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9,882 results for “Scale”
Fig. 1 in Armored scales (Hemiptera: Diaspididae) and their parasitoids on Hass avocado (Persea americana Miller) in two municipalities of the State of Mexico, Mexico
Fig. 1. Mean armored scales in 4 Hass avocado orchards in Coatepec Harinas and Villa Guerrero, Estado de Mexico, Mexico, from May 2017 to Apr 2018.
Fig. 1 in Characterization of the entomopathogenic fungal species Conoideocrella luteorostrata on the scale insect pest Fiorinia externa infesting the Christmas tree Abies fraseri in the USA
Fig. 1. Features of Fiorinia externa: (A) 30× view of alive first instar (crawler); (B) 30× view of adult female body (inside cover) with exuviae of first and second instar; (C) naked eye view of entomopathogenic fungus Conoideocrella luteorostrata on different stages of F. externa (black arrow heads); (D) close-up of C. luteorostrata covering F. externa (black arrow heads).
Fig. 3 in Armored scales (Hemiptera: Diaspididae) and their parasitoids on Hass avocado (Persea americana Miller) in two municipalities of the State of Mexico, Mexico
Fig. 3. Regression between percentage of parasitism and mean armored scales on Hass avocado orchards in Coatepec Harinas and Villa Guerrero, Estado de Mexico, Mexico.
Dataset - Papyrus - A large scale curated dataset aimed at bioactivity predictions
<p><strong>Fixed version of additional_files:</strong></p> <p><strong>- In the previous version of 05.6_additional_files</strong> the data type of some descriptors was assigned incorrectly</p> <p><strong>- In this fixed version </strong>data types are correct </p> <p>This repository contains version 05.6 of the Papyrus dataset, an aggregated dataset of small molecule bioactivities, as described in the article "Papyrus - A large scale curated dataset aimed at bioactivity predictions" doi.org/10.1186/s13321-022-00672-x.</p> <p>Changes compared to version 05.5</p> <p>- applied small molecule filter that filters out compounds with a MW < 200 or > 800, heavy metal containing compounds and mixtures</p> <p>- include TID column which contains information on the original protein identifier</p>
Data for: Widespread forest-savanna coexistence but limited bistability at a landscape scale in Central Africa
<p>This directory contains the data used in the article **Zwaan et al. (2024) Widespread forest-savanna coexistence but limited bistability at a landscape scale in Central Africa** (DOI: <span><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1088%2F1748-9326%2Fad8cef&data=05%7C02%7Ca.zwaan%40uu.nl%7C66d868876ad44e69715608dcf9c1c197%7Cd72758a0a4464e0fa0aa4bf95a4a10e7%7C0%7C0%7C638659857776731023%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=3Bk8nBksr5ZBWJF4nfBvx3yp1tbyZiy%2FLlnfwDCZwfE%3D&reserved=0">https://doi.org/10.1088/1748-9326/ad8cef</a></span>).</p> <p>This repository provides the datasets used for the figures and statistics in our research article. We hope that this data can be useful for other researchers. Feel free to explore and analyze the data as needed. If you use this data in your own research, please cite our article.</p> <p><br>**Contact**: For any questions or further information, please contact:<br>* Aart Zwaan<br>* Email: a.zwaan@uu.nl</p>
