Find research datasets worth reusing

opencc-by-4.0Oct 2023View details →

Collection of spatial information and maps of human past and environment in the Uralic languages speaker area

The collection of spatial information and maps of the past and environment in the Uralic languages speaker area consists excessive amount of multidisciplinary data related to the vast region extending from Eastern Europe to Siberia, encompassing countries like Russia, Finland, and parts of Scandinavia. Uralic speakers are predominantly found in this region, with historical roots in areas around the Ural Mountains and adjacent territories. These datasets can be integrated for multidisciplinary purposes, allowing to explore human-environment interactions, migration patterns, and cultural evolution over time. Datasets are collected initially by the BEDLAN team <a href="https://bedlan.net/">https://bedlan.net/</a>  - a research group specialized in various disciplines - linguists, archaeologists, geneticists, and geographers. The data collection and mapmaking have grown beyond the initial stages (publications, applications, exhibitions), hence collaborative effort for data publishing is now crucial. As the data collections and mapmaking continue to evolve dynamically together with ongoing projects, the current repository will be updated accordingly.

opencc-by-4.0Mar 2022View details →

Phase coding of spatial representations in the human entorhinal cortex

Supporting Preprocessed Electrophysiology Data for the article titled "Phase coding of spatial representations in the human entorhinal cortex". Data were preprocessed and analyzed using Matlab. Data, after decompression, are organized hierarchically in folders and subfolders (two levels). Main folder names are composed as subjectID_date_EC_DATA_taskNo Sub-folders named as:  CHn_single-unit ID  [subjectID, date, task num] + [Electrode channel, and single-unit ID]  subjectID: (subject1, subject2) date: mmm-dd task: (1,2,3,4) Virtual environments (1: backyard, 2: Louvre, 3: Luxor, 4: desert) electrode Channel: CH1,CH2, CH3, CH4, CH5 single-unit ID: 0, 1, 2, 3, 4, 5, 6 For each channel and single-unit, the following 9 datasets were computed and saved. For instance, for the first electrode and first single-unit class (CH=1, cell ID= 0): CH1_Clu0.mat                                  -- Summary of firing features of this single-unit including firing rate, and grid score of the cell. (In Matlab mat format) CH1_Clu0_MeanPhaseMap.csv       -- Mean spike phase map relative to gamma-band LFP CH1_Clu0_Phase.csv                       -- Spike phase CH1_Clu0_spikeData.csv CH1_Clu0_spkT.csv                          -- Spike times in increental order CH1_Clu0_VarPhaseMap.csv           -- Map of the variance of spike times in increental order CH1_Clu0_xval.mat                          --  Summary of firing features of 50% of single-unit spikes (every other spike) for cross-validation purposes. (In Matlab mat format) CH1_Clu0_xyPos.csv                       -- X,Y coordinates of the avatar's position in 1 ms resolution. In other words, the path taken by the avatar sampled at 1 kHz. CH1_Clu0_XYspkT.csv                     -- X,Y coordinates of the avatar at moments of spikes. In other words, the location in space where a spike was fired by the putative neuron.

opencc-by-4.0Jan 2019View details →

Data to "Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection"

This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in: Maiello, G. &dagger;, Paulun, V. C. &dagger;, Klein, L. K. , & Fleming, R. W. (2018) Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection. i-Perception,10(1), 1–5. doi:10.1177/2041669519827608. &dagger;co-first authors

opencc-by-4.0Oct 2022View details →

Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean

Koptekin et al. (2022) "Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean", Current Biology <a href="https://doi.org/10.1016/j.cub.2022.11.034">https://doi.org/10.1016/j.cub.2022.11.034</a>

OpenNeuro44/100

The human Voice Areas: spatial organisation and inter-individual variability in temporal and extra-temporal cortices

Open the record for dataset details and reuse information.

openPDDLJan 2019View details →

opencc-by-4.0Apr 2020View details →

Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC tonsil data-set

53 marker MELC Run in human tonsil. Each image depicts the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies. Images contain 2048 x 2048 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.

opencc-by-4.0Aug 2022View details →

Dataset for the article "Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion" by Roman Barth and Haitham Shaban

The data set comprises all raw microscopy images and DFCC analyses as presented in  Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion by Roman Barth and Haitham Shaban, published in Nucleus (https://doi.org/10.1080/19491034.2022.2088988) There are two folders for RNAPII and DNA each, one for the raw images and one for the processed DFCC data, supplied as .mat files. Every folder contains three sub-folders containing the data for the conditions: +Serum, -Serum, and +DRB.

opencc-by-4.0Dec 2017View details →

Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"

Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1]. [1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. European Journal of Neuroscience, https://doi.org/10.1111/ejn.13981.

opencc-by-4.0Oct 2021View details →

Vectra Polatis image of human colorectal cancer (CRC1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.

