Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
68
datasets available to search
ShareScore release 0.9.0
Dataset results
68 results for “ERP”
[Stimulus Set] Evoking the N400 event-related potential (ERP) component using a publicly available novel set of sentences with semantically incongruent or congruent eggplants (endings)
<p>During speech comprehension, the ongoing context of a sentence is used to predict sentence outcome by limiting subsequent word likelihood. Neurophysiologically, violations of context-dependent predictions result in amplitude modulations of the N400 event-related potential (ERP) component. While N400 is widely used to measure semantic processing and integration, no publicly-available auditory stimulus set is available to standardize approaches across the field. Here, we developed an auditory stimulus set of 442 sentences that utilized the semantic anomaly paradigm, provided cloze probability for all stimuli, and was developed for both children and adults. With 20 neurotypical adults, we validated that this set elicits robust N400's, as well as two additional semantically-related ERP components: the recognition potential (~250 ms) and the late positivity component (~600 ms). This stimulus set (<a href="https://doi.org/10.5061/dryad.9ghx3ffkg">https://doi.org/10.5061/dryad.9ghx3ffkg</a>) and the 20 high-density (128-channel) electrophysiological datasets (<a href="https://doi.org/10.5061/dryad.6wwpzgmx4">https://doi.org/10.5061/dryad.6wwpzgmx4</a>) are made publicly available to promote data sharing and reuse. Future studies that use this stimulus set to investigate sentential semantic comprehension in both control and clinical populations may benefit from the increased comparability and reproducibility within this field of research.</p>
Evoking the N400 Event-Related Potential (ERP) component using a publicly available novel set of sentences with semantically incongruent or congruent eggplants (endings)
<p class="MsoNoSpacing"><span>During speech comprehension, the ongoing context of a sentence is used to predict sentence outcome by limiting subsequent word likelihood. Neurophysiologically, violations of context-dependent predictions result in amplitude modulations of the N400 event-related potential (ERP) component. While N400 is widely used to measure semantic processing and integration, </span><span>no publicly-available auditory stimulus set is available to standardize approaches across the field. Here, we developed an auditory stimulus set of 442 sentences that utilized the semantic anomaly paradigm, provided cloze probability for all stimuli, and was developed for both children and adults. With 20 neurotypical adults, we validated that this set elicits robust N400's, as well as two additional semantically-related ERP components: the recognition potential (~250 ms) and the late positivity component (~600 ms). This stimulus set (<a href="https://doi.org/10.5061/dryad.9ghx3ffkg">https://doi.org/10.5061/dryad.9ghx3ffkg</a>) and the 20 high-density (128-channel) electrophysiological datasets (<a href="https://doi.org/10.5061/dryad.6wwpzgmx4">https://doi.org/10.5061/dryad.6wwpzgmx4</a>) </span><span>are made publicly available to promote data sharing and reuse. Future studies that use this stimulus set to investigate sentential semantic comprehension in both control and clinical populations may benefit from the increased comparability and reproducibility within this field of research.</span></p>
GitHub repositories for Enterprise Resource Planning (ERP) systems
<p>This dataset contains the main data and results of an analysis of open source ERPs (Enterprise Resource Planning) found in GitHub repositories.</p> <ul> <li><em>erp-repositories-ALL-short-filtered-v2.csv</em> is the main dataset file containing information collected for repositories using the GitHub Search API (11,500 relevant repositories after applying filtering criteria). It is also the main file that can be used for replication purposes of the reserch work.</li> <li>The remaining files contain results from the analysis on the dataset.</li> </ul> <p>The relevant paper from the 2024 International Conference on Software and Systems Reuse (ICSR) can be used for citing this work: The current status of open source ERP systems: a GitHub analysis, by Georgia M. Kapitsaki and Maria Papoutsoglou.</p>
