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1,654 results for “Automation”
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-11 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-11 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-12 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-12 site automated weather station and associated soil substation at several temporal scales. Air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths. The nearest precipitation data is available from CSIS Block-13 site automated weather station located 290m distance.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-13 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-13 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-14 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-14 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-15 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-15 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Seals from the Staatsbibliothek zu Berlin and their automated detection
<p>This repository is a first step towards the compilation of an Islamic seals database comprising the following components:</p> <p>- digital photos of pages with seals, retraceable to the original artifact.</p> <p>- cut-outs of seals.</p> <p>- scripts for aggregating these resources.</p> <p>- scripts for automatically identifying similar/same seals.</p> <p>This repository took the collection at the StaBi as a first case. As such, this repository can be used as a data set for training purposes.</p> <p><strong>Scripts for seal detection</strong></p> <p>This is very basic still, and the examples show how it can be build out in different directions. The seal was actually taken from an entirely different collection. It seems the seal itself is not in this collection (or not in this orientation?) but clearly it can already function somewhat as an archetype to catch any stamps. Note that it only highlights the most likely candidate on a page, hence its singling out of only one seal.</p> <p><strong>Images from the Staatsbibliothek zu Berlin</strong></p> <p>These images were extracted from the Staatsbibliothek zu Berlin digital collections website. They can be retraced to their origin as follows: the PPN number is an identification for the object. The following number identifies the page.</p> <p>For a direct verification of the image, use the IIIF server by reconstructing the URL with this formula: `https://content.staatsbibliothek-berlin.de/dms/` then the PPN number, then `/full/0/` then the page number ending with `.jpg`</p> <p>The associated catalog page and manuscript viewer can be found by reconstruction the URL with this formula: `https://digital.staatsbibliothek-berlin.de/werkansicht?PPN=` followed by the PPN number.</p> <p>These images were assumed to be published by the StaBi and/or Stiftung Preußischer Kulturbesitz in the public domain per https://digital.staatsbibliothek-berlin.de/nutzungsbedingungen They have been brought together here strictly for research purposes with no further rights claimed.</p> <p><strong>Scripts for the StaBi</strong></p> <p>I used two scripts to automatically get 570 pages that supposedly contain Islamic seals. I also did some additional things to get everything working, for getting the right URLs and massaging the URLs into usable shapes.</p>
Dataset for Automated Medical Transcription
<p>We generated this dataset to train a machine learning model for automatically generating psychiatric case notes from doctor-patient conversations. Since, we didn't have access to real doctor-patient conversations, we used transcripts from two different sources to generate audio recordings of enacted conversations between a doctor and a patient. We employed eight students who worked in pairs to generate these recordings. Six of the transcripts that we used to produce this recordings were hand-written by Cheryl Bristow and rest of the transcripts were adapted from Alexander Street which were generated from real doctor-patient conversations. Our study requires recording the doctor and the patient(s) in seperate channels which is the primary reason behind generating our own audio recordings of the conversations. </p> <p>We used Google Cloud Speech-To-Text API to transcribe the enacted recordings. These newly generated transcripts are auto-generated entirely using AI powered automatic speech recognition whereas the source transcripts are either hand-written or fine-tuned by human transcribers (transcripts from Alexander Street). </p> <p>We provided the generated transcripts back to the students and asked them to write case notes. The students worked independently using a software that we developed earlier for this purpose. The students had past experience of writing case notes and we let the students write case notes as they practiced without any training or instructions from us.</p> <p><strong>NOTE:</strong> Audio recordings are not included in Zenodo due to large file size but they are available in the <a href="https://github.com/nazmulkazi/dataset_automated_medical_transcription">GitHub</a> repository.</p>
Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects
<p>The dataset contains analyzed projects and modules for the paper "Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects". The contents are the following:</p> <ul> <li><a href="/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/manual-studied-modules.csv?versionId=8258b59c-3f88-487b-a4a3-007cb362a44a">manual-studied-modules.csv</a>: Manually analyzed Maven modules mentioned in Section 5.2</li> <li><a href="https://zenodo.org/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/projects.zip">projects.zip</a>: Instrumented and Mutated Github Projects. Projects list applied mutation changes, and their dynamic and static call graph.</li> </ul>
Automated MESSENGER Plasma Region Classifications via Unsupervised Transfer Learning
