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733 results for “Scheduling”
Simulation Results of the Distributed Schedule Optimization with Energy Storages using EO-COHDA
<p>This dataset contains the result of the evaluation of an approach to integrate energy storages in distributed flexibility negotiations. The data was created using the implemented approach on https://gitlab.com/digitalized-energy-systems/models/eo-cohda. To work with this results we highly recommend to use eo-cohda as well, because it provides a lot convenient utility functions for this.</p> <p>The dataset has been divided in two parts:</p> <ol> <li>the result of the negotiation, <ul> <li>format: hdf, readable using hdf-viewers/python</li> </ul> </li> <li>the generated schedules of the energy storages used for the negotiation. <ul> <li>format: binary, pickled real power schedules, readable using eo-cohda's utility methods.</li> </ul> </li> </ol> <p> </p>
Comparative observational study of gastric content in women scheduled for caesarean section or operative hysteroscopy: The ECHOCESAR study
<p class="MsoNoSpacing"><em><span>Objective:</span></em><span> There is a large literature concerning the estimation of gastric content in third-trimester pregnant women but their conclusions remain contradictory. Our objective was to compare gastric content in pr</span><span>egnant women and in non-pregnant ones using gastric ultrasound. </span></p> <p class="MsoNormal"><em><span>Design: </span></em><span>An observational two centres study was designed.</span></p> <p class="MsoNoSpacing"><em><span>Methods:</span></em><span> The antral cross-sectional area (CSA) in the semi-recumbent position (SRP), primary outcome, and in the right lateral position (RLD) were measured in pregnant women scheduled for a caesarean section (CS group) or non-pregnant women who underwent hysteroscopy (HS group) just before surgery. Perlas' score was also evaluated.</span></p> <p class="MsoNoSpacing"><em><span>Results:</span></em><span> One hundred and twenty-six patients were analysed, 61 patients in the CS group and 65 in the HS group. Antral CSA, measured in the SRP, was greater in the CS group than in the HS group (350 mm2 [236 - 415] vs 247 mm2 [180 - 318]; (P=0.001). This difference remained significant when the analysis was adjusted for fasting duration (P<0.001) or anxiety (P=0.001) or both (P<0.001). Among secondary outcomes, Perlas scores did not differ between groups (P=0.860). Concordance between antral CSA, categorised as corresponding to an empty or full stomach, and Perlas score was very poor.</span></p> <p class="MsoNoSpacing"><em><span>Conclusions: </span></em><span>Our results bring arguments to consider that individuals among pregnant women could have a delayed gastric emptying and support performing a gastric ultrasound in term pregnant patients prior to general anaesthesia.</span></p>
Data from: Drosophila medulla neuroblast termination via apoptosis, differentiation and gliogenic switch is scheduled by the depletion of the neuroepithelial stem cell pool
<p>The brain is consisted of diverse neurons arising from a limited number of neural stem cells. <em>Drosophila</em> neural stem cells called neuroblasts (NBs) produces specific neural lineages of various lineage sizes depending on their location in the brain. In the <em>Drosophila</em> visual processing centre - the optic lobes (OLs), medulla NBs derived from the neuroepithelium (NE) give rise to neurons and glia cells of the medulla cortex. The timing and the mechanisms responsible for the cessation of medulla NBs are so far not known. In this study, we show that the termination of medulla NBs during early pupal development is determined by the exhaustion of the NE stem cell pool. Hence, altering NE-NB transition during larval neurogenesis disrupts the timely termination of medulla NBs. Medulla NBs terminate neurogenesis via a combination of apoptosis, terminal symmetric division via Prospero, and a switch to gliogenesis via Glial Cell Missing (Gcm), however, these processes occur independently of each other. We also show that temporal progression of the medulla NBs is mostly not required for their termination. As the <em>Drosophila</em> OL shares a similar mode of division with mammalian neurogenesis, understanding when and how these progenitors cease proliferation during development can have important implications for mammalian brain size determination and regulation of its overall function.</p>
Job Shop Scheduling instances (SS + RD + EC)
