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Resting state with closed eyes for patients with depression and healthy participants
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Cortical myelin measured by the T1w/T2w ratio in individuals with depressive disorders and healthy controls
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Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
Wearable data and self reported fatigue scores from a remote observational study in Sjogren's disease, SLE and healthy participants
<p>Fatigue is a subjective, complex, and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. To date, there is no consensus about reliable quantitative assessments of fatigue.</p> <p>We collected observational data for a period of one month from 296 participants (healthy volunteers, Sjogren’s Syndrome, and Systemic Lupus Erythematosus patients) in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity, and sleep daily features, and app-based daily and weekly questions (e.g., pain, mood, general physical activity, and fatigue). When matching both sensor data and PROs, and excluding missing data, the dataset contains data from 183 subjects and 3950 recording days.</p> <p>The analysis of the association of digital data to self-reported fatigue was published at <em><strong>Rao C., et. al. (2023), Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease, Frontiers in Digital Health</strong></em>.</p> <p>Demographics, digital parameters, and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Details on the data files can be found under README.txt.</p>
resting state fMRI of 17 idiopathic epileptic dogs and 20 healthy control dogs
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Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals
<p>This repository contains Electrogastrography signals termed Electrogastrograms (<a href="https://en.wikipedia.org/wiki/Electrogastrogram">EGG</a>) recorded with surface Ag/AgCl electrodes placed over stomach and pre-processed in 20 healthy individuals (8 Females and 12 Males). The method for EGG recording and pre-processing together with subjects' data can be found in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>For each subject, EGG was recorded from three locations before (fasting state) and after (postprandial state) a commercial oat meal (274 kcal). Two 20 minutes recordings (files) are obtained for each subject - fasting and postprandial.</p> <p>Naming convention for files: <strong>subjects ID _ type of recording (fasting / postprandial)</strong>.</p> <p>Sample rate was set at 2 Hz and <a href="https://en.wikipedia.org/wiki/Analog-to-digital_converter">A/D card</a> had 16 bits resolution. Gain of the amplifier was set at 1000. Overall, file size is 7200 samples (2400 samples for each channel). All signals were filtered with 3<sup>rd</sup> order band-pass <a href="https://en.wikipedia.org/wiki/Butterworth_filter">Butterworth filter</a> with cut-off frequencies of 0.03 Hz and 0.25 Hz. In order to avoid phase distortion, zero-phase digital filtering was performed in <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> R2013a by <a href="https://www.mathworks.com/help/signal/ref/filtfilt.html">filtfilt()</a> function. <a href="https://www.gnu.org/software/octave/">GNU Octave</a> code for analysis of EGG signals with statistical calculations presented in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a> is also provided (<a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>).</p> <p>For convenient test download and appropriate preview, we provided all signals in <a href="https://en.wikipedia.org/wiki/Zip_(file_format)">.zip</a> and sample signal for ID1 in <a href="https://en.wikipedia.org/wiki/Text_file">.txt</a> form.</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/EGG-database.zip?versionId=84315b6b-58da-4655-83f4-8f1d43c3b02c">EGG-database.zip</a>, data files, text format</li> <li><a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>, GNU Octave code</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/README.txt">README.txt</a>, metadata for data files, text format</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_fasting.txt?versionId=47d0bd09-1a87-42f2-a3e5-ef0c4b4a18e2">ID1_fasting.txt</a> and <a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_postprandial.txt?versionId=c8936a32-2896-44d6-bf3d-2ee37887766c">ID1_postprandial.txt</a>, sample data files for subject ID1, text format</li> </ol> <p><strong>Data files contain numerical values with decimal point according to the following structure</strong></p> <ol> <li>column - CH1* (recorded samples from channel 1)</li> <li>column - CH2* (recorded samples from channel 2)</li> <li>column - CH3* (recorded samples from channel 3)</li> </ol> <p>* For exact anatomical locations for EGG channels CH1, CH2, and CH3, please refer to <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>If you find these signals useful for your own research or teaching class, please cite both relevant paper and dataset as:</p> <ol> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2019. Simple gastric motility assessment method with a single-channel electrogastrogram. <em>Biomedical Engineering/Biomedizinische Technik</em>, <em>64</em>(2), pp.177-185, doi: <a href="https://doi.org/10.1515/bmt-2017-0218">10.1515/bmt-2017-0218</a>.</p> </li> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2020. Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals [Data set]. <em>Zenodo</em>, doi: <a href="https://doi.org/10.5281/zenodo.3730617">10.5281/zenodo.3730617</a>.</p> </li> </ol> <p>DISCLAIMER: The GNU Octave code is provided without any guarantee and it is not intended for medical purposes.</p>
Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects
<p><strong>Study Sample and Ethics Statement</strong></p> <p>The sample consisted of 71 university students, average age 20.38 years (<em>SD</em> = 2.96), 78.8% female. Subjects with previous cardio-vascular disorders and irregular ECG were excluded. The study has been approved by the Institutional Review Board of the Department of Psychology, University of Belgrade No. 2018-19. All participants signed Informed Consents in accordance with the Declaration of Helsinki.</p> <p>In the course of visual examination, it was decided to discard ECG from one subject due to the presence of bigeminial arythmia, so further analysis was performed on 70 subjects instead of 71.</p> <p><strong>Measurement Setup</strong></p> <p>BIOPAC sensors (Biopac Systems Inc., Camino Goleta, CA, USA) were used for recording biosignals in another study (<a href="http://empirijskaistrazivanja.org/wp-content/uploads/2021/04/EIP2020_conf_proceedings.pdf#page=17">Bjegojević et al., 2020</a>). Here, we used only ECG signals recorded in sitting relaxed position from standard bipolar Lead I using the BIOPAC MP150 unit with AcqKnowledge software and ECG 100C module with surface H135SG Ag/AgCl electrodes (Kendall/Covidien, Dublin, Ireland). In order to decrease skin-electrode impedance, the skin was cleaned with Nuprep gel (Weaver & Co., Aurora, USA) to reduce skin-electrode impedance. The sampling frequency was set at 2000 Hz and the gain was set to 1000.</p> <p>ECG signals were recorded during relaxation in a sitting position and data were recorded during 2 min long intervals. More information is available in the article [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>].</p> <p><strong>Dataset, Code, and Feature Extraction Instructions</strong></p> <ol> <li><a href="https://zenodo.org/record/5736849/files/analysisECG.R?download=1">analysisECG.R</a>, function with analysis procedures written in <a href="https://www.r-project.org/">R programming language</a></li> <li><a href="https://zenodo.org/record/5736849/files/anec12919-sup-0001-supinfo.pdf?download=1">anec12919-sup-0001-supinfo.pdf</a>, detailed ECG processing and feature extraction procedure (also available as <a href="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fanec.12919&file=anec12919-sup-0001-Supinfo.docx">supplementary material</a> for article [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>])</li> <li><a href="https://zenodo.org/record/5736849/files/ecg_70.txt?download=1">ecg_70.txt</a>, .txt data file, text format</li> <li><a href="https://zenodo.org/record/5736849/files/mainECG.R?download=1">mainECG.R</a>, a main program written in R programming language</li> <li><a href="https://zenodo.org/record/5736849/files/R-studio-version-info.txt?download=1">R-studio-version-info.txt</a>, the version of <a href="https://www.rstudio.com/">R Studio</a> where the code was tested</li> <li><a href="https://zenodo.org/record/5736849/files/R-version-info.txt?download=1">R-version-info.txt</a><a href="https://zenodo.org/api/files/aa8d999e-1b08-44b5-883f-0540afe8feb8/R-version-info.txt"> </a>, the version of R programming language where the code was tested</li> </ol> <p>For ECG-based feature extraction, we used the following R packages:</p> <ol> <li><strong>signal</strong> - Signal Processing Functions (signal developers (2014). <em>signal: Signal processing</em>. <a href="http://r-forge.r-project.org/projects/signal/">http://r-forge.r-project.org/projects/signal/</a>)</li> <li><strong>pracma</strong> - Practical Numerical Math Functions ( Borchers, H. W. (2019). <em>Package ‘pracma’: Practical numerical math functions</em>. R package version, 2(1). <a href="https://CRAN.R-project.org/package=pracma">https://CRAN.R-project.org/package=pracma</a>)</li> </ol> <p>Please, note that the results of personality trait tests are not available in the current dataset. We are planning to open them in our future research. For more information and planned availability in open access, please, contact the corresponding author of [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>] by e-mail (<a href="mailto:nadica.miljkovic@etf.bg.ac.rs">nadica.miljkovic@etf.bg.ac.rs</a>).</p> <p><strong>Citing Instruction</strong></p> <p>If you find these signals and code useful for your own research or teaching class, please cite relevant dataset and supporting publications:</p> <ol> <li> <p>Boljanić, T., Miljković, N., Lazarević, L. B., Knežević, G., & Milašinović, G. (2021). Relationship between electrocardiogram-based features and personality traits: Machine learning approach. <em>Annals of Noninvasive Electrocardiology</em>, 00, e12919. <a href="https://doi.org/10.1111/anec.12919">https://doi.org/10.1111/anec.12919</a></p> </li> <li> <p>Bjegojević, B., Milosavljević, N., Dubljević, O., Purić, D., & Knežević, G. (2020). <a href="http://empirijskaistrazivanja.org/wp-content/uploads/2021/04/EIP2020_conf_proceedings.pdf#page=17">In pursuit of objectivity: Physiological measures as a means of emotion induction procedure validation</a>. <em>XXIVI Scientific Conference on Empirical Studies in Psychology</em>, p. 17-19.</p> </li> <li> <p>Boljanić, T., Miljković, N., Lazarević B. Lj., Knežević, G., & Milašinović, G. (2021). Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects (Version 1) [Data set]. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.5599239">https://doi.org/10.5281/zenodo.5599239</a></p> </li> </ol>
Raw data of individuals with Down syndromre, individuals with Williams syndrome, healthy children and adults in a visual learning task, a conditional learning task and a transitive inference task.