<p>This file contains the 1-minute resolution dataset (“labeled_sunside_data_3labels.csv”) for Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, features that go into clustering and post-cleaning methods, spacecraft positions (in MSO), total magnetic field, raw and cleaned clustering labels, and raw and cleaned transition name.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning (DOI: 10.3389/fspas.2025.1608091).</p> <p>This work was supported by NASA grants 80NSSC19K0789 and 80NSSC22K0993.</p> <p> </p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data_3labels.csv description</strong></p> <table style="width: 100.063%; height: 851.2px;"> <tbody> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p><strong>Column Name</strong></p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p><strong>Description</strong></p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> Epoch</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Epoch in datetime (YYYY-MM-DD HH:MM:SS)</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> x_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>x position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> y_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>y position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> z_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>z position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> btot_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Total magnetic field [nT]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> norm_Btot</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Magnitude of the total magnetic field normalized to 150nT. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_max_width</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_high_low</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> high_intensity</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Boolean if there is a peak with a higher minimum intensity threshold. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> spectra_counts</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>A ratio of spectra bins with non-zero counts to all possible spectra bins (i.e. way to determine if too much missing spectra data). See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> raw_named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind)</p> </td> </tr> <tr> <td style="width: 17.3792%;"> <p>intermediate_named_label</p> </td> <td style="width: 78.9512%;"> <p>Cleaned cluster assigned plasma region label with only relabeling rules applied. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned cluster assigned plasma region label with relabeling rules and post-processing applied (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> raw_transition_name</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Raw transition names (e.g. bow shock, magnetopause) based on "raw_named_label" cluster labels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> transition_name</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned transition names (e.g. bow shock, magnetopause) after removing likely transient transitions based on "named_label" cluster labels. See paper for more information</p> </td> </tr> </tbody> </table> <p> </p>
Automated sarcomere detection Matlab tool
<p>The Matlab code presented was developed by Dr. Ian Estabrook in 2019-2023 to automatically detect sarcomeres in multi-channel z-stack images of developing myofibrils, as used in the preprint: "A tension-driven sarcomere division mechanism facilitates muscle growth" by Clement Rodier, Ian Estabrook, Vincent Loreau, Dirk Görlich, Benjamin M. Friedrich, Frank Schnorrer. We thank Yasmin Magdy Emadeldin Mohamed Abdelghaffar for help with preparing this repository and the documentation of the code. An example data set with corresponding sarcomere tracking is included.</p>
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process - Datasets
<p>This dataset contains supporting data for a research project aimed at analysing herbarium samples from the New England area at a large scale with deep learning techniques. Details on the methodology are shared in the acompanying paper (to be published).</p> <p>Content:</p> <ul> <li>dataset600k_withAI.csv : A dataset of over 600.000 herbarium samples with its record metadata and a corresponding AI phenological annotations with matching confidence scores. The entirety of the record headers are provided, extracted directly from the NEVP portal. In addition, the AI labels are defined by the following headers. These 8 columns represent 4 binary classifiers with the Presence/Absence of each 4 traits and corresponding confidence (as a percentage - presence/absence percentages sum to 1).<br> <ul> <li> <table> <tbody> <tr> <td>Flowering</td> <td>Not Flowering</td> <td>Budding</td> <td>Not Budding</td> <td>Fruiting</td> <td>Not Fruiting</td> <td>Reproductive</td> <td>Not Reproductive</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li>data_species_with_statuses.csv: A processed dataset summarizing flowering period shift at a species level. Two types of headers are provided. <ul> <li>First metadata concerning the flowering shift and the data used to compute that value: <ul> <li> <table> <tbody> <tr> <td>genus</td> <td>genus_species</td> <td>slope</td> <td>nb_specimens</td> <td>p_value_significance</td> <td>trend_category</td> </tr> <tr> <td>Genus of the species</td> <td>Binomial name of the species</td> <td>Regression slope defining the flowering shift as a slope</td> <td>Number of herbarium specimens used to compute the shift</td> <td>P-value significance of the slope being non-zero. ('Non Significant'/'Significant')</td> <td>Summary of the shift as a binary characteristic ('Earlier'/'Later')</td> </tr> </tbody> </table> </li> </ul> </li> <li>Second, metadata summarizing various traits associated to each species: <ul> <li> <table> <tbody> <tr> <td>lifeform_status</td> <td>native_introduced_status</td> <td>wetland_status</td> <td>seasonality_average</td> <td>seasonality_spread</td> </tr> <tr> <td>Growth form from the USDA PLANTS Database. 'Forb_Herb', 'Shrub_Tree' or 'Vine'</td> <td>'Native'/'Introduced' status from the USDA PLANTS Database.</td> <td> <p>National Wetland Plant List (NWPL) Wetland Indicator Status within the Northcentral and Northeast Region</p> <p>'OBL'/'FACW'/'FAC'/'FACU'/'UPL'</p> </td> <td>A characteristic of the flowering season of the species based on the mean Day of Year of the analysed specimens: if <=180: 'Early', else 'Late'</td> <td>A characteristic of the flowering season of the species based on the spread of the flowering season. Less than 28 days: 'Narrow', larger: 'Large'.</td> </tr> </tbody> </table> <p> </p> </li> </ul> </li> </ul> </li> <li>phylogenetic_tree.tre: The raw data used to generate the visualization of the flowering seasonality character and the detected flowering shift foreach species on a phylogenetic tree.</li> <li>phylogenetic_processed_dataset.csv: The processed dataset resuting from the phylogenetic signal analysis. For each trait, an associated significance binary value is provided.</li> </ul>