<h1>Instance Structure</h1> <h2>📁 Definition:</h2> <p>For example, the filename `5_5_0_2_5_0.json` is defined as follows: 5 represents the number of jobs, 5 represents the number of machines, 0 indicates the type of distribution (0 = exponential, 1 = normal, 2 = uniform), 2 indicates the type of release and due date (0 = no restriction, 1 = by job, 2 = by operations), 5 denotes the quantity of speed scaling options for each machine, and 0 is the instance number.</p> <h2>📊 Job IDs: </h2> <p>An array of integers representing the job IDs (from 0 to 4).</p> <p><code>"nbJobs": [0, 1, 2, 3, 4]</code></p> <h2>🛠 Number of Machines: </h2> <p>An array of integers representing the number of machines (from 0 to 4). Each machine refeer an operation of a job that should be procedeed.</p> <p><code>"nbMchs": [0, 1, 2, 3, 4]</code></p> <p> </p> <h2>⏱️ Time and Energy:</h2> <p>An array of objects, each containing information about a job processed on a machine, including multiple speed-scaling options. Each job object includes the job ID, operations (with operation IDs as keys), and details such as processing time, energy consumption, release date, and due date.</p> <p><code>"timeEnergy": [</code><br><code> {</code><br><code> "jobId": 0,</code><br><code> "operations": {</code><br><code> "1": {</code><br><code> "speed-scaling": [</code><br><code> {"procTime": 209, "energyCons": 12},</code><br><code> ...</code><br><code> ],</code><br><code> "release-date": 0,</code><br><code> "due-date": 31</code><br><code> },</code><br><code> ...</code><br><code> }</code><br><code> },</code><br><code> ...</code><br><code>]</code></p> <h2>📅 Due Dates and Release Dates:</h2> <p>Within each operation in the `timeEnergy` array, the `release-date` represents the release date of the operation (in milliseconds) and the `due-date` represents the due date of the operation (in milliseconds).</p> <h2>🔄 Speed Scaling Options:</h2> <p> Each operation contains multiple speed-scaling options, providing different combinations of processing times and energy consumption levels.</p> <p><code>"speed-scaling": [</code><br><code> {"procTime": 209, "energyCons": 12},</code><br><code> {"procTime": 52, "energyCons": 59},</code><br><code> {"procTime": 40, "energyCons": 67},</code><br><code> {"procTime": 32, "energyCons": 72},</code><br><code> {"procTime": 30, "energyCons": 74}</code><br><code>]</code><br><br></p> <h2>🧩 Example:</h2> <p>Here is an example of how the data is structured for a specific job and its operations:</p> <p><br><code>{</code><br><code> "nbJobs": [0, 1, 2, 3, 4],</code><br><code> "nbMchs": [0, 1, 2, 3, 4],</code><br><code> "timeEnergy": [</code><br><code> {</code><br><code> "jobId": 0,</code><br><code> "operations": {</code><br><code> "1": {</code><br><code> "speed-scaling": [</code><br><code> {"procTime": 209, "energyCons": 12},</code><br><code> {"procTime": 52, "energyCons": 59},</code><br><code> {"procTime": 40, "energyCons": 67},</code><br><code> {"procTime": 32, "energyCons": 72},</code><br><code> {"procTime": 30, "energyCons": 74}</code><br><code> ],</code><br><code> "release-date": 0,</code><br><code> "due-date": 31</code><br><code> },</code><br><code> "3": {</code><br><code> "speed-scaling": [</code><br><code> {"procTime": 135, "energyCons": 25},</code><br><code> {"procTime": 40, "energyCons": 67},</code><br><code> {"procTime": 30, "energyCons": 74},</code><br><code> {"procTime": 28, "energyCons": 75},</code><br><code> {"procTime": 23, "energyCons": 79}</code><br><code> ],</code><br><code> "release-date": 32,</code><br><code> "due-date": 72</code><br><code> },</code><br><code> ...</code><br><code> }</code><br><code> },</code><br><code> ...</code><br><code> ]</code><br><code>}</code><br><br></p>
Raw data belonging to paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters"
<p>The Zip file contains the raw data used for drawing conclusions in the paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters" accepted for publication in Energy Informatics 2018.</p> <p>The raw data is the parameters of a sample of 50 domestic electric water heaters (DEWHs)<br> used for evaluating the algorithm in the paper. It is explained in the readme.txt.</p>
A matheuristic for the driver scheduling problem with staff cars
