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12,346 results for “derivatives”
Dataset: Automatic Derivation of Vulnerability Models for Software Architectures
<p>Dataset for our publication "<em>Automatic Derivation of Vulnerability Models for Software Architectures</em>"</p>
Sea salt aerosol AOD at 550nm derived from MODIS
<p>This dataset uses MODIS MOD08_M3 and MYD08_M3 data to derive monthly sea salt aerosol optical depth, using the Angstrom exponent as a filtering criterion. This dataset was created for the publication "The representation of sea salt aerosols and their role in polar climate within CMIP6", JGR: Atmospheres, 2023. The methodology for building this dataset is described in the publication, and the scripts used to do so can be found on GitHub at https://github.com/rlapere/CMIP6_SSA_Paper. The time period covered is 2005-2014, with a monthly time step. Warning: this dataset is only valid for polar regions, where sea salt dominate coarse mode aerosols. outside of 60-90N and 60-90S this product should not be used.</p>
LiDAR-derived voxel-based leaf area density distribution data of urban trees
<p>This dataset includes 3D tree models and radiative transfer codes used in a paper entitled "Simulating the 3D distribution of absorbed shortwave radiation in a tree crown: comparison of simplified and Monte Carlo models" submitted to the JGR: Atmospheres.</p>
Chiral Phosphoric Acid Catalyzed Asymmetric Hydrolysis of Biaryl Oxazepines for the Synthesis of Axially Chiral Biaryl Amino Phenol Derivatives
<p>This folder /DFT_structures/ contains the DFT-optimized geometries (in .xyz format together with the gas-phase energy, E) accompanying the paper</p> <p>"Chiral Phosphoric Acid Catalyzed Asymmetric Hydrolysis of Biaryl Oxazepines for the Synthesis of Axially Chiral Biaryl Amino Phenol Derivatives"</p> <p>Where conformers occur, they are always named from the lowest Gibbs energy to the highest in ascending order from c1 (sometimes omitted), c2, c3, ...</p> <p>This folder contains the following sub-folders:</p> <p>- /irc_movies/ contains movies of the IRC analyses for the rotational barrier TSs;</p> <p>- /structures/ contains the DFT-optimized structures</p>
Dataset for modeling of binding modes of xanthine derivatives as allosteric inhibitors of MTHFD2
<p>Dataset with structures from docking and MD simulation trajectories, and exploration of selectivity, of xanthine derived compounds targeting the allosteric site of MTHFD2 versus the isoform MTHFD1. </p>
InSAR-derived horizontal velocities in a global reference frame - final output dataset and software codes
<p>The output dataset as described in the article in title, extracted from COMET LiCSAR dataset in March 2021.</p> <p>We also provide a snapshot of the python3 codes used to generate the output dataset.</p> <p>Contents and description of the dataset:</p> <p>uaz_values.csv<br> ==============<br> Contains u_az values and other relevant data in columns:<br> frame - ID of related frame (same as in frame_values.csv)<br> esd_master - reference acquisition epoch<br> epoch - date of acquisition epoch<br> daz_total_wrt_orbits - original extracted azimuth shift w.r.t. orbits<br> daz_cc_wrt_orbits - original extracted azimuth shift w.r.t. orbits from intensity cross-correlation (prior to spectral diversity)<br> drg_wrt_orbits - original extracted range shift w.r.t. orbits<br> orbits_precision - precision of applied orbits (P..precise, R..restituted)<br> version - orbits version<br> daz_iono_grad_mm - u_az from ionosphere propagation<br> tecs_A - estimated TECs at centre of hyphotetical burst A<br> tecs_A - estimated TECs at centre of hyphotetical burst B<br> daz_mm_notide - u_az after correction of solid-earth tides<br> daz_mm_notide_noiono_grad - u_az after correction of solid-earth tides and ionospheric gradient propagation<br> is_outlier_* - flag of outlier datapoint, as identified through Huber loss function (related to velocity estimates in frame_values.csv)</p> <p>frame_values.csv<br> ================<br> Contains along-track velocity estimates and other relevant data in columns:<br> frame - ID of related frame<br> master - reference acquisition epoch<br> center_lon - longitude coordinate of the frame centre<br> center_lat - latitude coordinate of the frame centre<br> heading - satellite heading angle (from the geograpic north)<br> azimuth_resolution - extracted azimuth pixel spacing (in metres)<br> range_resolution - extracted range pixel spacing (in metres)<br> avg_incidence_angle - average incidence angle of the frame<br> centre_range_m - approximate slant distance between the satellite and centre of the frame<br> centre_time - acquisition time (UTC) of centre of the frame at the reference epoch (appliable to other epochs)<br> s1AorB - flag of S-1A or B of the reference epoch<br> slope_plates_vel_azi_itrf2014 - along-track velocity estimated from ITRF2014 plate motion model<br> slope_daz_mm_mmyear - estimated along-track velocity from the original u_az values (in mm/year)<br> slope_daz_mm_notide_mmyear - estimated along-track velocity from u_az values after correction on solid-earth tides<br> slope_daz_mm_notide_noiono_grad_mmyear - estimated along-track velocity from u_az values after correction on solid-earth tides and ionosphere<br> intercept_* - corresponding intercept (in mm)<br> *_RMSE_selection - RMSE of outlier-free u_az data samples<br> *_count_selection - count of outlier-free u_az data samples used to estimate corresponding velocity<br> *_RMSE_mmy_full - RMSE from all u_az data samples applying corresponding velocity and intercept (in mm/year)</p> <p><br> decomposed_grid.csv<br> ===================<br> Contains decomposed velocities (in 250x250 km spacing grid) and other relevant data in columns:<br> count - count of frames used for the decomposition<br> opass - orbital pass codes of the input frames (D..descending, A..ascending)<br> centroid_lon - longitude coordinate of the grid cell centre<br> centroid_lat - latitude coordinate of the grid cell centre<br> VEL_N_noTI - northward velocity component from data corrected for solid-earth tides and ionosphere<br> VEL_E_noTI - eastward velocity component from data corrected for solid-earth tides and ionosphere<br> VEL_N_noT - northward velocity component from data corrected for solid-earth tides<br> VEL_E_noT - eastward velocity component from data corrected for solid-earth tides<br> ITRF_N - northward velocity component from averaged ITRF2014 plate motion model<br> ITRF_E - eastward velocity component from averaged ITRF2014 plate motion model<br> *RMSE_* - RMSE of corresponding data</p>
Serum starvation affects mitochondrial metabolism of Adipose-derived Stem/stromal Cells
<p>Dataset of the scientific publication 'Serum starvation affects mitochondrial metabolism of Adipose-derived Stem/stromal Cells '</p>
Dataset for "Myoglobin-derived iron causes wound enlargement and impaired regeneration in pressure injuries of muscle"
<p>Primary images, data and quantification spreadsheets for "Myoglobin-derived iron causes wound enlargement and impaired regeneration in pressure injuries of muscle"</p>
Melanoma-derived soluble mediators modulate neutrophil plasticity and the release of Neutrophil Extracellular Traps
<p>Background: polymorphonuclear neutrophils (PMNs) are main effector cells in the inflammatory responses. The significance of PMN infiltration in tumor microenvironment remains to be clarified. Metastatic melanoma is the most lethal type of skin cancer with an increasing incidence over the last decades. Few studies investigated the role of PMNs in human melanoma. The aim of this study was to investigate the role of PMNs and their mediators in human melanoma; (2) Methods: highly purified human PMNs from healthy donors were stimulated, in vitro, with conditioned media derived from the melanoma cell lines SKMEL28 and A375 (melanoma-CM) as well as from primary melanocytes as control. PMN functions (chemotaxis, survival, activation, cell tracking, morphology and NETs release) were evaluated; (3) Results: we found that the A375 cell line produced soluble factors able to promote PMN chemotaxis, survival, activation, and to modify PMN morphological changes and kinetic properties. Furthermore, both melanoma cell lines CM induced activation and the release of neutrophil extracellular traps (NETs) from PMNs. By contrast, primary melanocytes CM did not modify any PMN biological behavior. In addition, serum levels of myeloperoxidase (MPO), matrix metalloprotease-9 (MMP-9), CXCL8/IL-8, granulocyte and monocyte colony stimulating factor (GM-CSF) and NETs components were significantly increased in advanced melanoma patients compared to healthy controls; (4) Conclusions: melanoma cell lines produce soluble factors able to ‘educate’ PMNs towards an activated functional state. Metastatic melanoma patients display increased circulating levels of neutrophil-related mediators and NETs, suggesting that a neutrophil-related signature exists in metastatic melanoma patients. Further investigations are needed to better understand the role of these “tumor-educated neutrophils” in modifying melanoma cell behavior.</p>