Time series of electrical conductivity, temperature and relative stream stage recorded in surface water and streambed sediments of River Erpe and River Gruendlach, Germany
<p><span><a href="../api/records/13336325/draft/files/temp_EC_timeseries.csv/content" target="_blank" rel="noopener noreferrer">temp_EC_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC) and relative stream stage (cm) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Gruendlach, Germany.</p> <p> </p> <p><span><a href="../api/records/13336325/draft/files/porewater_ec_timeseries.csv/content" target="_blank" rel="noopener noreferrer">porewater_ec_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC), relative stream stage (cm) and total pressure (hPa) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Ammer, Germany, and the Sturt River, South Australia.</p>
ERP evidence of embodiment of action-verbs at lexical stages in L1 and L2
<p>EEG data for the article : Britz J., Collaud E., Jost L., Sato S., Bugnon A., Mouthon M. and Annoni JM. ERP evidence of embodiment of action-verbs at lexical stages in L1 and L2. Brain sciences 2024</p> <p>The data used in the study were organized using the Brain Imaging Data Structure (BIDS) (Gorgolewski, K., Auer, T., Calhoun, V. et al., 2016) with the extension for EEG data (Pernet, C.R., Appelhoff, S., Gorgolewski, K.J. et al., 2019).</p> <p> </p> <p>.....</p>
Evoking the N400 Event-Related Potential (ERP) component using a publicly available novel set of sentences with semantically incongruent or congruent eggplants (endings)
Open the record for dataset details and reuse information.
[Stimulus Set] Evoking the N400 event-related potential (ERP) component using a publicly available novel set of sentences with semantically incongruent or congruent eggplants (endings)
Open the record for dataset details and reuse information.
EEG data for "Conversation electrified: ERP correlates of speech act recognition in underspecified utterances"
<p>Please refer to the publication in Plos One for a description of the experiment and data analysis: Gisladottir RS, Chwilla DJ, Levinson SC (2015) Conversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances. PLoS ONE 10(3): e0120068. doi: 10.1371/journal.pone.0120068</p>
NREM sleep EEG and wake ERP summary: The first wave of the Global Research Initiative on the neurophysiology of schizophrenia (GRINS)
<p>Motivated by the potential of objective neurophysiological markers to index thalamocortical function in patients with severe psychiatric illnesses, we comprehensively characterized key NREM sleep parameters across multiple domains, their interdependencies, and their relationship to waking event-related potentials and symptom severity. In 72 schizophrenia (SCZ) patients and 58 controls, we confirmed a marked reduction in sleep spindle density in SCZ and extended these findings to show that fast and slow spindle properties were largely uncorrelated. We also describe a novel measure of slow oscillation and spindle interaction that was attenuated in SCZ. The main sleep findings were replicated in a demographically distinct sample, and a joint model, based on multiple NREM components, statistically predicted disease status in the replication cohort. Although also altered in patients, auditory event-related potentials elicited during wake were unrelated to NREM metrics. Consistent with a growing literature implicating thalamocortical dysfunction in SCZ, our characterization identifies independent NREM and wake EEG biomarkers that may index distinct aspects of SCZ pathophysiology and point to multiple neural mechanisms underlying disease heterogeneity. This study lays the groundwork for evaluating these neurophysiological markers, individually or in combination, to guide efforts at treatment and prevention as well as identifying individuals most likely to benefit from specific interventions.</p>
Raw EEG data for the experiment reported in "Understanding the effects of constraint and predictability in ERP"