<p>Raw data of 17 individuals with Down syndrome (8 girls/women; average age: 17.8 years; range: 7.2-30.8 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 27 individuals with Williams syndrome (16 girls/women; average age: 23.7; range: 9.4-43.8 at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 71<strong> </strong>healthy children (31 girls; average age: 6.42 years; range: 2.95-11.64 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 22 healthy adults (11 femaleswomen; average age: 26.05 years; range: 20.32-29.76 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p>
Spatial characterization of the motor and non-motor somal and axonal transcriptome in adult healthy and mutant FUS mice
<table> <tbody> <tr> <td> <p>Here we investigated the transcriptome of motor and non-motor axons and cell bodies in the context of mutant FUS-related amyotrophic lateral sclerosis (ALS). We applied Nanostring GeoMX Digital Spatial Profiler platform to profile the transcriptome of subcellular compartments in the lower motor circuitry of a mouse model ricapitulating ALS motor symptoms. This work sheds light for the first time on the transcriptomic alterations in axons and in somas which may contribute to axonal degeneration and neuromuscular junction denervation, early features of ALS.</p> </td> </tr> </tbody> </table>
IMU data captured unobtrusively and in-the-wild by Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The dataset contains IMU signals captured in-the-wild via the accelerometer sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). A group of 31 PD patients and 14 Healthy controls contributed accelerometer data using their personal smartphones, for a period spanning many months.Tri-axial acceleration values were recorded automatically whenevera phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a list of python dictionaries, stored in a pickle file. Each dictionary in the list, corresponds to one subject and containes the following fields:</p> <p>1. subject_id: scalar<br> A numerical value that uniquely identifies the subject.</p> <p>2. subject_sessions: list of numpy.array<br> A list of numpy arrays of shape (N, 4) that contains the tri-axial accelerometer sessions that the subject contributed. N denotes the total length of the session in samples (which varies from session to session) Column 0 of the array contains the timestamps of the accelerometer samples. Columns 1-3 contain the acceleration values across the x,y,z directions.</p> <p>3. session_datetimes: list of datetime objects <br> A list of datetime objects that denote the capturing date and time of the corresponding entries in the subject_sessions field.</p> <p>4. annotation: dict<br> A dictionary containing the following tremor-related annotation values:<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.<br> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </p>
Raw data of healthy young adults in the Weather Prediction Task
<p>Raw data of 22 healthy young adults (11 females; average age: 26.29 years; range: 21.72–30.82) in the Weather Prediction Task with 100 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials.</p> <p>Raw data of 15 healthy young adults (9 females; average age: 26.58 years; range: 20.37–28.84) in the Weather Prediction Task with 200 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials.</p> <p>Bochud-Fragnière E, Banta Lavenex P and Lavenex P (2022) What Is the Weather Prediction Task Good for? A New Analysis of Learning Strategies Reveals How Young Adults Solve the Task. Front. Psychol. 13:886339. doi: 10.3389/fpsyg.2022.886339</p>
Magnetic resonance spectroscopy data acquired in tinnitus subjects and healthy volunteers using PRESS sequence
