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1,654 results for “Automation”
Digitalisation to improve automated agro-export logistics: Comprehensive bibliometric analysis
<p><strong>Introduction/objective</strong>: Digitalization in logistics transcended in the search for continuous improvement of good process optimization. This study aims to know the effectiveness of digitization implemented by companies to improve the automated logistics of cross-border trade in the agricultural sector.</p> <p><strong>Methodology</strong>: A bibliometric analysis was generated, exploring the evolution of the state of the art through Scopus, WOS and Dimensions databases, in order to select relevant empirical studies on digitization and automated logistics, using quality criteria and the application of the Prisma 2020 flowchart.</p> <p><strong>Results:</strong> Since 2017, there were signs of increased interest from researchers, highlighting authors such as Zoubek, Kumar and Ghobakhloo. This review provided insight into how digitization contributes to cost and time optimization in the logistics chain. Designing public policies allows a better integration of technology, such as IoT and AI. It identified 3 important blocks that have contributed to the effectiveness of digitization in automated logistics, they refer to “Impact of digitization on logistics efficiency and supply chain”, “Technology integration and automation in cross-border logistics” and “Governance, policy and social considerations in logistics digitization”.</p> <p><strong>Conclusions</strong>: Digitalization has been a fundamental element to improve logistics and make it autonomous within cross-border trade, allowing technology to get involved, integrating digital technologies such as artificial intelligence (AI), which reduced obstacles affecting the supply chain.</p>
SynActJ: Easy-to-use automated analysis of synaptic activity
<p>Neuronal synapses are highly dynamic communication hubs that mediate chemical neurotransmission via the exocytic fusion and subsequent endocytic recycling of neurotransmitter-containing synaptic vesicles (SVs). Functional imaging tools allow for the direct visualization of synaptic activity by detecting action potentials, pre- or postsynaptic calcium influx, SV exo- and endocytosis, and glutamate release. Fluorescent organic dyes or synapse-targeted genetic molecular reporters, such as calcium, voltage or neurotransmitter sensors and synapto-pHluorins reveal synaptic activity by undergoing rapid changes in their fluorescence intensity upon neuronal activity on timescales of milliseconds to seconds, which typically are recorded by fast and sensitive widefield live cell microscopy.</p> <p>The analysis of the resulting time-lapse movies in the past has been performed by either manually picking individual structures, custom scripts that have not been made widely available to the scientific community, or advanced software toolboxes that are complicated to use. For the precise, unbiased and reproducible measurement of synaptic activity, it is key that the research community has access to bio-image analysis tools that are easy-to-apply and allow the automated detection of fluorescent intensity changes in active synapses.</p> <p>Here we present SynActJ (<strong>Syn</strong>aptic <strong>Act</strong>ivity in Image<strong>J</strong>), an easy-to-use fully open-source workflow that enables automated image and data analysis of synaptic activity. The workflow consists of a Fiji plugin performing the automated image analysis of active synapses in time-lapse movies via an interactive seeded watershed segmentation that can be easily adjusted and applied to a dataset in batch mode. The extracted intensity traces of each synaptic bouton are automatically processed, analyzed, and plotted using a R Shiny workflow. We validate the workflow on time-lapse images of stimulated synapses expressing the presynaptic pH reporter Synaptophysin-pHluorin or a synapse-targeted calcium sensor, Synaptophysin-RGECO. We compare the automatic workflow to manual analysis and compute calcium-influx and SV exo-/ endocytosis kinetics and other parameters for synaptic vesicle recycling under different conditions. We predict SynActJ to become an important tool for the analysis of synaptic activity and synapse properties.</p>
Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)
<p>This dataset is related to the paper <strong>“Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)”.</strong></p> <p>https://www.sciencedirect.com/science/article/pii/S2001037021003792?via%3Dihub</p> <p>The dataset comprises horizontal and vertical frequencies of 2D NMR TOCSY of breast cancer-tissue sample. 2D TOCSY was acquired by employing a broadband high resolution 600.13 MHz (B0 = 14.1 T) NMR Bruker spectrometer (AVANCE III 600 with the Bruker magnet ASCEND 600) supported with the room temperature probe (BBO model-Bruker) and Magic Angle Spinning (MAS) probehead. 1D and 2D NMR spectra acquisition and processing were achieved by using the TopSpin software package 3.6.</p> <p>There are two files:</p> <p><strong>BreastCancerMetabolites.csv:</strong></p> <p>First column: numerical labels of the metabolites. Each number represent a metabolite. In total, there are 27 metabolites with multiple multiplets per metabolite.</p> <p>Second and third column: Horizontal and vertical frequencies for each metabolite.</p> <p><strong>Labels.csv:</strong></p> <p>The corresponding metabolites names.</p> <p> </p>