The Collaborative Skills Scale
<p>The Collaborative Skills Scale is an 18-item scale with three subscales: Participation, Perspective taking and Social regulation. These subscales are devided under nine subscales overall, in the original version every subskill had four items, 36 items in sum. Participation consists of 12 items for Action, Interaction and Task completion, Perspective taking is composed by 8 items for Adaptive responsiveness and Audience awareness, Social regulation has 16 items for Negotiation, Self evaluation, Transactive memory and Responsibility initiative in the data file. The scale applies 7-point Likert-type questions where 1 means Does not describe me at all, 7 means Completely desribes me.</p>
User Experience Evaluation of BRI Smart Billing Mobile Application Using System Usability Scale and Heuristic Evaluation
<p>This research aims to evaluate user perceptions of the design and functionality of the BRI Smart Billing Mobile app. Through a questionnaire distributed to active BRI Smart Billing users, this study will identify factors that influence user satisfaction with the visual appearance, layout of elements, and ease of use of features available on the dashboard. The results of this research are expected to provide input to the application developers regarding efforts to improve the quality of digital application services and provide recommendations for improving the design of the BRI Smart Billing Mobile dashboard to be more user-friendly and meet user needs.</p>
Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies
<p><span>Here we generated the global sand dune pattern map at a resolution of 30 m, named GSDP30. The GSDP30 map encompasses 11 types of sand dune patterns (SDPs): simple crescentic dunes, compound-complex crescentic dunes, simple linear dunes, compound-complex linear dunes, dome dunes, star dunes, parabolic dunes, dendritic dunes, network dunes, sand sheets, and others. The map is divided into 331 Tiff tiles, each characterized by a size of 15,360 × 15,360 pixels and named according to the longitude and latitude coordinates of its upper-left corner.</span></p>
Datasets from "Auroral Energy Deposition and Conductance During the 2013 St. Patrick's Day Storm: Meso-Scale Contributions" by Gabrielse et al.
<p><strong>1. README File for netCDF Data to be published alongside “Mesoscale Contributions to Auroral Energy Deposition and Conductance During the 2013 St. Patrick’s Day Storm” by Christine Gabrielse et al. in the Journal of Geophysical Research.</strong></p> <p>The data to be stored on Zenodo (<a href="https://zenodo.org/">https://zenodo.org/</a>) are in a netCDF format.</p> <p>This README applies to the following files:</p> <p> thg_ej_all-sky-imager_20130317050000_v01.nc</p> <p> thg_ej_all-sky-imager_20130317060000_v01.nc</p> <p> thg_ej_all-sky-imager_20130317070000_v01.nc</p> <p> thg_ej_all-sky-imager_20130317080000_v01.nc</p> <p> thg_ej_all-sky-imager_20130317090000_v01.nc</p> <p> thg_ej_all-sky-imager_20130317100000_v01.nc</p> <p> thg_ej_all-sky-imager_20130317110000_v01.nc</p> <p> thg_ej_all-sky-imager_20130317120000_v01.nc</p> <p> thg_ej_all-sky-imager_20130317130000_v01.nc</p> <p> </p> <p>The following are attributes