Two 4 µm thick serial sections were cut from CRC1 FFPE block using a microtome. The first slide was dewaxed and rehydrated before carrying out HIER with Antigen Retrieval Reagent-Basic (R&D Systems). The tissue was then blocked and incubated with the anti-CD3 antibody (Dako, Supplementary Table 2) followed by horseradish peroxidase (HRP) conjugated anti-rabbit antibody (Dako) and stained with 3,3' diaminobenzidine (DAB) substrate (Abcam) and haematoxylin. Areas with CD3+ infiltration in the proximity of the tumour invasive margin were identified by a clinical pathologist (M. R-J.) The second slide was stained with a panel of six antibodies (CD8, PD1, Ki67, PDL1, CD68, GzB, Supplementary Table 2), Opal fluorophores and 4’,6-diamidino-2-phenylindole (DAPI) on a Ventana Discovery Ultra automated staining platform (Roche). Expected expression and cellular localisation of each marker as well as fluorophore brightness were used to minimise fluorescence spillage upon antibody-Opal pairing. Following a one-hour incubation at a 60°C, the slide was subjected to an automated staining protocol on an autostainer. The protocol involved deparaffinisation (EZ-Prep solution, Roche), HIER (DISC. CC1 solution, Roche) and seven sequential rounds of: one hour incubation with the primary antibody, 12 minutes incubation with the HRP-conjugated secondary antibody (DISC. Omnimap anti-Ms HRP RUO or DISC. Omnimap anti-Rb HRP RUO, Roche) and 16 minute incubation with the Opal reactive fluorophore (Akoya Biosciences). For the last round of staining, the slide was incubated with Opal TSA-DIG reagent (Akoya Biosciences) for 12 minutes followed by Opal 780 reactive fluorophore for our hour (Akoya Biosciences). A denaturation step (100°C for 8 minutes) was introduced between each staining round in order to remove the primary and secondary antibodies from the previous cycle without disrupting the fluorescent signal. The slide was counterstained with DAPI (Akoya Biosciences) and coverslipped using ProLong Gold antifade mounting media (Thermo Fisher Scientific). The Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences) was used to scan the labelled slide. Six fields of view, within the area selected by the pathologist, were scanned at 20x and 40x magnification using appropriate exposure times and loaded into inForm{Kramer, 2018 #23} for spectral unmixing and autofluorescence isolation using the spectral libraries. After spectral unmixing and merging of six 20x fields of view for a total of >5mm2 ROI (Table 2), one single-tiff image was extracted for each marker and its intensity was rescaled from 0 to 1 with custom R scripts.

opencc-by-4.0Jul 2023View details →

Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes

Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles: <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> The spatial reference system is EPSG 25830.

opencc-by-4.0Jul 2022View details →

HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020

The monthly High spatial resolution human Thermal Index Collection (HiTIC-Monthly) includes near-surface air temperature (SAT) and 11 commonly used human-perceived temperature indices: indoor Apparent Temperature (ATin), outdoor shaded Apparent Temperature (ATout), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is stacked by year, and each stack is composed of 12 monthly images in the NetCDF format. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”. If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via <a href="mailto:zhangh573@mail2.sysu.edu.cn">zhangh573@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:yqzhao@link.cuhk.edu.hk">zhaoyq66@mail.sysu.edu.cn</a>. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in: Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Cong, J., Zeng, Z., Gui, K., Li, J., Chan, T. O., Li, X., Wu, S., Wang, P., and Wang, X.: HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020, Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, 2023.

opencc-by-4.0Aug 2023View details →

EEG recordings during resting-state and the maintenance periods of a spatial working memory task in humans

Scripts used to analyze data for the manuscript submitted for publication in EJN Script_Curve_Fitting_HBM.rtf Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com  We therefore considered this continuous change in power as an extraneous variable yk(x) impacting the measured power spectrum Pow(Ek), and modeled it with a binomial equation that best fit the data, where the coefficients in pi are in descending powers, and the length of p is (n+1), k is trial number (k = 1 to 10): yk(x) = p1 . x2 + p2 . x + p3 In order to statistically compare the topographies between the trials with perfect recall and the trials with failed recall, we subtracted this variable from the mean spectral topographies of each subject and for each electrode by first producing the mean spectral curves of each maintenance trial in the theta and alpha frequency bands, taking into account the IAF, and then calculating the coefficients (p1, p2 and p3) of the binomial equation using the Matlab function polyfit.m. Once the coefficients were determined, this estimate was subtracted from each power spectrum matrix using the following formula: PowFit(Ek) = Pow (Ek) – yk(x)   Script_Perf_Fail_EEG_Power_Spec_HBM.rtf Dr. Hadj Meziane: hbmeziane@gmail.com This script calculates EEG power spectra then compares perf and fail conditions, then plots brain topographies with statical results   Script_Perf_Fail_EEG_Sources_Spec_HBM.rtf Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com This script compares EEG source spectra then compares Perf vs. Fail conditions then plot statistical results (significant voxels) on MRI volume