Proofs from interactive and automated theorem provers to evaluate Kontroli & Dedukti
<p>This dataset contains proofs in the Dedukti format<br> from the interactive theorem provers (ITPs) Matita, HOL Light, and Isabelle/HOL, as well as<br> from the automated theorem provers (ATPs) iProver Modulo and Zenon Modulo.<br> This data is used in the evaluation of the proof checkers Kontroli and Dedukti.</p>
How to Build an Image Processing Pipeline for Automating Multiparameter Histocytometry Analysis
<p>Image files for evaluation of an upcoming Current Protocols submission, as well as associated reference files.</p>
ABR raw data and results from automated hearing threshold detection
<p><strong>This repository contains: </strong></p> <ol> <li><strong>data.tar.gz</strong>: raw data from Auditory Brainstem Response (ABR) measurements performed at over 4,000 mice at the German Mouse Clinic. It also contains suitably pre-processed ABR raw data of over 8,000 mice from another public data repository (10.5061/DRYAD.CV803RV) of the Wellcome Sanger Institute. All data is intended to be used by and compatible with code published on GitHub (https://github.com/ExperimentalGenetics/ABR_thresholder). <br> </li> <li><strong>results.tar.gz</strong>: results and visualisations - comparison of two new, independent automatic hearing threshold finding methods and comparison with the manual gold standard method.</li> </ol>
Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using 1H NMR
<p>Dataset to accompany the manuscript "Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using <sup>1</sup>H NMR"</p>
A novel strategy for fully automated segmentation of supratentorial meningiomas: Use of pre-trained models and inclusion of normal brain images
<p>This repository is accompanying MRI datasets under the journal, titled: <strong>A novel strategy for fully automated segmentation of supratentorial meningiomas: Use of pre-trained models and inclusion of normal brain images</strong>. </p> <p>Nii_data.tar.gz (zipped) file includes MRI images of all patients described in the paper that are formatted as .nii.</p>
Data from: Evaluating the foraging performance of individual honey bees in different environments with automated field RFID systems
<p>Measuring the individual foraging performances of pollinators is crucial to guide environmental policies that aim at enhancing pollinator health and pollination services. Automated systems have been developed to track the activity of individual honey bees, but their deployment is extremely challenging. This has limited the assessment of individual foraging performances in full-strength bee colonies in the field. Most studies available to date have been constrained to use downsized bee colonies located in urban and suburban areas. Environmental policy-making, on the other hand, needs a more comprehensive assessment of honey bee performances in a broader range of environments, including in remote agricultural and wild areas. Here we detail a new autonomous field method to record high quality data on the flight ontogeny and foraging performance of honey bees, using Radio-Frequency Identification (RFID). We separate bee traffic into returning and exiting tunnels to improve data quality, solving many previous limitations of RFID systems caused by traffic jams and the parasitic coupling of RFID antennae. With this method, we assembled a large RFID dataset made of control bee colonies from experiments conducted in different locations and seasons. We hope our results will be a starting point to understand how ontogenetic and environmental factors affect the individual performances of honey bees, and that our method will enable the large-scale replication of individual pollinator performance studies.</p>
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
Python lab automation landscape catalog
<p>This version contains all the useful original data, presented in a simple web page. Some more polishing is still necessary before this is appropriate for wider dissemination or contributions, therefore the pre-1.0 version tag.</p>
Dataset for "Automated Identification of Uniqueness in JUnit Tests"
<p>Dataset for "Automated Identification of Uniqueness in JUnit Tests"</p> <p>Author: Jianwei Wu, James Clause</p> <p>Please contact at wjwcis@udel.edu for any questions.</p> <p> </p>
Readme for the Dataset of "Automated Identification of Uniqueness in JUnit Tests"
<p>This is the README for the dataset of journal publication "Automated Identification of Uniqueness in JUnit Tests".</p> <p>Please read through this before using the dataset.</p>
FlavoriaFoodWeight1700: Automated Lunch Line Meal Pictures with Automatic Measurement of Weight and Contents
<p>A dataset of around 2000 pictures consisting of both pictures taken on a lunch line and their measured contents based on the lunch line's automation systems.</p> <p>The pictures were automatically taken and linked to data via RFID tags on each meal tray.</p> <p>The pictures are linkable to CSV meal contents. CSV includes explanatory headers.</p> <p> </p>