published within the netCDF’s metadata:</p> <p><strong>SUMMARY:</strong></p> <p>FILENAME:</p> <p> thg_ej_all-sky-imager_20130317050000_v01.nc</p> <p> </p> <p>PROJECT:</p> <p> STP>Solar-Terrestrial Physics</p> <p> </p> <p>SOURCE_NAME:</p> <p> THG>THEMIS (Time History of Events and Macroscale Interactions during</p> <p> Substorms) Ground-Based</p> <p> </p> <p>DISCIPLINE:</p> <p> Space Physics>Ionospheric Science</p> <p> Space Physics>Magnetospheric Science</p> <p> </p> <p>DATA_TYPE:</p> <p> EJ>Earth Camera Images, processed</p> <p> </p> <p>DESCRIPTOR:</p> <p> All-Sky-Imager</p> <p> </p> <p>FILE_NAMING_CONVENTION:</p> <p> source_datatype_descriptor_yyyyMMddHHmmss</p> <p> </p> <p>DATA_VERSION:</p> <p> 01</p> <p> </p> <p>PI_NAME:</p> <p> Christine Gabrielse</p> <p> </p> <p>PI_AFFILIATION:</p> <p> The Aerospace Corporation</p> <p> </p> <p>TEXT:</p> <p> Parameters (energy flux, mean energy, Hall conductance) derived from the THEMIS</p> <p> white light all-sky-imagers. See Gabrielse et al. (2021, Frontiers) and</p> <p> Gabrielse et al. (2024, JGR) for derivation methodology.</p> <p> </p> <p>INSTRUMENT_TYPE:</p> <p> Ground-Based Imagers</p> <p> </p> <p>MISSION_GROUP:</p> <p> Ground-Based Investigations</p> <p> </p> <p>LOGICAL_SOURCE:</p> <p> thg_ej_all-sky-imager</p> <p> </p> <p>LOGICAL_FILE_ID:</p> <p> thg_ej_all-sky-imager_00000000000000_v01</p> <p> </p> <p>LOGICAL_SOURCE_DESCRIPTION:</p> <p> Parameters (energy flux, mean energy, Hall conductance) derived from the THEMIS</p> <p> white light all-sky-imagers.</p> <p> </p> <p>TIME_RESOLUTION:</p> <p> 3s</p> <p> </p> <p>RULES_OF_USE:</p> <p> Invitation to co-authorship for use of this data is required. It is best to</p> <p> reach out to Dr. Gabrielse early in the project for guidance.</p> <p> </p> <p>GENERATED_BY:</p> <p> Christine Gabrielse</p> <p> </p> <p>ACKNOWLEDGEMENT:</p> <p> The work effort for this project was gratefully funded by NASA grants</p> <p> 80NSSC20K0725, 80GSFC22CA011, NAS5-02099, 80NSSC21K1552, AFOSR Grant</p> <p> FA9559-16-1-0364</p> <p> </p> <p><strong>GLOBAL ATTRIBUTES:</strong></p> <p>PROJECT:</p> <p> STP>Solar-Terrestrial Physics</p> <p>SOURCE_NAME:</p> <p> THG>THEMIS (Time History of Events and Macroscale Interactions during</p> <p> Substorms) Ground-Based</p> <p>DISCIPLINE:</p> <p> Space Physics>Ionospheric Science</p> <p> Space Physics>Magnetospheric Science</p> <p>DATA_TYPE:</p> <p> EJ>Earth Camera Images, processed</p> <p>DESCRIPTOR:</p> <p> All-Sky-Imager</p> <p>FILE_NAMING_CONVENTION:</p> <p> source_datatype_descriptor_yyyyMMddHHmmss</p> <p>DATA_VERSION:</p> <p> 01</p> <p>PI_NAME:</p> <p> Christine Gabrielse</p> <p>PI_AFFILIATION:</p> <p> The Aerospace Corporation</p> <p>TEXT:</p> <p> Parameters (energy flux, mean energy, Hall conductance) derived from the THEMIS</p> <p> white light all-sky-imagers. See Gabrielse et al. (2021, Frontiers) and</p> <p> Gabrielse et al. (2024, JGR) for derivation methodology.</p> <p>INSTRUMENT_TYPE:</p> <p> Ground-Based Imagers</p> <p>MISSION_GROUP:</p> <p> Ground-Based Investigations</p> <p>LOGICAL_SOURCE:</p> <p> thg_ej_all-sky-imager</p> <p>LOGICAL_FILE_ID:</p> <p> thg_ej_all-sky-imager_00000000000000_v01</p> <p>LOGICAL_SOURCE_DESCRIPTION:</p> <p> Parameters (energy flux, mean energy, Hall conductance) derived from the THEMIS</p> <p> white light all-sky-imagers.