Find research datasets worth reusing

Fig. 6 in Research Reconciling fossils with phylogenies reveals the origin and macroevolutionary processes explaining the global cycad biodiversity

Fig. 6 Reconstruction of the latitudinal span of cycads during geological times. This estimate has been obtained using the method of Silvestro et al. (2018b) as implemented in Zhang et al. (2022). Gray polygon represents the reconstructed span, black dots represent the paleolatitude and age of fossil tips, while black triangles show the latitude of extant species. C, Carboniferous; J, Jurassic; K, Cretaceous; N, Neogene; P, Permian; Pg, Paleogene; Tr, Triassic.

Fig. 5 in Research Reconciling fossils with phylogenies reveals the origin and macroevolutionary processes explaining the global cycad biodiversity

Fig. 5 Biogeographic processes explaining the global distribution pattern of cycads. The number of dispersal events into a region and out of a region as well as the number of local extinctions (extirpations) are compared between analyses including fossils (a, b) and analyses excluding fossils (c, d). Area names: WP, West Palearctic; EP, East Palearctic; WN, West Nearctic; EN, East Nearctic; CA, Central America; WI, Caribbean Islands; SA, South America; AF, Africa; IN, India; WA, Southeast Asia; AU, Australasia; GR, Greenland; AN, Antarctica.

2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection

Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient. Two main challenges in medical image processing has been generalization and personalization. Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization. The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.

Processed lung Nuclear-Seq data for healthy donors

This repository includes the lung Nuclear-Seq data for healthy donors

Example-Public-administration-Process

Process modeling citizen's income and class trip in PICTURE Tool and modeling OZG (German Online Access Law (“Onlinezugangsgesetz) application process one-for-all (EfA)

opencc-by-4.0Dec 2022View details →

Genetic impacts on DNA methylation help elucidate regulatory genomic processes

Background: Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk. Results: We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2,358 blood samples from three UK cohorts. Methylation levels at 34.2% of CpGs are affected by SNPs, and 98% of effects are cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both SNPs and CpGs with meQTLs are overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identify 1,520 co-localisations across 1,325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such as USP1 and DOCK7 (total cholesterol levels), and ICOSLG (inflammatory bowel disease). Enrichment analysis of meQTLs and integration with expression QTLs give insights into mechanisms underlying cis-meQTLs, (e.g. through disruption of transcription factor binding sites for CTCF and SMC3), and trans-meQTLs (e.g. through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites). Conclusions: Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. The MeQTL EPIC Database and viewer are available online at <a href="https://epicmeqtl.kcl.ac.uk/">https://epicmeqtl.kcl.ac.uk/</a>.

Memory related processing is the primary driver of human hippocampal theta oscillations

This dataset contains intra-cranial EEG (iEEG) recordings related to the paper: "Memory-related processing is the primary driver of human hippocampal theta oscillations" by Seger et al. The study investigates the role of hippocampal theta oscillations in the human brain during navigation (during which externally oriented sensorimotor processing occurs) and during mental simulation of navigation (when no externally oriented sensorimotor processing is present). The iEEG recordings underwent the initial preprocessing steps as described in the paper and were subsequently downsampled to 250Hz for the purpose of this dataset. The files in this dataset were saved as .MAT files.  The dataset includes the following zip files: “Seger_et_al_2023_iEEG_data.zip"    Description of data structure: "electrode.info" contains information about subject and electrode contact - electrode.info.subject_ID : participant ID - electrode.info.elec_ID : channel ID  - electrode.info.anat_loc : anatomical localization (see paper for methods) - electrode.info.hemi : hemisphere of electrode  - electrode.info.env_version : paradigm version  "electrode.data" contains all trial level data for this electrode (** Please note that all trial level iEEG data contains a 2 second (500 sample) buffer flanking both ends.) "electrode.data.nav_and_sim_trials" contains all the trial level data for the navigation with mental simulation trials  -electrode.data.nav_and_sim_trials.trial_info : information about the navigated / mentally simulated routes (ex. Start and end store, catch trial condition) -electrode.data.nav_and_sim_trials.nav_eeg : raw eeg for each navigation trial -electrode.data.nav_and_sim_trials.nav_eeg_tVect : timeVector corresponding to raw eeg data in ms (t=0 is when the trial begins t(end) - 2000 ms is when trial ends).  -electrode.data.nav_and_sim_trials.nav_position : avatar XY position for all time points during navigation trials -electrode.data.nav_and_sim_trials.sim_eeg : raw eeg for each mental simulation trial -electrode.data.nav_and_sim_trials.sim_eeg_tVect : timeVector corresponding to raw eeg data in ms (t=0 is when the trial begins t(end) - 2000 ms is when trial ends).  "electrode.data.nav_practice_trials" contains all the trial level data for the navigation practice trials -electrode.data.nav_practice_trials.trial_info : information about the route navigated (ex. Start and end store) -electrode.data.nav_practice_trials.nav_eeg : raw eeg for each practice navigation trial -electrode.data.nav_practice_trials.nav_eeg_tVect : timeVector corresponding to raw eeg data in ms (t=0 is when the trial begins t(end) - 2000 ms is when trial ends).  -electrode.data.nav_practice_trials.nav_position : avatar XY position for all time points during practice navigation trials "electrode.data.storefront_pres_trials" contains all the trial level data for the storefront presentation trials -electrode.data.storefront_pres_trials.eeg : raw eeg for each storefront presentation -electrode.data.storefront_pres_trials.eeg_tVect : timeVector corresponding to raw eeg data in ms (t=0 is when the trial begins t(end) - 2000 ms is when trial ends).  "electrode.data.crosshair_pres_trials" contains all the trial level data for the crosshair presentation trials -electrode.data.crosshair_pres_trials.eeg : raw eeg for each crosshair presentation -electrode.data.crosshair_pres_trials.eeg_tVect : timeVector corresponding to raw eeg data in ms (t=0 is when the trial begins t(end) - 2000 ms is when trial ends).

Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: a graph signal processing study

Dataset for the publication: 'Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: a graph signal processing study' Rigoni et al. 2023, Clinical Neurophysiology, doi: <a href="https://doi.org/10.1016/j.clinph.2023.05.012">https://doi.org/10.1016/j.clinph.2023.05.012</a> Dataset description func_data.mat : source-reconstructed EEG traces stored in a Fieldtrip format (data for each subj) struct_data: consensus structural connectome ROIpatch : mesh used to plot Fig4 SC_surrogates (W0): 1000 degree-preserving surrogates of the structural connectome computed with the null_model_und_sign function of the Brani Connectivity Toolbox; W0 are used for broadcasting analyses, section 2.6.2 SC_surrogates_harmonics (U0) : network harmonics of the surrogate structural connectomes (W0); U0 are used for broadcasting analyses, section 2.6.2 BD_surr : Broadcasting-direction (BD) of the degree-preserving surrogates of the structural connectome W0; used to produce FigS3 (data for each subj) PHI : matrix of +1/-1 to generate the functional surrogates used to define significance of SDI, as described in section 2.6.3 data_GSP2_surr : SDI of the functional surrogates used to define significance of SDI, as described in section 2.6.3 (data for each subj)   Abbreviations: EEG= electroencephalography; SDI= structure-decoupling index

Supplementary Figure Legend S1: An overview of DNA samples included in the NGS based chimerism validation process and AlloSeq HCT library preparation workflow.

Supplementary Figure Legend S1: An overview of DNA samples included in the NGS based chimerism validation process and AlloSeq HCT library preparation workflow. S1a: An overview of clinical and artificial DNA included in the NGS based chimerism validation process (Clinical samples, Artificial construct samples and ASHI EMO Proficiency samples). S1b: Schematic presentation of time required for the NGS Chimerism assay. S1c. AlloSeq HCT library preparation workflow.

Raw and pre-processed dataset

Hybrid streamflow modelling using machine learning and multi-model combination Global Hydrological model outputs that have been retrieved from eartH2Observer Water Cycle Integrator (WCI) <a href="http://https://wci.earth2observe.eu/portal/?">data portal</a>. The data preprocessed in an effort to improve streamflow forecasts. It incorporates the outputs of three GHM (WaterGAP3, PCR-GLOBWB, and LISFLOOD), which are driven by the daily 0.5° WATCH Forcing. Data ERA-Interim (WFDEI) meteorological dataset. With a monthly temporal resolution, this dataset spans from 1979 to 2012. The <a href="https://github.com/HassanAli99/ADS_Final_Thesis">GitHub </a>repository contains comprehensive pre-processing instructions.