<p>This dataset contains raw free induction decay (FID) signals collected during 1H magnetic resonance spectroscopy (MRS) study in 52 individuals with tinnitus (24 with unilateral and 28 with bilateral tinnitus) and 25 healthy volunteers (described in detail in a separate article doi:10.1038/s41598-023-45024-3).</p><p>Data acquisition was performed using 3T Siemens Prisma Fit scanner with a 20-channel receiver head-coil. A single voxel spectroscopy (SVS) PRESS (Point-Resolved Spectroscopy Sequence) sequence was applied for collection of MRS data, using standard Siemens water suppression (water saturation, 50 Hz bandwidth) and no lipid suppression. MRS data was collected from four cubic 3.75 cm3 (1.5 cm x 1.5 cm x 1.5 cm) regions-of-interest in the brain, placed in the left temporal lobe, right temporal lobe, left frontal lobe, and right frontal lobe. The MRS sequence parameters were: TR (time of repetition) = 2000 ms, TE (time of echo) = 40 ms, TA (time of acquisition) = 4 min 26 s, 128 averages with 1024 time points and 1200 Hz bandwidth.</p><p>MRS data is stored in RDA file format, developed by Siemens (see doi:10.1002/nbm.4257, Table 1). Each RDA file contains a text header (which can be viewed using a standard notepad application) and binary FID signal under the header. Data can be imported for analysis using several open-source packages (tested with FID-A doi:10.1002/mrm.26091 and spant doi:10.21105/joss.03646). </p><p>Naming scheme of files is as follows:</p><p><participant ID>_<hemisphere: L or R>_<region: F (frontal) or T (temporal)>.rda</p><p>For example: <i>001_L_F.rda</i> is data from participant 001 collected from a voxel placed in a ROI in the left frontal lobe.</p><p>In order to allow replication of the results from the original article, we also added information about the group of each of the subjects. This information is stored in a TSV file containing two columns: <i>participant_ID</i> and<i> group</i> (C – control, TU – unilateral tinnitus, TB – bilateral tinnitus).</p><p>Aside from replication of our results this dataset may be used e.g. for testing of different MRS data processing pipelines.</p>
Regional Heat Vulnerability Map and Cooling Solutions: A webtool of the Healthy Urban Environments Initiative
## Regional Heat Vulnerability Map and Cooling Solutions The regional heat vulnerability map and cooling solutions webtool offers two data sources for equitable heat mitigation. The dashboard layers vulnerability data onto land surface temperature regional rankings to identify areas with high and low heat exposure and vulnerability as well as the existing assets in each census block group. Additional layers can be added into the heat vulnerability map to highlight how heat affects critical infrastructures including schools, mobile home parks, parking lots, public transportation stops, pedestrian thoroughfares, and bikeways. The solutions tab showcases a variety of heat mitigation solutions and the research behind them. Heat-related solutions and resources from urban Maricopa County are included, including solutions funded through the Healthy Urban Environment Initiative. The data catalogued here are the underlying data that populate the webtool. ## Healthy Urban Environment (HUE) Initiative - Overview HUE is a solutions-focused research, policy and technology incubator to create healthier communities across Maricopa County (central Arizona, USA) through collaboration between researchers, practitioners and community members. As such, HUE funded rapid development, testing and deployment of heat-mitigation and air-quality improvement strategies and technologies. Heat emerged as the urgent focus, as urban centers across the desert Southwest continue to grow in size and density, aggravating existing challenges posed by the expansion of the built environment. In Phoenix, AZ, this expansion of the built environment creates conditions which magnify the intensity and duration of heat – making it difficult for residents to achieve thermal comfort throughout the day and night. Further, the legacies of urban sprawl and transportation planning in the Phoenix, Arizona metropolitan area have contributed to challenges with atmospheric pollutants. Importantly, urban heat and air qua
Neuroimaging predictors of creativity in healthy adults
Open the record for dataset details and reuse information.