Figure 2. Maximum likelihood phylogenetic tree topology derived from the combined 16S in Cryptic and widespread: a recipe for taxonomic misidentification in a freshwater crab species (Decapoda: Potamonautidae: Potamonautes sidneyi) as evident from species delimitation methods
Figure 2. Maximum likelihood phylogenetic tree topology derived from the combined 16S rRNA + COI sequence data, demonstrating the evolutionary relationships within the P. sidneyi s.l. species complex. Statistical support for nodes is provided as posterior probability values above nodes (> 0.95 PP) and bootstrap values below nodes (> 75%). An * or # denotes nodal relationships that were not supported (<0.95 PP/ <75%). Potamonautes sidneyi s.s. (clade 3) localities are marked with a dark blue triangle, while P. danielsi (clade 5) localities are marked by an orange square. The two new species, P. karooensis, (clade 2) and P. Ʋalles (clade 4), are marked by a light-blue circle and a green diamond, respectively. Specimens of P. barbarai are confined to clade 1.
Optimal control derived sensitivity-enhanced CA-CO mixing sequences for MAS solid-state NMR. Applications in sequential protein backbone assignments.
<p>Raw data pulse sequences and shapes for publication</p> <p><br> ## SEQUENCES ##<br> ./sequences_renamed<br> Pulse programs introduiced in this work. Previous pulse programs can be obtained from https://doi.org/10.5281/zenodo.7016441 or https://optimal-nmr.net/experiments.html</p> <p>## SHAPES ##<br> ./shapes_renamed<br> TROP shaped pulses for homonuclear 13C-13C homonuclear mixing discussed in this work. Heteronuclear shaped pulses can be obtained from https://doi.org/10.5281/zenodo.7016441 or https://optimal-nmr.net/sequences.html</p> <p>## RAW DATA ##<br> to decrease storage demands 3D-processed spectra were deleted and can be recovered using TopSpin command: ftnd 0<br> TopSpin NUS licence is required for processing of the NUS-sampled data. Transformed data can be obtained from authors on request.</p> <p># U-13C,15N,2H,1HN-SH3 sample at 55 kHz MAS<br> ./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/1<br> 1H saturation recovery</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/2<br> 1H hard pulse calibraton</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/3<br> 15N hard pulse calibration</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/4<br> conventional hNH with rampCP optimalization</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/5<br> sensitivity-enhahced se-hNH with TROP optimalization</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/11<br> 2D conventional hNH with rampCP </p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/12<br> 2D sensitivtiy-enhanced se-hNH with TROP </p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/13<br> CO hard pulse and hCO CP calibration</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/14<br> CA hard pulse and hCA CP calibration</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/27<br> CACO homoTROP power optimalization in sensitivity-enhanced se-hCACOHN experiment</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/28<br> CACO INEPT delay optimalization in conventional hcoCAcoHN experiment</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/31 to 38<br> comparison of hCANH experiment efficiency using combination of coherence transfer methods (rampCP, tmSPICE and TROP) for CAN and NN transfer (see experiment tiles)</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/41 to 45<br> comparison of hCONH experiment efficiency using combination of coherence transfer methods (rampCP, tmSPICE and TROP) for CON and NN transfer (see experiment tiles)</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/57 and 58<br> optimalization of selective CA and CO 90 and 180 pulse</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/59<br> COCA INEPT delay optimalization in conventional hCOcaHN experiment</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/60<br> COCA