Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

Microservice migration survey and responses

Survey sent out for our research on microservice migration of legacy systems, and the responses by the participants, as well as a thematic analysis of the open questions.

Dataset of simulated room impulse responses in three coupled rooms

This dataset accompanies the publication <blockquote> <div> <div> <div> Georg Götz, Teodors Kerimovs, Sebastian J. Schlecht, and Ville Pulkki. Dynamic late reverberation rendering using the common-slope model. In Proceedings of the AES 6th International Conference on Audio for Games, Tokyo, Japan, April 2024. </div> </div> </div> </blockquote> <div> <div> <div>   <div> <div> <div> The dataset includes room acoustic simulations conducted with the hybrid simulation suite Treble, using a transition frequency of approximately 750 Hz between wave-based and GA simulation. We simulated the coupled room geometry depicted in the file room_geometry2.pdf. The orange × indicates the source position, and receivers were uniformly distributed on the xy-plane with 0.3 m resolution. Each room has a height of 3 m and exhibits a uniform absorption distribution. Room R2 is the most reverberant with an absorption coefficient similar to concrete (αR2 = 0.01), whereas R1 and R3 are significantly less reverberant with αR1 = 0.2 and αR3 = 0.1, respectively. The dataset also includes the common-slope analysis results for the omnidirectional responses and also for the sector-based analysis as described in the paper. Please also refer to the following paper for more details on the common-slope analysis: <blockquote> Georg Götz, Sebastian J. Schlecht, and Ville Pulkki. Common-slope modeling of late reverberation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, pp. 3945–3957, September 2023. doi: <a href="https://doi.org/10.1109/TASLP.2023.3317572" target="_blank" rel="noopener">10.1109/TASLP.2023.3317572</a> </blockquote> </div> </div> </div> </div> </div> </div>

Ex vivo modeling of precision immuno-oncology responses in lung cancer

Single-cell RNA-sequencing (scRNA-seq) data from paired lung cancer organoids and immune cells. The experiment was performed using the Single Cell 5' solution of 10X Genomics.  The dataset includes 15 samples from 4 multiplexed experiments. The multiplexing was performed using the Feature Barcoding technology of 10X Genomics. <table> <tbody> <tr> <td>Sample name</td> <td>Multiplexed experiment</td> <td>Donor </td> <td>Hashtag name</td> <td>Description</td> </tr> <tr> <td>1-PBMCs_lung-19</td> <td>PBMCs_demux</td> <td>Lung-19</td> <td>Hashtag_1</td> <td>Untreated, baseline PBMCs for Lung-19</td> </tr> <tr> <td>2-PBMCs_lung-35</td> <td>PBMCs_demux</td> <td>Lung-35</td> <td>Hashtag_2</td> <td>Untreated, baseline PBMCs for Lung-35</td> </tr> <tr> <td>3-PBMCs_Lung-25</td> <td>PBMCs_demux</td> <td>Lung-25</td> <td>Hashtag_3</td> <td>Untreated, baseline PBMCs for Lung-25</td> </tr> <tr> <td>4-T_cells_Lung-19</td> <td>PBMCs_demux</td> <td>Lung-19</td> <td>Hashtag_4</td> <td>Tumor-stimulated immune cells for Lung-19</td> </tr> <tr> <td>5-T_cells_Lung-35</td> <td>PBMCs_demux</td> <td>Lung-35</td> <td>Hashtag_5</td> <td>Tumor-stimulated immune cells for Lung-35</td> </tr> <tr> <td>6-T_cells_Lung-25</td> <td>PBMCs_demux</td> <td>Lung-25</td> <td>Hashtag_6</td> <td>Tumor-stimulated immune cells for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-19</td> <td>Hashtag_1</td> <td>Tumor cells alone for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-35</td> <td>Hashtag_2</td> <td>Tumor cells alone for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-25</td> <td>Hashtag_3</td> <td>Tumor cells alone for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-19</td> <td>Hashtag_7</td> <td>Tumor cells and ts-immune cells for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-35</td> <td>Hashtag_8</td> <td>Tumor cells and ts-immune cells for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-25</td> <td>Hashtag_9</td> <td>Tumor cells and ts-immune cells for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-19</td> <td>Hashtag_10</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-35</td> <td>Hashtag_12</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-25</td> <td>Hashtag_13</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-25</td> </tr> </tbody> </table> This Zenodo repository provides: <ul> <li>Processed RNA-seq and hashtag oligo sequencing (HTO-seq) data (feature_bc_matrices.zip)</li> <li>Hashtag names and sequences (Custom_CMO_set.csv), which are needed to rerun Cellranger</li> <li>Seurat v5 objects (seurat_object_all_tumor_cells.rds, seurat_object_all_immune_cells.rds, seurat_object_PBMCs_demux.rds)</li> </ul> This Zenodo repository does not provide: <ul> <li>Sensitive raw sequencing data</li> <li>Sensitive metadata</li> </ul> The raw data generated from the scRNA sequencing is available at the European Genome-phenome Archive (EGA; <a href="https://ega-archive.org">https://ega-archive.org</a>) under accession number EGAD50000000845. <div> <div>   To cite our work: </div> Bassel Alsaed et al. Ex vivo modeling of precision immuno-oncology responses in lung cancer.Sci. Adv.10,eadq6830(2024).DOI:<a href="https://doi.org/10.1126/sciadv.adq6830">10.1126/sciadv.adq6830</a></div>