</p> <p>TIME_RESOLUTION:</p> <p> 3s</p> <p>RULES_OF_USE:</p> <p> Invitation to co-authorship for use of this data is required. It is best to</p> <p> reach out to Dr. Gabrielse early in the project for guidance.</p> <p>GENERATED_BY:</p> <p> Christine Gabrielse</p> <p>ACKNOWLEDGEMENT:</p> <p> The work effort for this project was gratefully funded by NASA grants</p> <p> 80NSSC20K0725, 80GSFC22CA011, NAS5-02099, 80NSSC21K1552, AFOSR Grant</p> <p> FA9559-16-1-0364</p> <p> </p> <p><strong>DIMENSIONS:</strong></p> <p>EPOCH:</p> <p> 1200</p> <p>LATITUDE:</p> <p> 400</p> <p>LONGITUDE:</p> <p> 400</p> <p> </p> <p><strong>VARIABLES:</strong></p> <p><strong>EPOCH:</strong></p> <p>CAT_DESC:</p> <p> Time in seconds since 1970-01-01 00:00:00</p> <p>TIME_SCALE:</p> <p> UTC</p> <p>TIME_BASE:</p> <p> 1970 (POSIX)</p> <p>FIELDNAM:</p> <p> Time in seconds since 1970-01-01 00:00:00</p> <p>FILLVAL:</p> <p> LONG = -2147483648</p> <p>FORMAT:</p> <p> I10</p> <p>UNITS:</p> <p> S</p> <p>VALIDMIN:</p> <p> LONG = 1363496400</p> <p>VALIDMAX:</p> <p> LONG = 1363528800</p> <p>VAR_TYPE:</p> <p> support_data</p> <p><strong>LATITUDE</strong>:</p> <p>_FILLVALUE:</p> <p> FLOAT = NaN</p> <p>CAT_DESC:</p> <p> Geodetic latitude</p> <p>FIELDNAM:</p> <p> Latitude</p> <p>FORMAT:</p> <p> F4.1</p> <p>FILLVAL:</p> <p> DOUBLE = -1.0000000e+31</p> <p>UNITS:</p> <p> deg</p> <p>VALIDMIN:</p> <p> DOUBLE = 45.000000</p> <p>VALIDMAX:</p> <p> DOUBLE = 84.900000</p> <p>VAR_TYPE:</p> <p> support_data</p> <p><strong>LONGITUDE</strong>:</p> <p>_FILLVALUE:</p> <p> FLOAT = NaN</p> <p>CAT_DESC:</p> <p> Geodetic longitude</p> <p>FIELDNAM:</p> <p> Longitude</p> <p>FORMAT:</p> <p> F5.1</p> <p>FILLVAL:</p> <p> DOUBLE = -1.0000000e+31</p> <p>UNITS:</p> <p> Deg</p> <p>VALIDMIN:</p> <p> DOUBLE = 180.00000</p> <p>VALIDMAX:</p> <p> DOUBLE = 339.60000</p> <p>VAR_TYPE:</p> <p> support_data</p> <p><strong>CONDUCTANCE:</strong></p> <p>_FILLVALUE:</p> <p> DOUBLE = NaN</p> <p>CAT_DESC:</p> <p> Hall conductance in a 2D grid organized by geographic latitude and longitude,</p> <p> units of mho</p> <p>DEPEND_0:</p> <p> Epoch</p> <p>DEPEND_1:</p> <p> Latitude</p> <p>DEPEND_2:</p> <p> Longitude</p> <p>DISPLAY_TYPE`:</p> <p> Image</p> <p>FIELDNAM:</p> <p> Hall conductance</p> <p>FILLVAL:</p> <p> DOUBLE = -1.0000000e+31</p> <p>FORMAT:</p> <p> F8.6</p> <p>LABLAXIS:</p> <p> Hall conductance</p> <p>UNITS:</p> <p> Mho</p> <p>VALIDMIN:</p> <p> DOUBLE = 0.00043800000</p> <p>VALIDMAX:</p> <p> DOUBLE = 88.000000</p> <p>VAR_TYPE:</p> <p> Data</p> <p><strong>Energy flux:</strong></p> <p>_FILLVALUE:</p> <p> FLOAT = NaN</p> <p>CAT_DESC:</p> <p> Precipitated energy flux in a 2D grid organized by geographic latitude and</p> <p> longitude, units of ergs/cm^2/s</p> <p>DEPEND_0:</p> <p> Epoch</p> <p>DEPEND_1:</p> <p> Latitude</p> <p>DEPEND_2:</p> <p> Longitude</p> <p>DISPLAY_TYPE`:</p> <p> Image</p> <p>FIELDNAM:</p> <p> Energy Flux (ergs/cm^2/s)</p> <p>FILLVAL:</p> <p> DOUBLE = -1.0000000e+31</p> <p>FORMAT:</p> <p> F9.4</p> <p>LABLAXIS:</p> <p> energy flux</p> <p>UNITS:</p> <p> ergs/cm^2/s</p> <p>VALIDMIN:</p> <p> DOUBLE = 0.010000000</p> <p>VALIDMAX:</p> <p> DOUBLE = 1100.0000</p> <p>VAR_TYPE:</p> <p> Data</p> <p><strong>ENERGY:</strong></p> <p>_FILLVALUE:</p> <p> FLOAT = NaN</p> <p>CAT_DESC:</p> <p> Mean energy of the precipitated population in a 2D grid organized by geographic</p> <p> latitude and longitude, units of keV</p> <p>DEPEND_0:</p> <p> Epoch</p> <p>DEPEND_1:</p> <p> Latitude</p> <p>DEPEND_2:</p> <p> Longitude</p> <p>DISPLAY_TYPE`:</p> <p> Image</p> <p>FIELDNAM:</p> <p> Energy (keV)</p> <p>FILLVAL:</p> <p> DOUBLE = -1.0000000e+31</p> <p>FORMAT:</p> <p> F6.3</p> <p>LABLAXIS:</p> <p> Energy</p> <p>UNITS:</p> <p> keV</p> <p>VALIDMIN:</p> <p> DOUBLE = 0.010000000</p> <p>VALIDMAX:</p> <p> DOUBLE = 22.000000</p> <p>VAR_TYPE:</p> <p> Data</p> <p> </p> <p>Examples of the data are as follows:</p> <p><strong>EPOCH:</strong></p> <p>data.<em>epoch</em>.<em>data</em>[<strong>0</strong>] = 1363496400</p> <p> </p> <p>This data is presented as number of seconds since January 1, 1970 in UT. The example above converts to 2013-03-17/05:00 UT. There are 1200 time values stored in each file.</p> <p> </p> <p><strong>LATITUDE:</strong></p> <p>data.<em>latitude</em>.<em>data</em>[0] = 45.0000</p> <p> </p> <p>This data is the geographic latitude in degrees of the 400x400 grid of data points. There are 400 latitude values stored in each file.</p> <p> </p> <p><strong>LONGITUDE:</strong></p> <p>data.<em>LONGITUDE</em>.<em>data</em>[<strong>0</strong>] = 180.000</p> <p> </p> <p>This data is the geographic longitude in degrees of the 400x400 grid of data points. There are 400 longitude values stored in each file.</p> <p> </p> <p><strong>CONDUCTANCE</strong>:</p> <p>data.<em>conductance</em>.<em>data</em>[<strong>0</strong>] = -1.0000000e+31</p> <p> </p> <p>This data is the Hall conductance in mho measured at 45 deg latitude, 180 deg longitude. The value in this example indicates that no conductance was measured here. A valid value would be something in the range of 0.00043800000 to 88 mho. There are 1200x400x400 conductance values stored in each file.</p> <p> </p> <p><strong>ENERGY FLUX:</strong></p> <p>data.<em>eflux</em>.<em>data</em>[<strong>0</strong>] = -1.00000e+31</p> <p> </p> <p>This data is the energy flux in ergs/cm^2/s measured at 45 deg latitude, 180 deg longitude. The value in this example indicates that no energy flux was measured here. A valid value would be something in the range of 0.01 to 1100.0000 ergs/cm^2/s. There are 1200x400x400 energy flux values stored in each file.</p> <p> </p> <p><strong>ENERGY:</strong></p> <p>data.<em>energy</em>.<em>data</em>[<strong>0</strong>] = -1.00000e+31</p> <p> </p> <p>This data is the energy in keV measured at 45 deg latitude, 180 deg longitude. The value in this example indicates that no energy was measured here. A valid value would be something in the range of 0.01 to 22 keV. There are 1200x400x400 energy values stored in each file.</p> <p> </p> <p><strong>2. README for text file Data to be published alongside “Mesoscale Contributions to Auroral Energy Deposition and Conductance During the 2013 St. Patrick’s Day Storm” by Christine Gabrielse et al. in the Journal of Geophysical Research.</strong></p> <p>This README applies to the following files:</p> <p> march172013_fortyukon_photometer_products.dat</p> <p> march172013_pokerflat_photometer_products.dat</p> <p>Header information in the files describe the contents. </p> <p>These are the photometer derived data taken on March 17, 2023. They include the time [UT], energy flux (Q) [ergs/cm^2/s], average energy (Eavg) [keV], and oxygen scale factor (fo).</p>