homoTROP power optimalization in sensitivity-enhanced se-hCOCAHN experiment</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/69<br> conventional 3D hCANH experiment using tmSPICE CAN transfer; with water suppression after first CP, 15% non-uniform sampled</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/70<br> sensitivity-enhanced 3D se-hCANH experiment using TROP transfer; with water suppression after first CP, 15% non-uniform sampled</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/72<br> conventional 3D hCONH experiment using tmSPICE CON transfer; with water suppression after first CP, 15% non-uniform sampled</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/73<br> sensitivity-enhanced 3D se-hCONH experiment using TROP transfer; with water suppression after first CP, 15% non-uniform sampled</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/74<br> sensitivity-enhanced 3D se-hCAcoNH experiment using TROP transfer and homoTROP; with water suppression after first CP, 15% non-uniform sampled</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/75<br> conventional se-hcoCAcoNH experiment using tmSPICE CN transfer and INEPT ‘out-and-back’ coCAco transfer; with water suppression before NH transfer, 15% non-uniform sampled</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/76<br> sensitivity-enhanced 3D se-hCOcaNH experiment using TROP transfer and homoTROP; with water suppression after first CP, 15% non-uniform sampled</p> <p>./JB.1p3mm.20221122.sh3.2H13C15N100pcbackexch/77<br> conventional 3D hCOcaNH experiment using tmSPICE CN transfer and INEPT ‘complete forward’ COca; with water suppression after first CP, 15% non-uniform sampled</p> <p><br> # U-13C,15N glycine at 16.5 kHz MAS<br> ./MAS_COCa_seTesting/62<br> sensitivity-enhanced se-hCACO experiment with homoTROP pulse without diagonal-phase control</p> <p>./MAS_COCa_seTesting/63<br> sensitivity-enhanced se-hCOCA experiment with homoTROP pulse without diagonal-phase control</p> <p><br> # U-13C,15N-fMLF at 20 kHz MAS<br> ./jb.20211102_fMLF_3.2/2<br> 13C direct excitation spectra</p> <p>./jb.20211102_fMLF_3.2/3<br> 13C and 1H hard-pulse calibration and HC CP optimalization</p> <p>./jb.20211102_fMLF_3.2/4<br> 15N hard-pulse calibration and HC CP optimalization</p> <p>./jb.20211102_fMLF_3.2/5<br> NCA rampCP optimalization</p> <p>./jb.20211102_fMLF_3.2/6<br> NCO rampCP optimalization</p> <p>./jb.20211102_fMLF_3.2/79<br> sensitivity-enhanced se-hNCACO with TROP pulses</p> <p>./jb.20211102_fMLF_3.2/83<br> sensitivity-enhanced se-hNCOCA with TROP pulses</p> <p>./jb.20211102_fMLF_3.2/87<br> conventional hNCACO with rampCP and DREAM mixing</p> <p>./jb.20211102_fMLF_3.2/87<br> conventional hNCOCA with rampCP and DREAM mixing</p>
Three datasets of global monthly gross primary productivity (GPP) during 2003-2018 derived from SIF, NIRv and LAI and their best-matching environmental factors
<p>As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous remote sensing vegetation indices (VIs) have participated in the estimation of global GPP. However, the relative performance of various VIs in estimating GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood.</p> <p>We used the Random Forest (RF) algorithm to identify the factors with the most powerful explanation of GPP and to explore the importance of these predictors. We trained six RF models to select features, i.e., two types of models (Plant Functional Type [PFT]-specific and universal) for each vegetation index (SIF, NIRv, and LAI). Each model comprised 100 decision trees, was sampled without replacement, and was trained using 70% of the data. Model performance was evaluated using out-of-bag (OOB) R-squared (R<sup>2</sup>) and root mean square error (RMSE) values. The predictor with the lowest importance score in the iteration was removed and the whole procedure was then repeated until only the vegetation index, CO<sub>2</sub>, and PFTs were left. The predictors used to estimate GPP were identified based on the performance curve of OOB R<sup>2</sup> and RMSE. The determination of the model is based on the principle that further reductions in the number of predictors would considerably reduce model performance, while increasing the number of predictors would not significantly improve model performance.