Bivariate GWA mapping reveals associations between aliphatic glucosinolates and plant responses to thrips and heat stress

Supplemental data on bivariate GWA mapping of stress phenotypes and metabolomes of Arabidopsis.

Streptococcus pyogenes pharyngitis elicits diverse antibody responses to key vaccine antigens influenced by the imprint of past infections.

Here you will find the raw data (RawData.RData) and code (CHIVAS_SEROLOGY_Code.Rmd, an R Markdown file) for generating the analysis and figures for the following publication: Streptococcus pyogenes pharyngitis elicits diverse antibody responses to key vaccine antigens influenced by the imprint of past infections. Joshua Osowicki1,2,3 #, Hannah R Frost1 #, Kristy I Azzopardi1, Alana L Whitcombe4, Reuben McGregor4, Lauren H. Carlton4, Ciara Baker1, Loraine Fabri1,5,6, Manisha Pandey7, Michael F Good7, Jonathan R. Carapetis8,9,10, Mark J Walker11,12,13, Pierre R Smeesters1,2,5,6, Paul V Licciardi2,14, Nicole J Moreland4 *, Danika L Hill15 *, Andrew C Steer1,2,3 * Provided in the RData file are the following items:  Dataframes:  "outcome" : clinical variables associated with human challenge for each participant "data" : ELISA and functional antibody responses for human challenge participants. Each timepoint and isotype for each antigen as seperate column) "data_long": Data equivalent to "data" file but in long format, i.e. One column for each antigen, timepoint and isotype as factors.  "data.melt" :  Data equivalent to "data" file but in longer format , i.e. timepoint, isotype and antigen as factors, 'value' as ELISA AU.  "luminex" : IgG responses to 6 antigens analysed by luminex bead-based assay in human challenge participants. "luminex.children" : IgG responses to 6 antigen analysed by luminex bead-based assay in children Vectors: "pharyngitis" : participant "id" for the 19 individuals that developed pharyngitis.  "Antigen.Order" : relates to "Main" antigen classification used in Figure 2 'additional" : relates to "Additional   Function:  "custom_theme" : used as a theme when using ggplot to graph.  Adobe Illustrator or Inkscape were used to generate the final image files for publication, with some graph editing to axes labels, font size, adding p-values etc.    Additional files:   3 .csv files have been included for download "ELISA_data_wide_format.csv", a wide format data table of 25 human challenge individuals and 219 variables. Equivalent to the 'data' dataframe in the RData file "CHIVAS_luminex.csv", a long format data table of 25 human challenge participants at 1 week, 1 month, and 3 months. Equivalent to the 'luminex' dataframe in the RData file.  "Luminex.children.csv", a datatable of 6 luminex variables for 39 children (healthy and post pharyngitis). Equivalent to the 'luminex.children' dataframe in the RData file.