Data for Publication: "Automated Investigation of Metal-Ligand Interactions by a Newly Established Robotic Workflow for Titrations"
<p>This dataset contains the whole primary and raw (original) data for the manuscript "Automated investigation of metal-ligand interactions by a newly established robotic workflow for titrations".</p>
Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy
<p>Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021). </p> <p>The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a></p>
Data for "Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments"
<p>Flow Cytometry and plate reader data from "High Throughput Experimentation to replicate Yeast Gates Experiment," accompanied with jupyter noteboooks to replicate the analyses. Sequencing data is <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA784977">available separately</a>.</p> <p>Files named <code>flow_cytometrya<em>x</em></code> should be concatenated: they are individual slices of a gzipped tar file. Concatenate them and then extract with <code>tar xzf <em>filename</em></code>.</p> <p>The paper is available on <a href="https://biorxiv.org/cgi/content/short/2022.05.31.493627">Biorxiv</a>.</p> <p>The Jupyter notebooks used to analyze this data for the paper are <a href="https://github.com/rpgoldman/replication-paper-data-analysis">available on GitHub</a>.</p>
Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes
<p>Type 1 diabetes is a chronic autoimmune disease resulting in an immune-mediated loss of pancreatic β-cells; however, an unbiased and reproducible profiling of type 1 diabetes-specific circulating immunome at disease onset has yet to be explored. In this study, fresh whole blood was collected from a pediatric cohort of 107 patients with new-onset type 1 diabetes, 85 relatives of patients with type 1 diabetes with 0-1 islet autoantibodies, 58 patients with celiac disease or autoimmune thyroiditis and 76 healthy controls. Up to 6 mL of blood was collected from each subject into a VACUETTE® TUBE 6 ml ACD-B (Greiner). Fresh whole blood underwent red blood cell lysis, was washed and stained with specific monoclonal antibodies. Fresh whole blood samples were stained with five panels of antibodies labelled as T cells, T&NK cells, B cells, Tregs and DCs/monos encompassing main subsets of T cells, NK cells, B cells, Tregs, DCs and monocytes detected using 26 surface markers and the intracellular marker forkhead box P3 (FoxP3); for the Treg panel, intracellular staining was performed after fixation and permeabilization. Cells were acquired on a BD FACSCanto-II flow cytometer equipped with FACSDiva software (Becton Dickinson, Franklin Lakes, NJ). </p>
Semi-automated Quantitative Morphometric Analysis of E18 Rat Hippocampal Neurons from 0.5 to 6 Days In Vitro
<p>This is the dataset presented in "Semi-automated quantitatve evaluation of neuron developmental morphology <em>in vitro</em> using the change-point test" by AS Liao, W Cui, VS Webster-Wood, and YJ Zhang (submitted to Neuroinformatics 2022).</p>
nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems - Resources
<p>This dataset comprises of resources required to replicate the results described in "<em>nNPipe</em>: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". <em>nNPipe </em>is a deep learning based method in which two deep convolutional neural networks are used for the automated analysis of 2048x2048 HRTEM images.</p> <p>The file contains:<br> - Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.<br> - A workflow file explaining the nNPipe workflow.<br> - Mathematica 12.1 code for the generation of computational models.<br> - MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.<br> - Weights and files required for training the YOLOv5x module.<br> - Weights and files required for training the SegNet module.<br> - Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images. </p>
Data from: A semi-automated approach to classify and map ecological zones across the dune-beach interface
<p>This is the raw data behind the publication: </p> <p><strong>A semi-automated approach to classify and map ecological zones across the dune-beach interface</strong></p> <p><strong>Abstract: </strong>Habitat classification and mapping underpins most conservation and management tools, because habitats are often used as a surrogate for all biodiversity. Some habitat boundaries are easy to delineate; however, sandy shores are ecotones or ecoclines given their dynamic interface between the marine and the terrestrial realms. Although methods for mapping habitats along shorelines have been broadly applied, we aim to test a semi-automated approach to mapping across-shore “sub-environments” in this transition zone at a finer scale. Using an empirical dataset of photographs covering a small area (three across-shore transects from each of two different areas) with a high resolution, we tested seven machine learning algorithms to determine which one had the best classification accuracy, and to identify which environmental variables are the main determinants of classifications. The randomForest, stochastic gradient boosting, and C5.0 algorithms most accurately classified the photographs as the correct sub-environment. Based on the randomForest algorithm, the variables entropy, drift cover rate, local slope, segmented vegetation cover and number of points with sand or marine litter had the highest influence on the classification. There was no sensitivity to spatial variation alongshore. This approach can be used to map sub-environments at larger scales using drone technology to capture georeferenced digital photographs systematically. Consequently, coastal habitats can be mapped at a finer scale without causing disturbance to this especially sensitive ecotone.</p>
Quality Assessment in DevOps: Automated Analysis of a Tax Fraud Detection System
<p>The dataset includes the results of the performance analysis of Big Blu case study under different workloads, number of available resources and execution demand of activities</p>
Dataset from "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.
<p>Dataset used in publication "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.</p> <p><br>This repository contains images from annual pollen trap samples mounted on slides and scanned under light microscopy; image annotation metadata; and the weights of the trained models from the YOLOv5 algorithm, saved after the last training epoch.</p> <p>More details can be found in the README file.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.