Enhancing bioreactor arrays for automated measurements and reactive control with ReacSight
<p>This dataset supports the paper <em>"</em>Enhancing bioreactor arrays for automated measurements and reactive control with ReacSight" (2021). ReacSight is a generic and flexible strategy to enhance bioreactor arrays for automated measurements and reactive experiment control. We used ReacSight to assemble a platform for cytometry-based characterization and reactive optogenetic control of parallel yeast continuous cultures. Using a dedicated bioreactor array, we showcased its capabilities on several applications. This dataset contains all the corresponding raw data. Together with code available in the <a href="https://gitlab.inria.fr/InBio/Public/reacsight">ReacSight git repository</a>, it allows to reproduce the analysis of raw data and the generation of the figures appearing in the paper.</p>
Combining dynamic and static analysis for automated grading SQL statements
<p><strong>Introduction</strong></p> <p>Our experiment was conducted in an undergraduate Relational Database course at the Australian National University. The experiment was conducted on August 10th 2018 when students enrolled in the Relational Database course started to learn relational data model and SQL. The experiment was carried out fully online for three weeks and a total of 393 students were enrolled. The students were asked to login in an online assessment platform and complete 15 exercises. This platform provided an SQLite environment in students browsers by compiling the SQLite C code with Emscripten.</p> <p>Students were allowed to submit and execute their answers in the form of SQL statements. If the execution result of the statement submitted by the student is the same as that of the reference statement, the online assessment platforms will return a feedback message indicating that the execution result is correct. During the interaction with the assessment platform, statements submitted by students were recorded and archived. Overall, our experiment had collected 12,899 statements submitted by students. To create a benchmark dataset that can be used to evaluate different grading approaches, we randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we average the scores provided by the three assistants and take it as the final score of each statement. The dataset collected in this experiment is ready for public release.</p> <p>All experimental data are stored in Submission.sqlite, which is an SQLite database file. It is recommended to use software such as DB browser or SQLite expert to explore the database.</p> <p> </p> <p><strong>Datatable description</strong></p> <p> </p> <p><em><strong>exercises_result</strong></em></p> <p>This datatable stores the statements submitted by students. Based on the execution result of statement, statements were divided into three categories.</p> <ul> <li>noninterpretable: the statement is non-executable.</li> <li>partially correct: the execution result of statement is different from the expected result.</li> <li>correct: the execution result of the SQL statement is the same as the expected result.</li> </ul> <p>After analyzing the correct statements, we found that the correct set contains some statements carefully constructed by students to deceive the examination system.</p> <p>Take exercise 1 as an example, the task is to answer the following questions using SQL statements.</p> <p>Question: Assume persons who were born in the same year are the same age and there is only one youngest person (with no ties/draws) in this database, who is/are the second youngest person(s) in the database? List the id(s) of the person(s).</p> <p>The reference statement to this exercise is:</p> <pre><code class="language-sql">SELECT p.id FROM person p WHERE p.year_born = (SELECT MAX(year_born) FROM person WHERE year_born < (SELECT MAX(year_born) FROM person)); </code></pre> <p>By exploring the database or trying to execute different statements, some students found that the ID of the person who met the conditions was '00000842', so the following statement was submitted.</p> <pre><code class="language-sql">select id from person where id ='00000842'; </code></pre> <p>The execution result of the above code was correct, but it was obviously not what the tutor expected. Therefore, we identified such statements as 'cheating'.</p> <p>Table 1 Description of exercises_result table.</p> <table> <thead> <tr> <th> <p><strong>field</strong></p> </th> <th> <p><strong>desc</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>submission_id</p> </td> <td> <p>Submission ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>submitted_answer</p> </td> <td> <p>statement submitted by student</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>submission_time</p> </td> <td> <p>Submission time</p> </td> <td> <p>NUM</p> </td> </tr> <tr> <td> <p>exercise_id</p> </td> <td> <p>Exercise ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>is_correct</p> </td> <td> <p>Mark whether the statement is correct</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>student_id</p> </td> <td> <p>Student ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>category</p> </td> <td> <p>categories of statement</p> </td> <td> <p>TEXT</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>exercises_benchmark</strong></em></p> <p>This datatable stores the scores provided by different assistants. We randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we averaged the scores provided by the three assistants as the final score of each statement.