D1.4 - Consolidated OA data set for responsible research practices and trust in science: POIESIS National and Regional Database

Deliverable 1.4 of the POIESIS project is a consolidated open access data set for responsible research practices and trust in science. D1.4 consists of two databases: the POIESIS National Database and the POEISIS Regional Database. The method and rationale behind the construction of all indicators in both databases are laid out in Work Package 1’s previous deliverable, D1.3 (Bauer et al., 2024). The two database are accompanied by a Guidance Sheet which provides details and relevant notes regarding the structure of the two databases.

opencc-by-nc-4.0Aug 2024View details →

Ndufs4 knockout mice with isolated complex I deficiency engage a futile adaptive brain response - supplementary data

This study aims to provide insight into the Leigh Syndrome pathomechanism at the sub-brain level. To this end, a comparative proteome analysis was performed on various brain regions of 6 wildtype (WT) and 6 whole body (WB) Ndufs4-/- (WB-KO) mice over two experiments. Samples were collected at six weeks after birth (i.e. ~ one week prior to WB-KO death). For the first experiment samples were collected from a brain slice (BrSl), which contained (parts of) the CC, HC, hypothalamus, thalamus, caudate putamen, and olfactory areas. For the second experiment the analysed brain regions consisted of cerebellum (CB), cerebral cortex (CC), hippocampus (HC), inferior colliculust (IC), and superior colliculus (SC).

Data for Responsive Soft Interface Liquid Crystal Microfluidics

The data used in the article entitled Responsive Soft Interface Liquid Crystal Microfluidics.

Individualistic reward-seeking strategies that predict response to nicotine emerge among isogenic male mice living in a micro-society

This repository contains data and basic code for analysis and modeling used in the following paper: Fayad et al. ""Individualistic reward-seeking strategies that predict response to nicotine emerge among isogenic male mice living in a micro-society", published on BioRxiv (https://www.biorxiv.org/content/10.1101/2023.10.28.564522v1)

Electrochemical data shown in A. Fasano, C. Baffert, C. Schumann, G Berggren, J. Birrell, V. Fourmond, C. Léger, "Kinetic modeling of the reversible or irreversible electrochemical responses of FeFe-hydrogenases", J. Am. Chem. Soc 146, 2, 1455–1466 (2024) doi: 10.1021/jacs.3c10693

Open the record for dataset details and reuse information.

Data set for "Multi-frequency SuperDARN HF radar observations of the ionospheric response to the October 2023 annular solar eclipse"

This data set contains rawACF and sounding mode data files from the Christmas Valley East (CVE) SuperDARN radar and manually scaled ionogram parameters from the Boulder (BC840) Digisonde used in the paper "Multi-frequency SuperDARN HF radar observations of the ionospheric response to the October 2023 annular solar eclipse" submitted to Geophysical Research Letters.

POST-FIRE-GERMINATIVE-RESPONSE,-FRUITS-AND-SEEDS-TRAITS-OF-SOME-SPECIES-OF-THE-TDF-IN-COLOMBIA

In fire-prone ecosystems, certain plant species possess diaspores with morphological and physiological traits that enable them to persist and thrive following fire events. Postfire germination has been associated with various fruit and seed traits, including fruit lignification, seed size, seed weight and seed dormancy. However, the collective role and interactions of these traits in determining germination tolerance after wildfires remain unclear. We characterized ten key traits (fruit type and size (length and width), seed mass and size (length and width), dispersal mechanism, storage behavior, seed dormancy, and ecological guild) across eighteen species exhibiting three post-fire germination responses (tolerant, sensitive, and stimulated). Multinomial logistic regression was used to determine the relationship between these traits. Our findings suggest practical implications for restoration and enrichment strategies in Colombian tropical dry forests by prioritizing species with protective fruits, dormant and orthodox seeds to increase the probability of sexual regeneration after fires, and to contribute to the functional diversity and resilience of these ecosystems.

Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinoma

This repository contains imaging mass cytometry (IMC) data and associated files for the publication "Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinoma". Code used to analyze these data can be found at https://github.com/FertigLab/HCCTLS. Raw IMC data have been uploaded as .mcd files (n=10). The file "IMCpanel.xlsx" indicates the antibodies used for each IMC channel. In addition, we have uploaded .tiff files (n=38) generated from each .mcd, which were used to identify and annotate individual tertiary lymphoid structures (TLS) as some ROI had multiple TLS. The file "Updated Catalogue of TLS with S numbers.xlsx" indicates which TLS correspond to each ROI in the .mcd files. To determine the area of each TLS, .tiff files were annotated using ImageJ and the area of each TLS was exported and stored in the file, "TLS_selection_area_pixels.csv". The XY coordinates of individual TLS were obtained by using FlowJo software to annotate .fcs files resulting from single cell segmentation of each ROI.

Topography Response to Horizontal Slab Tearing and Oblique Continental Collision: 3D Thermomechanical Numerical Modelling

This repository submission provides the 3D thermo-mechanical numerical modelling code I3ELVIS developed by the workgroup of Prof. Taras Gerya (ETH Zürich). The code is based on finite-difference and marker-in-cell methods (Gerya & Yuen, 2003, 2007; Gerya, 2019). The code solves the momentum, continuity, and energy equations on the fixed Eulerian grid and transports the physical properties by Lagrangian markers using the velocity field. The code also accounts for the major phase transitions in the Earth’s mantle and internal heat sources arising from adiabatic, radiogenic, and frictional heating. Partial melting and melt extraction processes are neglected for the sake of simplicity. A more detailed description of the code, including the governing equations and the adopted rheological model can be found in various published literature (Andrić-Toma&scaron;ević et al., 2023; Boonma et al., 2023; Maiti et al., 2024).  Interested users are recommended to contact Prof. Taras Gerya (taras.gerya@erdw.ethz.ch).  The repository also provides input and output files to run and reproduce model results and manuscript figures of Maiti et al., 2024 (JGR Solid Earth). Paraview States for processing .vtr result files and visualizing the model output data. Matlab script to visualise the topography from .grd files.

From forest strategy to measures: what are forest owner attitudes & area size responses toward climate-proof forest management in Sweden and the Netherlands?

The anonymous datasets from Swedish (records of 98 responses) and Dutch forest climate surveys (records of 241 responses), which are used for the statistical Kruskal Wallis tests, are freely available and accessible via this repository database (https://zenodo.org), as prescribed by the FAIR data policy of the Forclimit project sponsors.  The surveys and the Small Scale Forestry article are deliverables of the European 2017-2020 Forclimit project "Mobilizing and monitoring climate positive efforts in the forest sector" The umbrella of the project is FACCE ERA-GAS, i.e. ERA-NET co-fund for monitoring & mitigation of GHG from agriculture and sylviculture (Horizon2020, award # 696356). The other co-funders are the Dutch research council 'NWO' (grant award # 516-095-7542) and its Swedish equivalent 'FORMAS' (grant award # 2017-01751).

Dataset and R script - Non-linear transcriptomic responses to compounded environmental changes across temperature and resources in a pest beetle, Callosobruchus maculatus

This dataset contains data and R script for analysis on life history and transcriptomic responses to single dimensional changes in resource (chickpea-27°C) and temperature (cowpea-35°C) and multi-dimensional environmental changes in resource and temperature (chickpea-35°C) in a pest beetle, Callosobruchus maculatus (control treatment = cowpea-27°C). Dataset contains life history data collected in laboratory conditions (tab 1), logFC data (RNA-sequencing; Novogene Co. Ltd.) for Spearman rank correlation tests between treatments (tabs 2-4), read count data (RNA-sequencing; Novogene Co. Ltd.) for differential expression analysis using edgeR (R1-5 = Four samples at cowpea-27°C; R7-12 = Four samples at cowpea-35°C; R13-17 = Four samples at chickpea-27°C; R25-28 = Four samples at chickpea-35°C; tab 5) and edgeR output data for plotting in R (tab 6).

opencc-by-4.0May 2023View details →

A transposase-derived gene responsible for human brain development

Vertebrate brain development is associated with prominent neuronal cell death and DNA breaks, but their causes and functions are not well understood. DNA transposable elements and transposase-derived genes could contribute to DNA breaks and somatic genome rearrangements, however their contributions to brain development are largely unknown. PiggyBac Transposable Element Derived 5 (PGBD5) is an evolutionarily conserved vertebrate DNA transposase-derived gene with retained nuclease activity in human cells. Here, we show that PGBD5 contributes to normal brain development in mice and humans, and its deficiency causes disorder of intellectual disability, movement, and seizures. In mice, Pgbd5 is required for the developmental induction of post-mitotic DNA breaks and recurrent somatic brain genome rearrangements. In the cerebral cortex, loss of Pgbd5 leads to aberrant differentiation and gene expression of distinct neuronal populations, including specific types of glutamatergic neurons, which can explain the features of PGBD5 deficiency in humans. Thus, PGBD5 is a transposase-derived gene required for brain development in mammals.