Supplementary Dataset: Air quality modeling intercomparison and multi-scale ensemble chain for Latin America
<p>The Supplementary dataset of the manuscript titled "Air quality modeling intercomparison and multi-scale ensemble chain for Latin America" can be downloaded via this link:<br>https://swiftbrowser.dkrz.de/public/dkrz_3ab03fbe-db0a-42e8-8b19-caf61d10634d/PAPILA/</p> <p>The data repository contains the model data used in the model intercomparison with six global and regional chemical-transport model over Latin America and the observation datasets used in the model intercomparison. This work presents the first model intercomparison and ensemble construction for Latin America, which was assembled under the Prediction of Air Pollutants in Latin America (PAPILA project (https://papila-h2020.eu/papila). </p>
Linked collectors and determiners for: Revision of world Austroterobiinae and Parasaphodinae (Hymenoptera: Chalcidoidea: Pteromalidae), parasitoids of giant scales (Hemiptera: Coccoidea: Monophlebidae).
Natural history specimen data linked to collectors and determiners held within, "Revision of world Austroterobiinae and Parasaphodinae (Hymenoptera: Chalcidoidea: Pteromalidae), parasitoids of giant scales (Hemiptera: Coccoidea: Monophlebidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5274b2f8-e4d0-4ac9-8e81-a23e886b2380">https://bionomia.net/dataset/5274b2f8-e4d0-4ac9-8e81-a23e886b2380</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5274b2f8-e4d0-4ac9-8e81-a23e886b2380">https://gbif.org/dataset/5274b2f8-e4d0-4ac9-8e81-a23e886b2380</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: New genera and species of felt scales (Hemiptera: Coccomorpha: Eriococcidae), with descriptions of new species and immature instars of described species.
Natural history specimen data linked to collectors and determiners held within, "New genera and species of felt scales (Hemiptera: Coccomorpha: Eriococcidae), with descriptions of new species and immature instars of described species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3c98b60d-917e-428e-af26-ae56accd2646">https://bionomia.net/dataset/3c98b60d-917e-428e-af26-ae56accd2646</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3c98b60d-917e-428e-af26-ae56accd2646">https://gbif.org/dataset/3c98b60d-917e-428e-af26-ae56accd2646</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Bumble bees collected in a large-scale field experiment in power line clearings, southeast Norway.
Natural history specimen data linked to collectors and determiners held within, "Bumble bees collected in a large-scale field experiment in power line clearings, southeast Norway". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/78822c79-7646-448d-aac2-35700498c147">https://bionomia.net/dataset/78822c79-7646-448d-aac2-35700498c147</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/78822c79-7646-448d-aac2-35700498c147">https://gbif.org/dataset/78822c79-7646-448d-aac2-35700498c147</a>. Formatted as a Frictionless Data package.
