Find research datasets worth reusing

Composition variations in Cu(In,Ga)(S,Se)2 solar cells: not a gradient, but an interlaced network of two phases

Abstract Record efficiency in chalcopyrite-based solar cells Cu(In,Ga)(S,Se)2 is achieved using a gallium gradient to increase the band gap of the absorber towards the back side. Although this structure has successfully reduced recombination at the back contact, we demonstrate that in industrial absorbers grown in the pilot line of Avancis, the back part is a source of non-radiative recombination. Depth-resolved photoluminescence (PL) measurements reveal two main radiative recombination paths at 1.04 eV and 1.5-1.6 eV, attributed to two phases of low and high band gap material, respectively. Instead of a continuous change in the band gap throughout the thickness of the absorber, we propose a model where discrete band gap phases interlace, creating an apparent gradient. Cathodoluminescence and Raman scattering spectroscopy confirm this result. Additionally, deep defects associated to the high gap phase reduce the absorber performance. Etching away the back part of the absorber leads to an increase of one order of magnitude in the PL intensity, i.e., 60 meV in quasi Fermi level splitting. Non-radiative voltage losses correlate linearly with the relative contribution of the high energy PL peak, suggesting that reducing the high gap phase could increase the open circuit voltage by up to 180 mV.

Application of deep neural networks to reconstruct coastal water quality data

In this study ordinary and new integrated deep neural networks were developed for the reconstruction of measured specific conductance (SC) data. Five stations of USGS in the Gulf of Mexico were considered as case study.

opencc-by-4.0May 2023View details →

IS network seismograms from the Mw7.8 and Mw7.5 2023 Kahramanmaraş earthquakes

M7.?.tar.gz : TAR+GZIP files containing 10 minutes of seismic data from the IS network recording the 2023 Kahramanmaraş earthquake doublet ????_??.tar.gz: TAR+GZIP files of selected IS stations in the Northern Dead Sea basin between 01/2021 to 03/2023.  local_eq.tar.gz: TAR+GZIP files of IS stations in the Jordan Valley containing local earthquake data

Benchmarking results for the Open Biomedical Network Benchmark

Aggregated benchmarking results for OBNB <div> </div>

Data for "Monte Carlo samplers for efficient network inference"

This directory contains data corresponding to all figures in the publication "Monte Carlo samplers for efficient network inference" in PLOS Computational Biology by Z. Kilic et al.

opencc-zeroJun 2023View details →

Physics-driven universal twin-image removal network for digital in-line holographic microscopy - dataset

Dataset (Matlab files) containing holograms and reference reconstructions employed in: M. Rogalski, P. Arcab, L. Stanaszek, V. Micó, C. Zuo and M. Trusiak, "Physics-driven universal twin-image removal network for digital in-line holographic microscopy", Submitted 2023 This dataset should be used together with the codes present at: https://github.com/MRogalski96/UTIRnet

Simulated_bending_spectras_and_NN_network

This folder is used as a supplementary material for article "Fiber shape sensing using Long Period Fiber Grating and Machine Learning Numerical analysis". It comprises first matlab generated data in the form of fiber random bending profiles and their associated LPG spectral transmission, as calculated with coupled-mode-theory. Then, the data has been used to train a keras NN model, that is also given in the folder.

The surgical instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention

In the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention, we collected and annotated the surgical instrument dataset.

Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics

Results and code to replicate analysis in "Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics" by Toneyan and Koo.

opencc-by-4.0Apr 2022View details →

(raw dataset) "Spatial biology of Ising-like synthetic genetic networks"

This is the raw data for the manuscript Simpson et al "Spatial biology of Ising-like synthetic genetic networks"

Data for Objective identification of pressure wave events from networks of 1-Hz, high-precision sensors

Data from pressure sensors assembled by Matthew Miller which consist of either a Bosch BMP388 or Bosch BME280 Adafruit breakout board connected to a Raspberry Pi Zero W single-board computer used to log the data. Data are recorded at 1-second intervals. The data are stored in .csv files: one for each day for each sensor. Sensors were placed in networks in the Toronto, ON, Canada, New York, NY, USA, and Raleigh, NC, USA metro areas. Code for processing these data can be found at https://doi.org/10.5281/zenodo.8087843.

