between the saurornithoidids and the Jordan theropod. in A new Theropod Dinosaur from the Upper Cretaceous of Central Montana
Text-fig. 10.—A possible hunting set of Hell Creek theropods, drawn to scale. A, Tyrannosaurus rex. B, Albertosaurus lancensis. C, the Jordan theropod. D, Saurornithoides mongoliensis. S. mongoliensis is not present in the Hell Creek, but is used to represent those saurornithoidids and dromaeosaurids present and represented by isolated teeth. A fifth form, Paronychodon lacustris, also represented only by isolated teeth has not been included but was probably intermediate between the saurornithoidids and the Jordan theropod.
Text-fig. 7.—A, Ratio (H/L) of height of maxilla (at anterior margin of antorbital recess) to length of maxilla (to anterior margin of antorbital recess), plotted against length (L) of maxilla (to anterior margin of antorbital recess). Length scale in cm. B, Ratio (D/L) of depth of dentary ramus to length of maxilla (to anterior margin of antorbital recess), plotted against length of maxilla (to anterior margin of antorbital recess). Length scale in cm. in A new Theropod Dinosaur from the Upper Cretaceous of Central Montana
Text-fig. 7.—A, Ratio (H/L) of height of maxilla (at anterior margin of antorbital recess) to length of maxilla (to anterior margin of antorbital recess), plotted against length (L) of maxilla (to anterior margin of antorbital recess). Length scale in cm. B, Ratio (D/L) of depth of dentary ramus to length of maxilla (to anterior margin of antorbital recess), plotted against length of maxilla (to anterior margin of antorbital recess). Length scale in cm.
Dataset - Hydrolisis and codigestion data from lab and demo scale in INCOVER project at Chiclana de la Frontera (SP).
<p>This dataset contains data obtained from pilot at lab and demo scales, for control and optimization of the hydrolysis and codigestion processes of algae, with sewage sludge and molasses.</p> <p>The dataset can help future projects to increase TRL of the pilot plants from TRL 5-6 to closest market applications (TRL 7-8).</p>
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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.