<p>This repository contains the raw EEG data for the above-named paper. Preprocessing scripts are stored at: <a href="http://osf.io/fndk5/">https://osf.io/fndk5/</a></p> <p>The raw EEG data are the files *.eeg, *.vmrk and *.vhdr (BrainVision format EEG data). The numeric prefix indicates the participant ID. All three files must be stored in the same directory to work with the preprocessing script. Individual participant log files from the experimental presentation paradigm are stored in the zipped subdirectory opensesame_logs.zip. To work with the preprocessing script, these must be unzipped into a folder called opensesame_logs, stored in the folder containing the raw EEG files.</p> <p>A repository of intermediate preprocessing files based on the raw data is at <a href="http://zenodo.org/record/7002697">https://zenodo.org/record/7002697</a>.</p> <p>Note that the following raw files are included in the dataset for transparency but were not preprocessed for the final analysis: subject 15 due to a recording software crash mid-experiment, and subjects 40:43 as their EEG data were corrupted.</p>
[SUPERCEDED] Preprocessed EEG data for the experiment reported in "Understanding the effects of constraint and predictability in ERP"
<p>This repository contains <strong><em>an outdated version of</em></strong> intermediate preprocessing files for the above-named paper. Please see the current version here: <a href="https://zenodo.org/record/7334782">https://zenodo.org/record/7334782</a></p>
ERP Process
<p><a href="https://www.impactfirst.co/id/c/apa-itu-erp" target="_blank" rel="noopener">ERP (Enterprise Resource Planning) software</a> operates by integrating various business processes and functions into a unified system, streamlining operations, and providing real-time insights across an organization. This centralization is achieved through a modular design, where each module caters to a specific business function such as finance, human resources, supply chain management, inventory, sales, and customer relationship management (CRM). These modules are interconnected, allowing data to flow seamlessly between them, reducing data silos and ensuring that all departments have access to the most up-to-date information.</p> <p>At the core of ERP software is a centralized database that stores all the data from different modules. When a transaction or update occurs in one module, the centralized database ensures that the information is immediately reflected across all relevant modules. For example, when a sales order is processed, the inventory module updates stock levels, the finance module records the revenue, and the supply chain module adjusts procurement schedules as needed. This real-time data synchronization enhances efficiency, accuracy, and decision-making.</p> <p><a href="https://www.impactfirst.co/id/c/software-erp" target="_blank" rel="noopener">ERP systems</a> also include robust analytics and reporting tools that provide insights into business performance. These tools can generate detailed reports and dashboards that help management monitor key performance indicators (KPIs), identify trends, and make informed strategic decisions. By leveraging these insights, organizations can optimize their operations, improve productivity, and drive growth.</p> <p>Furthermore, ERP software supports automation of routine tasks and workflows, reducing manual intervention and minimizing errors. For instance, automated invoicing, payroll processing, and inventory replenishment can save time and resources, allowing employees to focus on more value-added activities. The system’s workflow management capabilities also ensure that business processes are consistent, standardized, and compliant with internal policies and external regulations.</p> <p>Scalability is another significant feature of ERP software. As businesses grow and their needs evolve, ERP systems can be scaled to accommodate new users, additional modules, and increased transaction volumes without disrupting existing operations. This scalability ensures that the ERP system can support the organization’s growth and changing requirements over time.</p> <p>In summary, <a href="https://www.impactfirst.co/id/erp/software-erp" target="_blank" rel="noopener">ERP software</a> works by integrating and automating various business processes through a centralized database, providing real-time data synchronization, comprehensive analytics, and scalable solutions. This integration enhances operational efficiency, improves data accuracy, and supports informed decision-making, ultimately driving business success.</p>
Online Tone Manipulation in Violin Performance: An ERP and ERSP study