<p>This document contains the instances that were used as part of the paper 'A matheuristic for the driver scheduling problem with staff cars' that was submitted at the European Journal of Operational Research. </p> <p>Abstract: </p> <p>In the public bus transport industry, it is estimated that the cost of a driver schedule accounts for approximately 60% of a transport company's operational expenses. Hence, it is important for transport companies to minimize the overall cost of driver schedules. A duty is defined as the work of a driver for a day and the driver scheduling problem (DSP) is concerned with finding an optimal set of driver duties to cover a set of timetabled bus trips. Numerous labor regulations and other practical conditions enforce drivers to travel within the city network to designated bus stops to start/end duty, to take a break or to takeover a bus from another driver. The paper focuses on the driver scheduling problem with staff cars (DSPSC), where staff cars can be utilized by the drivers to fulfill their travel activities. However, staff cars should always be returned to the depot and can perform multiple round trips during the day. The problem is restricted by the number of cars available at the depot. We present a matheuristic for solving the DSPSC and the proposed method is tested on instances from Danish and Swedish companies. A comparison with a state-of-the-art mixed integer programming (MIP) solver indicates that the matheuristic provides better solutions, with comparable computation times, for 6 out of 10 large instances. For instances that have more than 6 staff cars and 1200 bus trips, the improvement is 13-15% on average. </p>
Positive and Negative Affect Schedule (PANAS): psychometric properties for the online version in a clinical sample with emotional disorders
<p>This dataset contains sociodemographic and clinical data about 595 patients with emotional disorders that participated in a study with the objective of examining the psychometric properties of the online version of the Positive and Negative Affect Schedule (PANAS) in Spanish clinical sample.</p> <p> </p> <p> </p>
Problem instances for integrated truck and workforce scheduling problem
<p>We randomly generated in total 500 problem instances for the integrated truck and workforce scheduling (ITWS) problem which is proposed in the article "Tadumadze, G., Boysen, N., Emde, S., and Weidinger, F. (2019): Integrated truck and workforce scheduling to accelerate the unloading of trucks. <em>European Journal of Operational Research</em>, 278(1), 343-362, <a href="https://doi.org/10.1016/j.ejor.2019.04.024">https://doi.org/10.1016/j.ejor.2019.04.024</a>".</p> <p>The problem instances are generated for two representative truck scheduling settings:</p> <ul> <li>distribution center (ITWS-DC)</li> <li>cross-docking terminal (ITWS-CD).</li> </ul> <p>The instance size is defined by the number of dock doors for inbound operations |D|, the number of inbound trucks |J| (and the number of outbound trucks |O| for the ITWS-CD setting) and they are dubbed as follows:</p> <ul> <li>XS: |D| = 5; |J| = 20; (|O| = 5)</li> <li>S: |D|= 15; |J| = 50; (|O| = 15)</li> <li>M: |D|= 25; |J| = 100; (|O| = 25)</li> <li>L: |D|= 50; |J| = 200; (|O| = 50)</li> </ul> <p>For each setting the instances are divided in sets "computational study" and "managerial insights".</p> <p>For each setting, the dataset "computational study" is divided into two parts (“part 1” and “part 2”) consisting of 80 and 60 instances which vary in problem size (XS, S, M and L) and the expected relative width of the trucks' time windows (Ω_{max}).</p> <p>The dataset "managerial insights" consists of problem instances that vary in acceleration effect of trucks' processing times by additional logistics workers (κ) and scenarios of the workforce size |W|. The notations for the workforce scenario have following meanings:</p> <ul> <li>plentiful workforce: |W|=2 ⋅ |D|</li> <li>scarce workforce |W|=|D|.</li> </ul> <p>For each setting (i.e., ITWS-DC and ITWS-CD) subset “part 1” of "managerial insights" contains 100 instances (50 with scarce workforce scenario and 50 with plentifull workforce scenario) and “part 2” - 10 instances.</p> <p>The instances are labeled according to the following naming convention: "[problem]_[setting]_[size]_[Ω_{max}]_[κ]_[ID]" where ID is a running index of instance of the same parameter constellation. As instances in “part 1” of the dataset “managerial insights” vary in workforce scenarios the instance names are extended by [scenario] from ahead so that following naming convention is applied: "[scenario]_[problem]_[setting]_[size]_[ Ω_{max}]_[κ]_[ID]".</p> <p>For detailed instance generation scheme see Section 4.1 of the above mentioned work.