opencc-by-4.0Feb 2024View details →

Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy

Data associated with Chen, Nieman, Spurrell et al "Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy" Nature Immunology, 2024.  (1) Multiplex RNA scope. Processed cell-level data from 3 datasets, separated into manually selected "Stem Immunity" regions and "Tumor" regions, in separate files.  (2) GeoMx. Unprocessed GeoMex data, with standard fields provided by Nanostring software.  (3) MERFISH. Transcript-level information, including CellPose and Baysor segmentation.  (4) Co-registered multispectral RNA and protein images. Processed cell-level data from 46 patients. Patient-level meta data information is available in supplementary table 1, tab B_Patient_Metadata. Fluorophore channel information is Figure 1a.  Paper: <a title="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2" href="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2">https://www.nature.com/articles/s41590-024-01792-2</a>

opencc-by-4.0Dec 2016View details →

Fig. 3. Spatial 2D in Nir Raman Scattering For The Study Of Biochemical Features Of The Human Skin Epidermis And A Skin Surface Micro-Mapping In Vitro

Fig. 3. Spatial 2D image of the human epidermis surface: A) an optical image; B) mapping scheme; C) micro–Raman signal intensity map.

Figure 6 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 6. Estimated area of Disenchantment Bay (km2) containing three different ice cover types: scattered (1–3 tenths), intermediate (4–6 tenths), and dense (7–10 tenths), and all types combined (i.e., ice-covered area [ICA]) from 3 May to 4 August 2002. Estimates of ice cover were averaged within grid cells (when n> 1) and the areas of cells with each type of ice cover were summed (Jansen et al. 2006)) and then scaled upward (proportionately) based on the percent of the study area that was sampled on a given day.

Figure 7 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 7. Patterns of ship movement and visitation time in Disenchantment Bay, Alaska, in 2002 (n = 56 cruise ships of 105 total during study). Shading of cells represents the cumulative time that visiting ships spent within that cell for each of three months: May, June, and July (including early August). Four distinct shades, from gray to black, reflect increasing residence: <5 min, 5–10 min, 10–20 min,>20 min, respectively. Refer to Figure 1 for geographical points and scale.

Figure 5 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 5. Standardized resource selection coefficient by harbor seals for ice cover class in Disenchantment Bay, Alaska, 3 May to 4 August 2002, for the Bernoulli part of the P1B model (i.e., for the cell-based abundance of seals). The solid circles and lines are for all seals, and the open circles and dashed lines are for mother-pup pairs. Confidence intervals (95%) are shown by the vertical bars. The thin horizontal line represents equal selection for all ice cover classes.

Figure 4 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 4. Standardized resource selection coefficient by harbor seals for ice cover class in Disenchantment Bay, Alaska, 3 May to 4 August 2002, for the Bernoulli part of the P1B model (i.e., for the cell-based spatial distribution of seals). The solid circles and lines are for all seals, and the open circles and dashed lines are for mother-pup pairs. Confidence intervals (95%) are shown by the vertical bars. The thin horizontal line represents equal selection for all ice cover classes.

Figure 3 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 3. Spatial distribution of harbor seals, ice cover zones, and maximum penetration of cruise ships on the day of surveys () and the previous day () in Disenchantment Bay, Alaska, on (A) 6 May, (B) 16 May, (C) 31 May, (D) 20 June, (E) 18 July, and (F) 4 August 2002 (figures for all 18 survey dates are available as supplemental material online). The time of day that ships reached their maximum penetration appears near the location symbol. The range of seal counts summed per grid cell is shown in three levels: small dot (<5 seals), medium dot (5–20 seals), and large dot (>20 seals). A small, overlying white dot indicates the presence of at least one mother-pup pair within that grid cell. Ice cover is represented by a gradient in cell-color shading: light gray (scattered), medium gray (intermediate), dark gray (dense). For this graphic, if cells with no ice data were bounded on three sides by cells with ice measures, the average of neighboring cells was used as an estimate. Refer to Figure 1 for geographical points and scale.

Figure 2 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 2. Counts of all seals and pups along video sampling transects (relative abundance) in Disenchantment Bay, May to August 2002. Raw counts for all seals are shown as solid circles, and raw counts for pups are shown as open circles. The thick solid curve is the fitted GAM model for all seals, with the 95% prediction intervals shown by the thinner solid lines. The thick dashed curve is the fitted GAM model for pups, with the 95% prediction intervals shown by the thinner dashed lines.