Japanese Small-scale fishers adaptive capacity and furthest response to climate change
<p>The database contains data collected from 25 communities along the western coast of Shikoku, Japan, between January and March 2023. It includes values of adaptive capacity indicators aligned with the adaptive capacity domains outlined by Cinner and Barnes (2019). Additionally, the dataset captures fishers' responses to climate change, following the adaptation pathways proposed by Fedele et al. (2019) and Ojea et al. (2020).<br><br>References: </p> <p>Fedele, G., Donatti, C. I., Harvey, C. A., Hannah, L. & Hole, D. G. Transformative adaptation to climate change for sustainable social-ecological systems. <em>Environmental Science & Policy</em> <strong>101</strong>, 116–125 (2019).</p> <p>Cinner, J. E. & Barnes, M. L. Social Dimensions of Resilience in Social-Ecological Systems. <em>One Earth</em> <strong>1</strong>, 51–56 (2019).</p> <p>Ojea, E., Lester, S. E. & Salgueiro-Otero, D. Adaptation of Fishing Communities to Climate-Driven Shifts in Target Species. One Earth 2, 544–556 (2020).</p>
Code and data of "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"
<p>Code and data to reproduce the analyses and figures presented in "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"</p>
Vector biology of the soft scales Parthenolecanium corni (Bouché) and Parthenolecanium persicae (Fabricius) (Hemiptera: Coccidae) with grapevine leafroll-associated viruses and grapevine virus A
<p>Raw tables of Elisa and RT-PCR results of LR1 and GVA transmission tests by the soft scales Parthenolecanium corni (Bouché) and Parthenolecanium persicae (Fabricius) (Hemiptera: Coccidae) to grapevine</p>
The first 10-m China's national-scale sandy beach map in 2022 derived from Sentinel-2 imagery
<p>This is the first 10-meter national scale beach map dataset of China. Based on the cloudless Sentinel-2 images for the whole year of 2022, we use the image classification method to draw a 10-meter beach map of China. The projection coordinate system of this data is WGS_1984_UTM_Zone_51N and the geographic coordinate system is GCS_WGS_1984.<br>The "Shape_Leng" field in the data set represents the circumference of the beach, the "Shape_Area" field represents the area of the beach, and the "Province" field represents the province of each independent beach.</p>
Machine learning based multi-scale remodelling code
<p>This instruction illustrates a machine learning-based multi-scale model to predict bone formation in tissue scaffolds. This code uses neural networks to predict bone formation in synthetic scaffolds. We are sorry that the code is a little bit messy as we are not good at coding. The code can be used to predict bone remodelling results in synthetic scaffolds in a multi-level way. Therefore, it enables to inversely identification of the bone remodelling related parameters from clinical data. In order to run the machine learning-based multiscale bone remodelling program. The following platforms are what you need:</p> <ol> <li>Abaqus v2016/v614</li> <li>Matlab R2020b</li> <li>An Abaqus plugin tool which can be downloaded from <a href="https://github.com/mhogg/pyvxray.git">https://github.com/mhogg/pyvxray.git</a></li> <li>Jupyter notebook with Python 3.</li> </ol> <p> </p> <p><strong>Here is a detailed description of the program</strong>.</p> <ol> <li><strong>demo_example and demo_pearson_opt. </strong>This document provides instructions on running a demo example and a demo example calculating Pearson’s coefficient. The necessary functions for running the machine learning-based algorithm are located in the folder “demo_example”. In "demo_example", Multiscale_boneRemodelling_ML is the main function to start the program. “macro_umat” is the user subroutine to pass the homogenized material properties to Abaqus. “read_macro_1423” is a post-process file to obtain necessary stress/strain data information. “Sheep_macro_1423_C3D4.inp” is the input file of the sheep tibia scaffold model. Before start, please change line 49 and line 58 of “macro_umat” file to your current work directory. In “results” file, the results are obtained by running the demo_program. In “demo_pearson_opt” file, there are inversely-identified virtual X-ray images and <em>in-vivo </em>X-ray images. The python code "sheep3_6_9_8roi.ipynb" can be found to calculate the Pearson’s coefficient between the virtual X-ray images and in-vivo X-ray images. Jupyter notebook is required to run “sheep3_6_9_8roi.ipynb” to calculate the Pearson’s coefficient in "demo_pearson_opt". “image_opt” is the calculated Pearson’s coefficient based on the inverse-identified case.