<p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Data and Software used in <br> Online Tone Manipulation in Violin Performance: An ERP and ERSP Study.<br> <em>Ángel David Blanco, Jordi Costa-Faidella, Alfonso Pérez, David Dalmazzo, Rafael Ramirez, Iria SanMiguel</em><br> (not published at this moment)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>FILES:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1. Online_Tone_Manipulation_Violin_DATA.rar</strong></p> <p>In this compressed file we found 3 folders:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1.1 Raw_data</strong>: raw data of the participants of the experiment.</p> <p>Inside we find 16 folders. Each one contains the raw data of each participant: SXX (where XX is the code assigned to each subject).<br> Data from participants S01 and S11 are missing due to technical problems.</p> <p>Each folder contains:</p> <p>audio: This folder contains the audio recorded during each block of the session.<br> sXX: This folder contains the EEG files recorded during each block of the session.<br> tony: This folder contains the tony and excel files with the information about the audio onsets and the onsets of corrective movements.</p> <p>We also find 2 matlab scripts:</p> <p>main_final.m: This script creates one *.set file per block with the EEG data and the audio markers for each event. <br> main_EEG.m This script creates the Merged_Datasets.set file with the data from all the blocks. It also cleans the data from noise artifacts that were previously visual inspected.<br> It also computes the average reference, filters the Data, computes ICA and removes those components related with ocular activity. <br> It also creates te SXX_MergedDatasets_filt25_ICprun.set and the SXX_MergedDatasets_filt50_ICprun_TF.set</p> <p>Those files can already be found inside each folder. </p> <p>SXX_MergedDatasets_filt25_ICprun.set: This file contains the data for the ERPs already processed (pass band filter 1-25Hz). <br> SXX_MergedDatasets_filt25_ICprun_TF.set: This file contains the data for the ERSPs already processed (pass band filter 1-50Hz).</p> <p>RECODED TRIGGERS <br> (Based on audio onsets and logfiles)<br> Hundreds: TASK<br> Tens: FEEDBACK<br> Units: ORDER<br> 0: Reference<br> 100: Active<br> 200: Replayed<br> 300: Manipulated Active<br> 400: Post-error manipulation Active<br> 500: Non-manipulated active<br> 600: Manipulated Replayed<br> 700: Post-error manipulated Replayed<br> 800: Non-manipulated Replayed<br> 900: Onset End Correction Active<br> 1000: Onset End Correction Passive<br> 10: Open-String Note<br> 20:In Tune ONSET<br> 30: Mistuned ONSET <br> 40: In Tune STABLE<br> 50: Mistuned STABLE<br> 60: Notes with correction ONSET (All)<br> 70: Mistuned notes with correction ONSET<br> 80: Mistuned notes without correction ONSET<br> 1: Low (15-30c)<br> 2: LowHigh(30-50c)<br> 3: Middle (50-70c)<br> 4: MiddleHigh(70-100)<br> 5: High (>100)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>In Raw_data we can also found two scripts</p> <p>load_participants.m: This script executes the main_final.m script for each participant.<br> load_participants_EEG.m: This script executes the main_EEG.m script for each participant</p> <p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1.2 ERPs:</strong></p> <p>Inside this folder we find 3 more folders:</p> <p>Active: Contains the *.set files with the ERPs for each event of interest inside the Active condition.<br> Replayed (Passive): Contains the *.set files with the ERPs for each event of interest inside the Replayed condition.<br> Reference Melody: Contains the *.set files with the ERPs of the reference melody.</p> <p>Events of interest in the names of each Folder:<br> XXXX_Tuned: tuned notes<br> XXXX_Mistuned: notes with an error higher than 30 cents.<br> XXXX_nonman: nonmanipulated<br> XXXX_man: manipulated<br> XXXX_postman: postmanipulated<br> XXXX_Corr_Low: Trials with slow corrective movements (>350 ms) <br> XXXX_Corr_Medium: Trials with medium corrective movements (250-350ms)<br> XXXX_Corr_High: Trials with fast corrective movements (<250ms)<br> XXXX_Low: low error (15-30 cents)<br> XXXX_Medium: Medium error (30-50 cents)<br> XXXX_Medium_High: Medium High error (50-70 cents)<br> XXXX_High_High: High errors (>70 cents)</p> <p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> <br> <strong>1.3 ERSPs:</strong></p> <p>ERSPs of Active Tuned and Mistuned and Replayed Tuned and Mistuned in MATLAB Data files.