</p> <p>Here we provide a set of high-spatial resolution (1/12°) global gridded products of monthly GPP for 2003-2018 generated for each vegetation index based on a generic model with an optimal configuration, i.e., an optimal combination of VI and other relevant variables using the RF algorithm. R<sup>2</sup> of three optimal VI-based GPP estimation models ranges from 0.84 to 0.85, and RMSE ranges from 1.51g C·m<sup>−2</sup>·d<sup>−1</sup> to 1.54g C·m<sup>−2</sup>·d<sup>−1</sup>. More information about the datasets can be found in Zhao and Zhu (2022) <strong><em>Remote Sensing</em></strong>.</p> <p><em>Zhao W, Zhu Z. Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity[J]. Remote Sensing, 2022, 14(24): 6316.</em></p>
Generation of human induced pluripotent stem cells-derived cortical neurons for high throughput imaging of neurite morphology and neuron maturation
<p>Figure 5: Whole cell patch clamping showed the differentiated neurons are functional.</p>
Generation of human induced pluripotent stem cells-derived cortical neurons for high throughput imaging of neurite morphology and neuron maturation
<p>Figure 3 - Neurite outgrowth dataset. Comparing neurite outgrowth at Day1 and 15 post-seeding cortical neural progenitors. </p>
BridgeDb: pathway identifier mapping database derived from Wikidata
<p>Release of a BridgeDb gene identifier mapping database between Wikidata and Ensembl.</p> <pre>[INFO]: Database finished. INFO: old database is Wikidata 1.0.0 (build: 20230506) INFO: new database is Wikidata 1.0.0 (build: 20230506) INFO: Number of ids in Wd (Wikidata): 153715 (unchanged) INFO: Number of ids in En (Ensembl): 153637 (unchanged) INFO: new size is 95 Mb (changed +0.0%) INFO: total number of identifiers is 307352 INFO: total number of mappings is 307430 </pre>
Short-Term Effects of Moderate Severity Disturbances on Forest Canopy Structure- Derived Data
<p>NEON plot-level derived tables used in this study. All the derived data were openly available from The National Science Foundation's National Ecological Observatory Network (NEON).</p> <ul> <li>01: R codes for 01) LiDAR-derived canopy metrics calculation; 02) Modelling</li> <li>02: Input ground survey data with removed duplicate rows (tree_rm_duplicates.csv); CSV files - NEON vegestructure data (disturbance_structural_diversity_data_2022_1109_den4.csv)</li> </ul> <p>All NEON’s Discrete return LiDAR point cloud (DP1.30003.001) (NEON 2021a) and Vegetation structure (DP1.10098.001) (NEON 2022) data are available on NEON data portal as follows: <a href="https://data.neonscience.org/data-products/DP1.30003.001">https://data.neonscience.org/data-products/DP1.30003.001</a> (<a href="https://doi.org/10.48443/6E8K-3343">https://doi.org/10.48443/6E8K-3343</a>), and <a href="https://data.neonscience.org/data-products/DP1.10098.001/RELEASE-2022">https://data.neonscience.org/data-products/DP1.10098.001/RELEASE-2022</a> (<a href="https://doi.org/10.48443/RE8N-TN87">https://doi.org/10.48443/RE8N-TN87</a>), respectively.</p> <p>R scripts for calculating canopy metrics (Hardiman et al. 2021) are available on Environmental Data Initiative (EDI): <a href="https://doi.org/10.6073/pasta/97f8092c10aecbc62aec12557ecb52bd">https://doi.org/10.6073/pasta/97f8092c10aecbc62aec12557ecb52bd</a></p> <p> </p> <p>Reference</p> <ul> <li>Hardiman, B.S., E.A. LaRue, J.R. Foster, R. Fahey, and J. Hatala Matthes. 2021. Forest structural diversity at NEON sites in the continuous USA that experienced recent moderate disturbance ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/97f8092c10aecbc62aec12557ecb52bd (Accessed 2023-05-24).</li> <li>NEON. 2021a. Discrete return LiDAR point cloud (DP1.30003.001). National Ecological Observatory Network (NEON). doi: <a href="https://doi.org/10.48443/6E8K-3343">https://doi.org/10.48443/6E8K-3343</a>.</li> <li>NEON. 2022. Vegetation structure (DP1.10098.001). National Ecological Observatory Network (NEON). doi: <a href="https://doi.org/10.48443/RE8N-TN87">https://doi.org/10.48443/RE8N-TN87</a>.</li> </ul>
Data for 'Josephson diode effect derived from short-range coherent coupling'
<p>An Igor file stores source data for the three figures in the main manuscript and the supplementary figures. The wave names are tied to file names of the raw data. The raw data for the three figures and the supplementary figures are compressed in 'raw data.zip'.</p>