</p> <p>The file for the problem ITWS-DC are structured as follows:</p> <p>|J| = <number of inbound trucks>;</p> <p>T = <length of planning horizon>;</p> <p>K_j = <vector with |J| elements: the j-th element corresponds to number of unloading modes for truck j>;</p> <p>|W| = <total number of available workers for inbound operations>;</p> <p>|D| = <number of inbound doors>;</p> <p>rho_{jk} = <|J|*K_{max} matrix (with K_max=max_{j∈J}{K_j}): each entry in k-th column and j-th row corresponds to the processing time of truck j if it is unloaded by k logistics workers (the elements in k-th column and j-th row where k<K_j is set to the default value “0”)>;</p> <p>a_j = <vector with |J| elements: the j-th element corresponds to the arrival time of truck j>;</p> <p>d_j = <vector with |J| elements: the j-th element corresponds to the arrival time of truck j>;</p> <p>alpha_j = <vector with |J| elements: the j-th element corresponds to the duration of truck j’s initial docking (i.e., step (1))>;</p> <p>beta_j = <vector with |J| elements: the j-th element corresponds to the duration of truck j’s initial docking plus preparation (i.e., step (1) + step (2))>;</p> <p>gamma_j = <vector with |J| elements: the j-th element corresponds to the duration of truck j’s trailer secluding plus undocking (i.e., step (4) + step (5))>;</p> <p>delta_j = <vector with |J| elements: the j-th element corresponds to the duration of truck j’s final undocking (i.e., step (5))>;</p> <p> </p> <p>The file for the problem ITWS-CD are extended by following additional parameters:</p> <p>|O| = <number of inbound trucks>;</p> <p>FI_{jo} = <|J|*|O| matrix: each entry in o-th column and j-th row corresponds to the weight, of a shipment delivered by inbound truck j dedicated to outbound truck o>;</p> <p>dep_o = vector with |O| elements: the o-th element corresponds to the departure time for outbound truck o>;</p> <p>fi_{go} = <|D|*|O| matrix: each entry in o-th column and g-th row corresponds to the transfer time for moving goods from dock g to outbound truck o>;</p>
Sensor data from Almeria and Barcelona for the implementation and optimisation of INCOVER's irrigation system (FINoT controller and scheduler).
<p>The purpose of the data is to help local irrigation communities, city's landscape gardeners and others that perform irrigation activities in INCOVER’s Demo Sites 1 and 2 to define site-specific thresholds that deficit irrigation can be achieved and set limits under which the automated irrigation profile can operate by optimising water consumption. These sensor values are associated with the sensor technology exploited by FINT in INCOVER (FDR). Moreover, sensor streams can also help technology modellers in the area of IoT to get an example of syntactic formulation of data services that are based on IoT networked devices.</p>
Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods - instances
<p>Instances for periodic scheduling problem used in conference paper <a title="Paper Details, Citation and Download" href="https://www.scitepress.org/PublicationsDetail.aspx?ID=nP/EuJcR7dI=&t=1">Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods</a> <span></span> <a href="https://doi.org/10.5220/0012325800003639" target="_blank" rel="noopener">10.5220/0012325800003639</a></p> <p> </p>
Artifact of the paper: Scheduling with lightweight predictions in power-constrained HPC platforms
<p>Please refer to the <a href="https://zenodo.org/records/13961003/files/artifact-overview.pdf?download=1&preview=1">artifact-overview.pdf</a> file in this dataset for instructions to reproduce the experiments we have conducted for this article, or for more context about the article.</p>
Instance data: open-shop scheduling problems with any regular minsum objective
<p>Open shop scheduling instances used in the working paper</p> <ul> <li>Emde, S. & Lysgaard, J. (2021). Branch-cut-and-price for open-shop scheduling problems with any regular minsum objective.</li> </ul> <p>The instances in file <em>Brucker_instances.csv</em> are based on the test data for the classic [O||Cmax] problem from Brucker, P., Hurink, J., Jurisch, B., & Wöstmann, B. (1997). A branch & bound algorithm for the open-shop problem. <em>Discrete Applied Mathematics</em>, <em>76</em>(1-3), 43-59. They are enriched with release and due dates as well as machine-pair dependent transportation delays. The instances in file <em>random_instances.csv </em>are new.</p> <p>The files contain comma-separated values. Each line (except for the header) stands for one instance. The columns labels are:</p> <p>ID: label (identifier) of the instance</p> <p>n: number of jobs</p> <p>m: number of machines</p> <p>p: processing times; each square bracket stands for one machine, the values inside the brackets for the processing times of the jobs on the respective machine</p> <p>t: transfer times between machines; note that the dummy machine 0 where all jobs originate and end is the last machine, i.e., it has the highest index</p> <p>r: release dates for each job</p> <p>d: due dates for each job.