</p> <p>Table 2 Description of exercises_benchmark table.</p> <table> <thead> <tr> <th> <p><strong>Field</strong></p> </th> <th> <p><strong>comment</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>Submission_id</p> </td> <td> <p>Submission ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>grade</p> </td> <td> <p>grade provided by tutor</p> </td> <td> <p>REAL</p> </td> </tr> <tr> <td> <p>tutor</p> </td> <td> <p>tutor</p> </td> <td> <p>TEXT</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>exercises_exercise</strong></em></p> <p>This datatable stores the exercises provided by tutor.</p> <p>Table 3 Description of exercises_exercise table.</p> <table> <thead> <tr> <th> <p><strong>Field</strong></p> </th> <th> <p><strong>comment</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>id</p> </td> <td> <p>Exercise ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>title</p> </td> <td> <p>Title of exercise</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>preamble</p> </td> <td> <p>Description of exercise</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>difficulty</p> </td> <td> <p>Coefficient of difficulty</p> </td> <td> <p>integer</p> </td> </tr> <tr> <td> <p>ref</p> </td> <td> <p>Reference statement</p> </td> <td> <p>integer</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>database schema</strong></em></p> <p>Please refer to db_schema.pdf for the database schema used in the experiment.</p> <p> </p> <p><strong>BibTex</strong></p> <p>if you want to cite our paper:</p> <p> </p> <blockquote> <pre>@article{wang2020combining, title={Combining dynamic and static analysis for automated grading SQL statements}, author={Wang, Jinshui and Zhao, Yunpeng and Tang, Zhengyi and Xing, Zhenchang}, journal={J Netw Intell}, volume={5}, number={4}, pages={179--190}, year={2020} }</pre> </blockquote>
Dataset and code for paper "An automated quantification tool for angiogenic sprouting from endothelial spheroids"
<p>This repository contains raw data and code for the manuscript with DOI: 10.3389/fphar.2022.883083</p>
Automated Reasoning in Temporal DL-Lite
<p><strong>Automated Reasoning in Temporal DL-Lite*</strong></p> <p>We investigate the feasibility of automated reasoning over temporal DL-Lite (TDL-Lite) knowledge bases (KBs). We translate TDL-Lite KBs into a fragment of First Order temporal logic and then into LTL, and apply off-the-shelf LTL and FO-based reasoners for checking the satisfiability. We conduct various experiments to analyse the size of the LTL translation as well as the runtime performance of different reasoners on toy scenarios and on randomly generated TDL-Lite KBs. To improve the reasoning performance when dealing with large ABoxes, our work also proposes an approach for abstracting temporal assertions in KBs. We run several experiments with this approach to assess the effectiveness of the technique by measuring the gain in terms of the size of the translation, and the number of both ABox assertions and individuals. We also measure the runtime of the solvers on such abstracted KBs. Lastly, in an effort to make the usage of TDL-Lite KBs a reality, we present a fully-fledged tool with a graphical interface to design and reason over them. Our interface is based on conceptual modeling principles and it is integrated with our translation tool and a temporal reasoner.</p> <p>(*) This work has been submitted to the Journal of Automated Reasoning</p>
Supplemental data for submission "Bridging between LegalRuleML and TPTP for Automated Normative Reasoning"
<p>These files are supplementary material to the submission<br> Bridging between LegalRuleML and TPTP for Automated Normative Reasoning<br> by<br> Alexander Steen and David Fuenmayor<br> submitted to the 6th International Joint Conference on Rules and Reasoning (RuleML+RR 2022), 2022.</p> <p>Files ex1.lrml.xml and ex2.lrml.xml are two example LegalRuleML files.<br> Files ex1.dsl.p and ex2.dsl.p are two examples from above translated to the NMF DSL.<br> The files ex1.output.X.p and ex2.output.X.p are the translations of the NMF files into the concrete logic X (X = SDL or X = cJ (Carmo Jones) or X = aqvist (system E)).</p> <p>Alexander Steen, <alexander.steen@uni-greifswald.de></p>
Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"
<p><strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong></p> <p><strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> </p> <p><em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis</p> <p> - "main-program-grid-search.py" file: Main program executing all experiments for the grid search analysis</p> <p><em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.</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.