Data and statistical analysis for: Response to Elexacaftor/Tezacaftor/Ivacaftor in intestinal organoids derived from patients with cystic fibrosis
<p>Data and code to reproduce all statistical analyses and figure in the manuscript "Response to Elexacaftor/Tezacaftor/Ivacaftor in intestinal organoids derived from patients with cystic fibrosis".</p> <p>The dataset is stored in the file `area_data.csv`, the columns are:</p> <ul> <li>`well` The ID of the well within the plate the measurement was taken</li> <li>`time` time from start of measurement</li> <li>`area` area of the organoids</li> <li>`patient` Anonymized ID of the patient</li> <li>`date` Date when the experiment was performed (date and patient ID identify the plate)</li> <li>`filename` The filename in raw data (not published) this plate was stored in. (uniquely identifies a plate)</li> <li>`mix` Identifies one od two variants of plate layouts used</li> <li>`replicate` ID of technical replicate (two technical replicates were done for each condition on the same plate)</li> <li>`type` The main condition - one of "TEZ/IVA", "ELX/TEZ/IVA", "FskOnly" (Control, only forskolin)</li> <li>`fsk_concentration` concentration of forskolin (μM)</li> </ul> <p>The file "organoids.Rmd" reproduces the analyses, other files are supporting to let the main analysis run.</p> <p>The analysis is written in R markdown.</p> <p> </p>
LD Scores for the Admixed American, African, East Asian, European and South-East Asian ancestries derived from 1000 G
<p>LD Scores for the Admixed American, African, East Asian, European and South-East Asian ancestries derived from 1000 G.</p> <p>These files are needed to run the <a href="https://github.com/bulik/ldsc">LD Score regression</a> method embeded in the <a href="https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline">JASS pipeline</a></p> <p>Full detail on how these datasets have been derived are available here: https://www.biorxiv.org/content/10.1101/2023.06.23.546248v1</p> <p> </p>
AMYPAD PNHS - Integrated dataset (Harmonized and Derived) v202306
<p><strong>Description:</strong></p> <p>The Amyloid Imaging to Prevent Alzheimer’s Disease (AMYPAD) Prognostic and Natural History Study (PNHS) is an open-label, prospective, multicentre, cohort study linked to a variety of European Parent Cohorts.</p> <p>AMYPAD PNHS aims to improve disease modelling efforts and individualized risk stratification within the context of Alzheimer's disease, by the additional collection of amyloid burden, measured by positron emission tomography (PET) imaging.</p> <p>The current version of the AMYPAD PNHS dataset integrates information from 10 Parent Cohorts: ALFA+, AMYPAD DPMS, DELCODE, EMIF-AD (60++), EMIF-AD (90+), EPAD LCS, FACEHBI, FPACK, Microbiota, and UCL-2010-412. This dataset includes a total of 3368 participants. Of them, 1620 underwent a baseline amyloid PET that includes the visual read and the Centiloid quantification (1476 subjects), among other metrics. Moreover, 888 participants have (at least) one follow-up PET scan, 763 of them with Centiloid quantification. The participant's clinical outcomes (e.g., cognition), disease (imaging) biomarkers, risk factors (e.g., genetics and environmental), and other relevant variables are included in the dataset.</p> <p><strong>Data Access Request:</strong></p> <p>Those researchers interested in using the integrated AMYPAD PNHS dataset can request access to the imaging, clinical, and biomarker data for scientific research investigation and/or educational activities. The application can be performed via the <a href="https://fair.addi.ad-datainitiative.org/">FAIR Data Service</a> of the Alzheimer’s Disease Data Initiative (<a href="https://www.alzheimersdata.org/">ADDI</a>).</p> <p><em>Direct link: </em><a href="https://fair.addi.ad-datainitiative.org/#/data/datasets/amypad_pnhs__harmonised_and_derived__v202306">https://fair.addi.ad-datainitiative.org/#/data/datasets/amypad_pnhs__harmonised_and_derived__v202306</a></p> <p>Further details can be found in the <a href="https://doi.org/10.5281/zenodo.7962925">AMYPAD PNHS Data Access Request</a> documentation.</p> <p><strong>Note:</strong></p> <p>This version of the dataset includes only the 'harmonised' and 'derived' set of variables (further details can be found in the <a href="https://doi.org/10.5281/zenodo.7962924">AMYPAD PNHS Data Access Request</a> documentation.</p>
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.