</li> <li><strong>Micro_samples</strong> contains the files for the generation of micro RVE samples. “microRVE.inp” is the input file of the micro RVE for Abaqus. “Micro_USDF1.for” is the Fortran file that is used as a user subroutine in Abaqus to consider the adaptive bone density change in the bone regeneration area. “read_microRVE” is the post-process file to obtain strain and stress information.</li> <li><strong>Neural_network_training</strong> contains the files for the training of the 1<sup>st</sup> neural network for calculating the elastic tensor and the 2<sup>nd</sup> series of neural networks for calculating the unit SED components. In <strong>1<sup>st</sup>_neural_network</strong> file, a Matlab code is for the training of the neural network based on Matlab R2020b. The training data of the 1<sup>st</sup> round and the 2<sup>nd</sup> round are provided in the dataset file. In <strong>2<sup>nd</sup>_neural_network</strong> file, a Matlab code “Train3D_SED” is used for the training of 21 independent neural networks for predicting 21 unit SED components. “dataset” contains the training data of unit SED components.</li> <li><strong>ML_approach </strong>contains the files of the proposed machine learning-based approach and the trained neural networks. The Matlab code “Multiscale_boneRemodelling _ML” is the program for the proposed approach, which will call Abaqus for the finite element analysis at the macroscopic level. “sheep_macro_1423_C3D4.inp” is the input file of the in-silico model, including sheep tibia, bone fixation plate, screws and scaffold. “macro_umat” is the user subroutine file. “read_macro_1423” is used for post-process of FE data. “Trained_model” file includes all the trained neural networks.</li> <li><strong>Inverse_identification</strong> contains files for the inverse problem. “image_analysis” contains the 5000 virtual images generated by the proposed ML approach (“postresults_imageupdate_5000). “sheep3_6_9_8roi.ipynb” is the python-based code for the images analysis and calculates the Pearson’s coefficient. “NN_pear” is the trained neural network to output Pearson’s coefficient. “Pea_opt” is the code to find the optimal bone remodelling parameters by multi-objective genetic algorithm. “in-vivo X_ray” includes the clinical X-ray images taken at different time points. “inverse_identified_results” includes the final inverse identified results.</li> </ol>
Fig. 13 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 13. Map showing potential Akchagylian transgression mechanisms and possible connection points with brief outline of the requirements for each and the observed phenomena each would be able to explain. The main mechanisms discussed are climate driven base level rise and connection with an adjacent basin. Three potential adjacent water bodies are considered as the source of incoming water (Arctic Ocean, Indian Ocean and the Black Sea). For each of the three two potential gateways are suggested.
Fig. 10 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 10. Left: Correlation of late Pliocene - Pleistocene continental stages and reference faunas of the Pontocaspian domain with standard chronostratigraphy and mammal-based biochronological units. Right: Map of mammal reference localities. MN 16: Early Villafranchian/Early Villanyian 1) Diliska (N41.43, E43.46), 2) Kushkuna 1 (N41.28, E45.45), 3) Veselovka (N45.13, E36.92). MN 17: Middle Villafranchian/ Late Villanyian 4) Sablya (N44.53, E43.23¬), 5) Kryzhanovka 1,2 (N46.56, E30.8), 6) Kotsakhuri (N41.35, E46.23), 7) Kryzhanovka 3 (N46.56, E30.8). MN 17-MQ1: Late Villafranchian/Late Villanyian-Early Biharian 8) Muhkai 2 (N42.25, E47.36), 9) Psekups (N44.73, E39.23), 10) Dmanisi (N41.33, E44.34), 11) Tsalka (N41.6, E44.09). Late Villafranchian – Early Galerian / Early Biharian 12) Duzdag 1 (N40.68, E46.89), 13) Tizdar (N45.36, E37.1), 14) Nesmeyanovka (N47.29, E41.5), 15) Sarkel (N47.7, E42.2), 16) Iskra (N45.35, E36.83), 17) Akhalkalaki (N41.4, E43.48). Galerian / Late Biharian 18) Margaritovo 2 (N46.94, E38.87), 19) Haykadzor (N40.54, E43.65). Aurelian / Toringian 20) Chernyi Yar (N48.01, E46.11).
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.