</p> <p>Inside each file we can find the ERSPs and the ITC for different electrodes:</p> <p>ersp_XX: where XX is the name of the electrode (C3,C4,CP3,CP4)..<br> itc_XX: where XX is the name of the electrode (C3,C4,CP3,CP4)..</p> <p>both the ersp_XX and the itc_XX are three-dimensional matrices:</p> <p>frequencies (30 points) X time (200 points) X participants (15 subjects).</p> <p>The frequencies and times variables contain an array with the information of the frequency value (Hz) and time value (ms) for each point.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2. Online_Tone_Manipulation_Violin_STIMULI_and_MAX_software.rar</strong></p> <p>In this compressed file we found two folders:</p> <p><br> <strong>2.1 Online_Tone_Manipulation_System_in_Max folder</strong></p> <p>This folder contains the system in Max that allows us to manipulate the pitch of the played note in the melody.</p> <p>recording_session.maxpat: open this file to access the system.<br> random_file.csv: file which contains the order of the melodies reproduced to the participants, the note which has to receive the manipulation, and the direction of the manipulation (1 up, 0 down).</p> <p>We can also find two folders:</p> <p>audio: the audio of the participant for each block is recorded and saved inside this folder<br> New_generated_melodies: This folder needs to contain the melodies reproduced to the participant during the experiment</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2 Stimuli folder</strong></p> <p>This folder contains three folders:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.1 Generated_Scores folder</strong></p> <p>This folder contains the code and which generates the score images used during the experiment.</p> <p>Inside the folder we can find:</p> <p>generate_scores.m: Script used to generate the score images</p> <p>New_generated_scores folder: This folder contains the XML code and the *.jpg file for each score.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.2 Screen</strong></p> <p>This folder contains the code used to deliver the visual information to the participant during the session and also the clicks sent to the DSP computer and the markers to the EEG computer via parallel port.<br> The random_file.csv inside this folder has to be the same that the one contained inside the Online_Tone_Manipulation_System_in_Max.</p> <p>Inside this folder we can find:</p> <p>Violin_screen_Brainlab.m: script with the code which has to be executed to start delivering the visual instructions to the participants</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.3 Violin_Sample_sounds</strong></p> <p>Inside this folder we can find two folders:</p> <p>New_generated_melodies: contains the final generated melodies of the experiment<br> Original_Sounds: contains the original sounds used to generate the rest of the melodies of the experiment</p> <p>We can also find two important scripts:</p> <p>Generate_audios: this script generates the different melodies of the experiment from the original sounds.<br> Randomize_audios_new: this script generates the random_file.csv with the random order of the melodies together</p> <p><br> </p>
Preprocessed EEG data for the experiment reported in "Understanding the effects of constraint and predictability in ERP"
<p>This repository contains intermediate preprocessing files for the above-named paper. Preprocessing scripts are stored at: <a href="http://osf.io/fndk5/">https://osf.io/fndk5/</a></p> <p>For the raw EEG data, please see <a href="http://zenodo.org/record/6992085">https://zenodo.org/record/6992085</a>.</p> <p>The intermediate preprocessing files are *_ica.rds (R datasets saved after running ICA), and *_prepro.rds (R datasets saved after individual preprocessing complete, before combination with other participants). To use the data with the preprocessing scripts, download and save it in a folder called prepro_EEG_data as explained in the preprocessing script.</p>
ERP_Dataset