</p> <p>Mj: set of machines on which the jobs must be processed; each square bracket stands for one job, values inside the brackets for machine indices. Note that indices are zero-based.</p> <p>In instance set <em>Brucker_instances.csv</em>, every machine is visited by every job, therefore there is no column Mj. Conversely, due dates are immaterial for the random instances because of their quadratic completion time objective. Hence, they are omitted from the table.</p>
Problem instances for outbound truck loading and scheduling problem
<p>The following dataset contains problem instances for the outbound truck scheduling and loading problem, which are proposed in the work "Giorgi Tadumadze & Simon Emde (2021): Loading and scheduling outbound trucks at a dispatch warehouse. <em>IISE Transactions</em>, DOI: <a href="https://doi.org/10.1080/24725854.2021.1983923">10.1080/24725854.2021.1983923</a>”.</p> <p>The problem instances are stored in table “instances”, where columns of tables can be interpreted as follows:</p> <ul> <li>ID: <running index>;</li> <li>name: <instance name, specifying the number of items <span class="math-tex">\(m\)</span>, number of trucks <span class="math-tex">\(n\)</span>, value of parameter <span class="math-tex">\(\alpha\)</span>, [value of parameter <span class="math-tex">\(\Delta\)</span>], and the width of trucks’ time windows>;</li> <li>O: <number of served OEMs>;</li> <li>m: <number of items>;</li> <li>n: <number of trucks>;</li> <li>Q: <total number of available workers>;</li> <li>D: <total number of available dock doors>;</li> <li>w_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the size (required space) of item <span class="math-tex">\(i\)</span>>;</li> <li>d_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the deadline of item <span class="math-tex">\(i\)</span>>;</li> <li>r_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the relative importance (penalty cost per time unit of earliness) of item <span class="math-tex">\(i\)</span>>; </li> <li>c_j: <vector with <span class="math-tex">\(n\)</span> elements: the <span class="math-tex">\(j\)</span>-th element corresponds to the capacity of truck <span class="math-tex">\(j\)</span>>;</li> <li>a_j: <vector with <span class="math-tex">\(n\)</span> elements: the <span class="math-tex">\(j\)</span>-th element corresponds to the earliest possible departure time of truck <span class="math-tex">\(j\)</span>>;</li> <li>b_j: <vector with <span class="math-tex">\(n\)</span> elements: the <span class="math-tex">\(j\)</span>-th element corresponds to the latest possible departure time of truck <span class="math-tex">\(j\)</span>>;</li> <li>q_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the number of required workers to prepare and load item <span class="math-tex">\(i\)</span>>;</li> <li>rho_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the handling time of item <span class="math-tex">\(i\)</span>>;</li> <li>B_i: < <span class="math-tex">\(m \times n\)</span> matrix: each entry in <span class="math-tex">\(j\)</span>-th column and <span class="math-tex">\(i\)</span>-th row corresponds to the binary parameter which has a value 1 if set of available trucks <span class="math-tex">\(B_i\)</span> contains truck <span class="math-tex">\(j\)</span> (i.e., if truck <span class="math-tex">\(j\)</span> departs towards the OEM, who ordered item); 0 otherwise>;</li> </ul> <p>The first 270 entries (ID between 1-270) contain OTSLP instances with different instance sizes, used for the computational performance experiments (Section 5.1).</p> <p>The following 100 entries (ID between 271-370) contain 40 OTSLP instances with the varying time window width for each truck (ID between 271-310), 30 OTSLP instances with the varying level of available workers (ID between 311-340), and 30 OTSLP instances with the varying level of available dock doors (ID between 341-370), used for the managerial inside experiments (Section 5.2).</p> <p>The detailed computational results for each instance and solution approach are reported in tables, which are named with the following convention: <results_<approach>”. Specifically, we report the required computational runtime in CPU seconds, status of the found solution (“Optimal”, “Infeasible”, “Feasible” / “AbortTimeLim”), as well as the best found upper (and lower) bound in columns “runtime”, “status”, “UB” and “LB”.</p>