Data for "Gut microbial genes are associated with neurocognition and brain development in healthy children"
<p><strong>Datasets accompanying<em> Gut microbial genes are associated with neurocognition and brain development in healthy children</em>, submitted to Nature Microbiology.</strong></p> <p><strong>Contents:</strong></p> <ul> <li> fecal_samples_master.csv <ul> <li>Metadata for all fecal samples processed by the Klepac-Ceraj Lab at Wellesley College</li> </ul> </li> <li>filemakerdb.csv <ul> <li>Initial export and parsing (long form) of deidentified patient metadata from internal filemnaker pro database</li> </ul> </li> <li>gbm.txt <ul> <li>Info about potentially neuroactive gene sets</li> <li>This was acquired as Supplementary Dataset 1 from <a href="https://doi.org/10.1038/s41564-018-0337-x">https://doi.org/10.1038/s41564-018-0337-x</a></li> </ul> </li> <li>batchXXX_analysis_noknead.tar.gz <ul> <li>Sequencing batches 001-012 (see fecal_samples_master.csv for metadata about samples contained in each batch)</li> <li>Each tarball contains: <ul> <li><strong>cluster.yaml</strong>: configuration file for snakemake pipeline (<a href="https://github.com/Klepac-Ceraj-Lab/snakemake_workflows">repo link</a>)</li> <li><strong>config.yaml</strong>: run configuration for snakemake pipeline</li> <li><strong>.snakemake/</strong>: metadata about snakemake pipeline runs on engaging cluster at MIT</li> <li><strong>output/</strong>: outputs from metaphlan2 and humann2 analysis runs. Note: kneaddata sequence files were not included, but will be uploaded to SRA (link to come)</li> </ul> </li> </ul> </li> <li>All <a href="https://www.uniprot.org/">uniprot</a> searches were performed 2019-09-19 <ul> <li>uniprot-abxr.tsv <ul> <li>search term: "keyword:\"Antibiotic resistance [KW-0046]\" AND reviewed:yes"</li> </ul> </li> <li>uniprot-carbohydrate.tsv <ul> <li>search term: "keyword:\"Carbohydrate metabolism [KW-0119]\" AND reviewed:yes"</li> </ul> </li> <li>uniprot-fa.tsv <ul> <li>search term: (keyword:\"Fatty acid biosynthesis [KW-0275]\" OR keyword:\"Fatty acid metabolism [KW-0276]\") AND reviewed:yes"</li> </ul> </li> </ul> </li> </ul>
Raw data of healthy children and adults in a visual learning task, a conditional learning task and a transitive inference task.
<p>Raw data of 71<strong> </strong>healthy children (31 girls; average age: 6.42 years; range: 2.95-11.64 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p><p>Raw data of 22 healthy adults (11 femaleswomen; average age: 26.05 years; range: 20.32-29.76 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p>
Healthy Eyes, Happy Child
<p>Healthy Eyes, Happy Child” (HEHC) projetc investigated the impact of spectacle correction on the well-being of children from ages 6 to 12 years in rural primary schools in the Pinetown Education District of Kwa-Zulu Natal, South Africa.</p>
PTR-ToF-MS data from cooking experiments in Healthy Energy-efficient Urban Home Ventilation
<pre>The dataset contains high-resolution PTR-Tof MS data from preparing meals consisting of fried salmon and vegetables in SINTEFs ventilation laboratory. <br>The data are organized in csv files containing concatenated results of ppb-values. PTR-ToF-MS grouped by month, m/z-valuens in column names. Relatable to the list of experiments. See readme file for details and 10.1016/j.buildenv.2024.111743 for description</pre>
"Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" - Genomes and annotation
<p>File-dump associated with the manuscript:</p> <p>"<strong>Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" (Not yet published)</strong></p> <p>MGVs refer to the viral genome database in the publication: https://www.nature.com/articles/s41564-021-00928-6 </p> <p> </p> <p>Following uploaded:</p> <p>File 1: VOG Markers in vOTUs/vMAGs and MGV genomes</p> <p>File 2: Viral Tree Newick file with vOTUs/vMAGs and MGV genomes</p> <p>File 3: All vOTUs/vMAGs genomes</p> <p>File 4: Master table annotation of vOTUs/vMAGs</p> <p>File 5: Centenarian bacterial isolate proviruses</p>
Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers
<p>3D EIT dataset of ten healthy human volunteers, as described in the corresponding <a href="http://dx.doi.org/10.1371/journal.pone.0191870">journal publication at PLOS ONE</a> or the first author's <a href="http://dx.doi.org/10.5075/epfl-thesis-8343">PhD thesis at EPFL</a>. Please also read the attached ReadMe file.</p> <p>When using this data please cite the corresponding journal publication:</p> <blockquote> <p>Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers, PLOS ONE, 2018, <a href="http://dx.doi.org/10.1371/journal.pone.0191870">https://dx.doi.org/10.1371/journal.pone.0191870</a></p> </blockquote>
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.