The Response of the Southern Ocean to Climatological Iceberg Freshwater Forcing

Data Availability Statement: The data presented here are the model outputs for the study titled "The Response of the Southern Ocean to Climatological Iceberg Freshwater Forcing." Due to size limitations on data uploads, a coarser resolution dataset is provided. For access to the full-resolution dataset, please contact the corresponding author at <a rel="noopener">jingwei.zhang@utas.edu.au</a>.

opencc-by-4.0Oct 2024View details →

A fMRI dataset in response to large number of short natural dynamic facial expression videos

<pre>#A fMRI dataset in response to large number of short natural dynamic facial expression videos Natural facial expressions dataset (NFED),a dataset of functional magnetic resonance imaging (fMRI) responses to 1,320 short (3s) facial expression video clips.NFED offers researchers fMRI data that enables them to investigate the neural mechanisms involved in processing emotional information communicated by facial expression videos in real-world environments. The dataset contains raw data, pre-processed volume data,pre-processed surface data and suface-based analyzed data. To get more details, please refer to the paper at {website} and the dataset at https://openneuro.org/datasets/ds005047 ## Preprocess procedure The MRI data were preprocessed by using Kay et al, combining code written in MATLAB and certain tools from FreeSurfer, SPM,and FSL(http://github.com/kendrickkay). We used FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to construct the pial and white surfaces of participants from the T1 volume. Additionally, we established an intermediate gray matter surface between the pial and the white surfaces for all participants. **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/volume_pre-process/** Detailed usage notes are available in codes, please read carefully and modify variables to satisfy your customed environment. ## GLM of main experiment We utilized a single-trial General Linear Model (GLMsingle) (https://github.com/cvnlab/GLMsingle) approach, an advanced denoising approach in MATLAB R2019a, to model the pre-processed fMRI data from main experiment. For single trials, the method of GLM was developed to offer estimations of BOLD response magnitudes ('betas'). GLMsingle requires only fMRI time series data and a design matrix as inputs, integrating three techniques to enhance the accuracy of experimental GLM beta estimates. Firstly, for each voxel, a custom HRF is identified from a library of candidate functions. Secondly, cross-validation is utilized to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Thirdly, to improve the stability of beta estimates for closely spaced trials, ridge regression is employed on a voxel-wise basis to regularize the betas. In this study, three betas were calculated by analyzing the BOLD response corresponding to individual video onset ranging from 1 to 3 seconds with 1-second intervals. We produced individual GLMsingle models for each session (consisted of 4 training runs and 2 test runs). In general, for each video within the training set, 2 (repetitions) x 3 (seconds) betas were acquired. Similarly, for each video within the testing set, 10 (repetitions) x 3 (seconds) betas were acquired. The utilization of repetitions enabled us to acquire video-evoked responses with a high signal-to-noise ratio (SNR). The regressors in the GLMsingle toolbox mainly include the following categories: 1.Experimental Design Matrix: This is constructed based on the experimental design, including experimental conditions, task events, etc., and is used to estimate the BOLD (Blood - Oxygen - Level - Dependent) signal response. 2.Data-driven nuisance regressors: The data-driven nuisance regressors used in the GLMdenoise technique. These are identified by analyzing the data itself and are used to remove noise and improve the accuracy of beta estimation. 3.Physiological noise regressors: May include indicators of physiological signals such as heart rate and respiration, and are used to correct the influence of physiological noise on the BOLD signal. 4.Movement parameter regressors: Usually include head movement parameters, such as translation and rotation, and are used to correct signal changes caused by head movement. 