Problem instances for scheduling personnel for the build-up of unit load devices at an air cargo terminal with limited space
<p>We randomly generated problem instances for the problem of scheduling the build-up of unit load devices at an air cargo terminal under space and personnel constraints (ULDSP in the following) which are used in the working paper "Emde, S., Abedinnia, H., Lange, A., and Glock, C. (2018): Scheduling personnel for the build-up of unit load devices at an air cargo terminal with limited space. Working paper TU Darmstadt".</p> <p>In total there are 60 problem instances which are divided in three datasets (small, medium, large) each of which contains 20 problem instances.</p>
AI-SPRINT GPU STochastic Scheduler
<p>This repository includes the source code and the datasets used to evaluate the GPU STochastic Scheduler developed in the context of the AI-SPRINT project. The corresponding results are included in the AI-SPRINT project deliverable "D3.3 - Second release and evaluation of the runtime environment".</p>
Weighted Link Schedules in 100-node Fixed Topology Wireless Networks
<p>This is a data repo for the data samples used for learning the link scheduling in a fixed-topology placed networks. This data set contains samples for 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.</p>
Weighted Link Schedules in sub-100 node Random Topology Wireless Networks
<p>This is a data repo for the data samples used for learning the link scheduling in a randomly placed networks. This data set contains samples for sub 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.</p>
Weighted Link Schedules in sub-100 node Fixed Topology Wireless Networks
<p>This data repo contains the link schedules in a multi-hop wireless networks that aims to maximize the weighted throughput. Each instance is represented by a numpy data file that contains the necessary data fields to reconstruct the original problem instance.</p> <p> </p> <p>Note: 100_7_new.tar.xz should be in the repo https://zenodo.org/deposit/7671940.</p>
Data - Positive and Negative Affect Schedule in early COVID-19 pandemic
<p>Data of a Positive and Negative Affect Schedule (PANAS) study done in early COVID-19 pandemic. The dataset has 4 sheets. The first one and the second one have 334 rows and 45 columns. The third and the fourth have 87 rows and 2 columns. Note that the first sheet includes a comment about the data in column 5, rows 336-341. Sheets 1 and 2 present the same information, but in different language: sheet one is in English and sheet 2 is in Spanish. Sheets 3 and 4 also have the same content, again: sheet 3 in English and sheet 4 in Spanish.</p> <p>SPANISH: Datos de estudio con escala Positive and Negative Affect Schedule (PANAS) realizado en los inicio de la pandemia COVID-19. El dataset incluye 4 hojas. La primera y la segunda contienen 334 filas y 45 columnas. La tercera y la cuarta tienen 87 filas y 2 columnas. Nótese que la primera hoja incluye un comentario sobre los datos en la columna 5, filas 336-341. Las hojas 1 y 2 contienen la misma información, pero en distinto idioma: la hoja 1 en inglés y la hoja 2 en español. Las hojas 3 y 4 también contienen la misma información, de nuevo: la hoja 3 en inglés y la hoja 4 en español. </p>
Prediction of Individual Autism Diagnostic Observation Schedule (ADOS) scores based on neural responses during live eye-to-eye contact
<p>Social difficulties are impactful in autism spectrum disorder (ASD), and links between these difficulties and underlying neural processes are active research questions. We present a multivariate classification method for neural data acquired from 36 participants during a live eye-to-eye contact task. Participants were either typically developed (TD) or diagnosed as ASD through gold-standard Autism Diagnostic Observation Schedule (ADOS) evaluation. We hypothesized multivariate classification could discriminate TD vs. ASD based on neural responses. Support vector machine (SVM) classification was able to discriminate between groups during the eye-contact interaction. In addition, it was found that underlying neural patterns contributing to binary classification also predicted measured ADOS scores with high correlation even though ADOS scores were not used for training. The correlation between observed and predicted ADOS scores was 0.72 (p < 0.002) for eye-to-eye contact. These findings suggest neural responses to live eye-to-eye contact are predictive of social symptomatology in ASD.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.