<p>This dataset contains survey responses regarding the ERP (Enterprise Resource Planning) usage experience of workers from shipbuilding and maritime companies in South Korea.</p> <ol> <li><strong>ID</strong>: Unique identifier for each respondent.</li> <li><strong>Gender</strong>: Respondent's gender (1 for Male, 2 for Female).</li> <li><strong>Usage Period</strong>: Duration for which the respondent has been using the current system or tool. (1 for Less than 6 months, 2 for 6 months – 1 year, 3 for 1 year – 2 years, 4 for 2 years – 5 years, 5 for More than 5 years)</li> <li><strong>Age</strong>: Respondent's age. (1 for 10s, 2 for 20s, 3 for 30s, 4 for 40s, 5 for 50s)</li> <li><strong>Position</strong>: Respondent's position or job title in the organization. (1 for Clerk, 2 for Assistant Manager, 3 for Manager, 4 for Deputy Manager, 5 for General Manager, 6 for Executive Director)</li> <li><strong>Module</strong>: Specific module or section of the tool/system that the respondent mainly uses. (1 for Operation Management, 2 for Design, 3 for Sales/Business Management, 4 for Materials/Procurement, 5 for Quality Management, 6 for Management/Human Resource, 7 for Finance Accounting, 8 for Other)</li> <li><strong>SYQ1, SYQ2, SYQ3</strong>: Questions related to System Quality. These are statements or questions gauging the user's perception of the system's reliability, efficiency, etc.</li> <li><strong>INQ1, INQ2, INQ3</strong>: Questions related to Information Quality. These relate to the accuracy, relevancy, and timeliness of the information provided by the system.</li> <li><strong>PUS1, PUS2, PUS3</strong>: Questions related to Perceived Usefulness. These are statements or questions assessing how useful the user finds the system in their day-to-day tasks.</li> <li><strong>SEQ1, SEQ2, SEQ3</strong>: Questions related to Service Quality. These focus on the user's satisfaction with the service/support related to the system.</li> <li><strong>PEOU1, PEOU2, PEOU3</strong>: Questions related to Perceived Ease of Use. These assess how easy the user finds the system to use and navigate.</li> <li><strong>IFL1, IFL2, IFL3</strong>: Questions related to another ICT Infrastructure. </li> <li><strong>ISK1, ISK2, ISK3</strong>: Questions related to ICT Skills. These gauge the user's proficiency or comfort level with ICT tools and technologies.</li> <li><strong>TMS1, TMS2, TMS3</strong>: Questions related to Top Management Support. These assess the user's perception of how much support and endorsement the system receives from top management.</li> <li><strong>COI1, COI2, COI3</strong>: Questions related to Continuance Intention. These measure the user's intention to continue using the system in the future.</li> </ol>
NREM sleep EEG and wake ERP summary: The first wave of the Global Research Initiative on the neurophysiology of schizophrenia (GRINS)
Open the record for dataset details and reuse information.
River Erpe (Berlin, Germany) groundwater levels, temperature and 222-radon data set, June 2019
<p>Time series of groundwater levels and temperature, chloride concentrations as well as 222-radon activities in piezometers P0 to P9 and P11 located close to Heidemühle at the River Erpe, Berlin, Germany, collected between June and November 2019. In "Rn_eql_incubations.csv", "mea" denotes the mean activity and "sdd" the associated standard deviation.</p>
Behavior and ERP data for "A logarithmic magnitude representation in working memory"
<p>Behavior and ERP data for "The foundation of Fechner's law"</p>
L'ERP ET LE SYSTEME EDUCATIF : QUELLE SYNERGIE ? REVUE DE LITTÉRATURE
<p><span>Cet article examine l'adoption et l'utilisation des systèmes d'information dans le secteur éducatif, en particulier au sein des institutions académiques. L'objectif principal est d'analyser les facteurs qui favorisent ou freinent cette adoption et d'évaluer l'impact du soutien technique et de l'accompagnement client sur l'expérience utilisateur. Une étude approfondie a été réalisée à partir d'entretiens semi-structurés avec des enseignants et des administrateurs utilisant la plateforme digitale . Les résultats montrent que l'utilité perçue et la facilité d'utilisation sont des éléments clés pour l'adoption de la technologie, malgré certains défis persistants. L'étude propose des recommandations pour améliorer l'intégration des systèmes d'information dans les pratiques éducatives, en mettant l'accent sur un soutien technique personnalisé et l'enrichissement des ressources pédagogiques.</span></p>
SMART ERP for the Behavioral Treatment of Youth With Obsessive Compulsive Disorder (OCD)
ClinicalTrials.gov study NCT03672565. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.