5.Polynomial regressors: Polynomial terms used to o characterize the baseline signal level. **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/GLMsingle-main-experiment/matlab/NFED_GLMsingle.m** #### retinotopic mapping The fMRI data from the the population receptive field experiment were analyzed by a pRF model implemented in the analyzePRF toolbox (http://cvnlab.net/analyzePRF/) to characterize individual retinotopic representation. Make sure to download required software mentioned in the code. **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_prf.m** #### fLoc experiment We used GLMdenoise,a data-driven denoising method,to analyze the pre-processed fMRI data from the fLoc experiment.We used a "condition-split" strategy to code the 10 stimulus categories, splitting the trials related to each category into individual conditions in each run. Six response estimates (beta values) for each category were produced by using six condition-splits.To quantify selectivity for various categories and domains,we computed t-values using the GLM beta values after fitting the GLM.The regions of interest with category selectivity for each participant were defined by using the resulting maps. **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_floc.m** ## Validation ### Basic quality control The fundamental quality control suggests that the data displays good quality. To evaluate the quality of the structural data obtained from NFED, we employed four crucial metrics:Coefficient of Joint Variation(CJV), Contrast-to-Noise Ratio (CNR), Signal-to-Noise Ratio in Grey Matter (SNR_GM), and Signal-to-Noise Ratio in White Matter (SNR_WM). Specifically, the CJV is between white matter(WM) and grey matter(GM).The CNR assesses the relationship between the contrast of GM and WM with the noise present in the image. The SNR assesses the connection between the mean signal measurements and the noise present in the image. The SNR assessment is conducted individually for GM and WM.For quality control of the NFED’s functional scans, we assessed the amount of head motion for each participant and the temporal signal-to-noise ratio (tSNR) of the time-series data, separately. **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/T1_Image-quality-metrics/T1data/noiseeval/cal_SNRindex.m** **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/FD/FD.py** **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/tSNR/tSNR.py** ### The visual cortex exhibits reliable BOLD responses to natural facial expression videos stimuli in main experiment. In the main experiment, there were 5 participants who accomplished 10 sessions, each consisting of of 4 training runs and 2 test runs, making a total of 60 runs(40 training and 20 test runs) in all. For each run, 30 (videos)*2 (repetitions) x 3 (seconds) betas were acquired. Z-score normalization was performed on the raw betas at each voxel for each run.The betas were then averaged across stimulus repetitions to generate a vector of betas. In general, 44 runs (40 training runs and 4 test runs)*90 betas were acquired. Hence, the test-retest reliability of responses to these videos in main experiment were evaluated through computing the Pearson correlation between the 90 betas obtained from the even runs and odd runs on each vertex. **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/reliability_face/reliability_face.py** ### noise celling The code are available at https://openneuro.org/datasets/ds005047/validation/code/noise_celling/sub-xx" store the intermediate files required for running the program **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/noise_celling/Noise_Ceiling.py** ### Correspondence between human brain and DCNN The code are available at https://openneuro.org/datasets/ds005047. We combined the data from main experiment and functional localizer experiments to build an encoding model to replicate the hierarchical correspondences of representation between the brain and the DCNN. The encoding models were built to map artificial representations from each layer of the pre-trained VideoMAEv2 to neural representations from each area of the human visual cortex as defined in the multimodal parcellation atlas. **code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/dnnbrain/** ### Semantic metadata of action and expression labels reveal that NFED can encode temporal and spatial stimuli features in the brain The code are available at https://openneuro.org/datasets/ds005047/validation/code/semantic_metadata/xx_xx_semantic_metadata" store the intermediate files required for running the program. **code:https://openneuro.org/datasets/ds005047/derivatives/validation/code/semantic_metadata/** ## results The results can be viewed at "https://openneuro.org/datasets/ds005047/derivatives/validation/results/brain_map_individual". ## Whole-brain mapping The whole-brain data mapped to the cerebral cortex as obtained from the technical validation. **code: https://openneuro.org/datasets/ds005047/derivatives/validation/results/show_results_allbrain/Showresults.m** ## Mannually prepared environment We provide the *requirements.txt* to install python packages used in these codes. However, some packages like *GLM* and *pre-processing* require external dependecies and we have provided the packages in the corresponding file. ## stimuli The video stimuli used in the NFED experiment are saved in the "stimuli" folders. </pre>