opencc-by-2.0Jul 2023View details →

Dataset, setups and scripts to numerically reproduce the breaching process

This dataset is part of the article: Numerical investigation of mode failures in submerged granular columns - E. P. Montellà, J. Chauchat, C. Bonamy, D. Weij, G. H. Keetels and T. J. Hsu. This file contains the numerical results obtained with SedFoam to model the breaching experiments of Weij (2020) (full thesis available at https://pure.tudelft.nl/ws/portalfiles/portal/84166231/2020_03_05_thesis_dweij.pdf ). The folder and files are set to reproduce the Experiments 8 and 16 in case the user wants to re-launch the numerical simulations. These simulations can be executed with ./Allrun. Two Python scripts are available for post-processing. The Python scripts require the fluidfoam package to be installed and it is freely available at https://github.com/fluiddyn/fluidfoam .

opencc-by-4.0Jul 2023View details →

Supplementary material for the study titled "Investigating ChatGPT's Potential in the Requirements Elicitation Process"

This supplementary material folder consists of ChatGPT-generated responses to 6 questions asked to elicit requirements for the development of Trustworthy AI. This also has a glossary of Trustworthy AI qualities and requirement quality attributes used to evaluate the responses consisting of Trustworthy AI requirements mentioned above.

opencc-by-4.0Jul 2023View details →

Processed data for Microdiversity of the Vaginal Microbiome is Associated with Preterm Birth.

Processed data for the manuscript Microdiversity of the Vaginal Microbiome is Associated with Preterm Birth.

Fig. 4 in Insights on the antiradical capacity and mechanism of phytocannabinoids: H-abstraction and electron transfer processes in physiological media and the influence of the acid-base equilibrium

Fig. 4. Gibbs free energies of the HOO• scavenging reaction of phytocannabinoids under physiological conditions.

Fig. 5 in Insights on the antiradical capacity and mechanism of phytocannabinoids: H-abstraction and electron transfer processes in physiological media and the influence of the acid-base equilibrium

Fig. 5. Localized TSs for fHAT mechanism of phytocannabinoids in the lipid-like environment. The Distances are in A.

opencc-by-4.0Jul 2023View details →

Processed ApRES displacement of internal reflectors at Helheim Glacier in August, 2016.

This is the processed ApRES product using dataset collected in August, 2016. A readme file is included to extract displacement time series at a given point.

dryad32/100

Data from: Evolutionary process of extremely twisted heteromorph ammonites from the Upper Cretaceous in Japan

The heteromorph ammonite Pravitoceras sigmoidale with extremely twisted retroversal hook and its probable ancestors, Didymoceras awajiense and D. morozumii, occur mainly in the Upper Cretaceous Izumi and Sotoizumi groups in southwest Japan. Their morphological characteristics were investigated in this study. Morphotypes I–II were recognized in D. morozumii. Morphotypes III–V were recognized in D. awajiense. Morphotypes VI–VIII were recognized in P. sigmoidale. Moreover, all morphotypes were recognized in both the dextral and sinistral forms of these three species. Based on the morphological and stratigraphic relationships, it is considered that the evolutionary process of these species is divided into three steps: (1) shift from morphotypes I–II to III; (2) increase in variation from morphotypes III to III–V; and (3) shift from morphotypes III–V to VI–VIII. By comparing these morphotypes based on the 'growing tube model' belonging to the moving frame analysis, it is seen that there are no significant gaps of coiling patterns in the evolutionary processes of this lineage. Meanwhile, morphotypes III–V of D. awajiense have a larger morphological variation than morphotypes I–II of D. morozumii and morphotypes VI–VIII of P. sigmoidale. These results, and the radiometric ages from previous studies, suggest that D. awajiense, with considerable variation between ancestor and descendant species, existed in a short interval. It seems that P. sigmoidale occurred after the continuous but rapid and drastic morphological evolution from D. morozumii via D. awajiense.

opencc-zeroAug 2023View details →

opencc-by-4.0Aug 2023View details →

Processed API-Bench Dataset Used In kNM-LM

This is the processed API-Bench dataset used in code completion, more details can be found in our paper "Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases".

opennotspecifiedSep 2021View details →

Fig. 59 in The scope of flavin-dependent reactions and processes in the model plant Arabidopsis thaliana

Fig. 59. Putative reaction catalyzed by HTH (HOTHEAD). Shown are the successive oxidations of the ω-hydroxyl group to the aldehyde and eventually, to the carboxy-group, leading to an α,ω-dicarboxylic acid. Members of the GMC oxidoreductases typically react readily with dioxygen to produce hydrogen peroxide.