opencc-by-4.0Feb 2023View details →

Colombia Coauthorship networks divided by openalex lvl 0 concepts

Graphs are uploaded in gml format, which can be easily imported by networkx, gephi and neo4j. The undirected graphs are constructed from openalex database (last update Dic 2022), where nodes are authors and edges specify whether or not two nodes coauthored at least one paper. We avoided papers with more than 10 authors since they are very scarse and could affect the posterior analysis of the networks. The attributes of the nodes consist of the list of used words for each author and its frequency and all the concepts (of every level) the papers of the author are labeld. The attributes of the edges only contains the number of papers published between two authors. For more information about openalex concepts, visit https://docs.openalex.org/api-entities/concepts

PMX NN Repair: Datasets, Networks and Virtual Machine

Supplementary Material for the Paper "Can we trust neural networks in pharmacometrics? Yes, wec an, but we need guarantees!" by David Boetius, Dominic Stefan Bräm, Marc Pfister, Stefan Leue, and Gilbert Koch.

Data for "A multinode quantum network over a metropolitan area"

This dataset is for the research article "A multinode quantum network over a metropolitan area".

Dataset - On the performance of online adaptation of robots controlled by nanowire networks

Dataset of the experiments considered in the article in the title

opencc-by-4.0Dec 2022View details →

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

This is the online data repository accompanying the following manuscript: Changes in patterns of age-related network connectivity are associated with risk for schizophrenia Roberta Passiatorea,b,c, Linda A. Antonuccia, Thomas P. DeRamusb, Leonardo Faziod, Giuseppe Stolfaa, Leonardo Sportellia,e, Gianluca C. Kikidisa,e, Giuseppe Blasia,f, Qiang Chene, Juergen Dukartc,g, Aaron L. Goldmane, Venkata S. Mattaye,h, Teresa Popolizioi, Antonio Rampinoa,f, Fabio Sambataroj, Pierluigi Selvaggia,f, William Ulriche, Apulian Network on Risk for Psychosisa,k,l,m,n,o, Daniel R. Weinbergere,h,p,q,r, Alessandro Bertolinoa,f1, Vince D. Calhounb1, Giulio Pergolaa,e,p1 a Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124 Bari, Italy b Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303 Atlanta, GA c Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428 Jülich, Germany d Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, 70010 Casamassima, Italy e Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, MD f Psychiatric Unit, University Hospital, 70124 Bari, Italy g Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany h Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287 Baltimore, MD i Neuroradiology Unit, Scientific Institute for Research, Hospitalization and Health Care, Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Foggia, Italy j Section of Psychiatry, Department of Neuroscience, University of Padova, 35121 Padova, Italy k Department of Mental Health, Azienda Sanitaria Locale Foggia, 71121 Foggia, Italy l Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy m Department of Mental Health, Azienda Sanitaria Locale Barletta-Andria-Trani, 76123 Andria, Italy n Department of Mental Health, Azienda Sanitaria Locale Bari, 70132 Bari, Italy o Department of Mental Health, Azienda Sanitaria Locale Brindisi, 72100 Brindisi, Italy p Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205 Baltimore, MD q Department of Neuroscience, Johns Hopkins University School of Medicine, 21287 Baltimore, MD r Department of Genetic Medicine, Johns Hopkins University School of Medicine, 21287 Baltimore, MD   Members of the Apulian Network on Risk for Psychosis include Ileana Andriola, Lucia Mare, Nicolò Parente, Alessandra Raio, Veronica D. Toro, (Department of Translational Biomedicine and Neuroscience – University of Bari Aldo Moro, Bari, IT), Mario Altamura, Marimar Castrigno, Melania Difino (Department of Mental Health, ASL Foggia, Foggia, IT; Department of Clinical and Experimental Medicine, University of Foggia, Foggia, IT), Flora Brudaglio, Domenico S. Savino, Rossana Vista (Department of Mental Health, ASL Barletta-Andria-Trani, Andria, IT), Angela Carofiglio, Barbara Gelao, Marina Mancini (Department of Mental Health, ASL Bari, Bari, IT), Alessandro Saponaro, Anna Manzari, Domenico Suma (Department of Mental Health, ASL Brindisi, Brindisi, IT). Abstract Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single- session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age-stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlate with genetic risk for SCZ and are detectable with MRI in young participants. DOI: 10.1073/pnas.2221533120   Files: - NeuroMark_1.0_template  <ol> <li>NeuroMark_1.0.nii:  4D Nifti containing the 100 Independent Components (ICs) spatial maps that was used for individual IC extraction based on Du Y, Fu Z, Sui J, Gao S, Xing Y, Lin D, Salman M, Abrol A, Rahaman MA, Chen J, Hong LE, Kochunov P, Osuch EA, Calhoun VD; Alzheimer's Disease Neuroimaging Initiative. NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. Neuroimage Clin. 2020;28:102375. doi: 10.1016/j.nicl.2020.102375. Epub 2020 Aug 11. PMID: 32961402; PMCID: PMC7509081.</li> <li>NeuroMark_1.0_Label.doc: document containing labels for each IC extracted including brain regions, peak coordinates of ICs, corresponding functional network in which ICs are arranged, and the corresponding image number in the NeuroMark_1.0.nii file. NOTE</li> </ol> - AveragedMatrix <ol> <li>vectorized_FNC_LIBD.RData, vectorized_FNC_UNIBA1.RData, vectorized_FNC_UNIBA1.RData:</li> <li>FNC_all_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects averaged across groups, sessions and cohorts</li> <li>FNC_all_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during resting state fMRI averaged across groups and cohorts</li> <li>FNC_all_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during encoding (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li>FNC_all_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during retrieval (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li>FNC_all_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during working memory (2-Back) fMRI averaged across groups and cohorts</li> <li>FNC_all_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix across subjects during emotion recognition (Faces/FMT) fMRI averaged across groups and cohorts</li> <li>FNC_yNC_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC averaged across sessions</li> <li>FNC_yNC_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during resting state fMRI</li> <li>FNC_yNC_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during encoding (RISE/PEAR) fMRI</li> <li>FNC_yNC_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during retrieval (RISE/PEAR) fMRI</li> <li>FNC_yNC_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during working memory (2-Back) fMRI</li> <li>FNC_yNC_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during emotion recognition (Faces/FMT) fMRI</li> <li>FN_oNC_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC averaged across sessions</li> <li>FNC_oNC_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during resting state fMRI</li> <li>FNC_oNC_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during encoding (RISE/PEAR) fMRI</li> <li>FNC_oNC_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during retrieval (RISE/PEAR) fMRI</li> <li>FNC_oNC_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during working memory (2-Back) fMRI</li> <li>FNC_oNC_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during emotion recognition (Faces/FMT) fMRI</li> <li>FNC_ySIB_averaged (UNIBA1, LIBD): mean FNC matrix on ySIB averaged across sessions</li> <li>FNC_ySIB_rest (LIBD): mean FNC matrix on ySIB during resting state fMRI</li> <li>FNC_ySIB_enc (UNIBA1, LIBD): mean FNC matrix on ySIB during encoding (PEAR) fMRI</li> <li>FNC_ySIB_ret (LIBD): mean FNC matrix on ySIB during retrieval (PEAR) fMRI</li> <li>FNC_ySIB_nback (UNIBA1, LIBD): mean FNC matrix on ySIB during working memory (2-Back) fMRI</li> <li>FNC_ySIB_emo (UNIBA1, LIBD): mean FNC matrix on ySIB during emotion recognition (Faces/FMT) fMRI</li> <li>FNC_oSIB_averaged (UNIBA1, LIBD): mean FNC matrix on oSIB averaged across sessions</li> <li>FNC_oSIB_rest (LIBD): mean FNC matrix on oSIB during resting state fMRI</li> <li>FNC_oSIB_enc (UNIBA1, LIBD): mean FNC matrix on oSIB during encoding (PEAR) fMRI</li> <li>FNC_oSIB_ret (LIBD): mean FNC matrix on oSIB during retrieval (PEAR) fMRI</li> <li>FNC_oSIB_nback (UNIBA1, LIBD): mean FNC matrix on oSIB during working memory (2-Back) fMRI</li> <li>FNC_oSIB_emo (LIBD): mean FNC matrix on oSIB during emotion recognition (FMT) fMRI</li> <li>FNC_yPSY_averaged (UNIBA2): mean FNC matrix on yPSY averaged across sessions</li> <li>FNC_yPSY_rest (UNIBA2): mean FNC matrix on yPSY during resting state fMRI</li> <li>FNC_yPSY_enc (UNIBA2): mean FNC matrix on yPSY during encoding (RISE) fMRI</li> <li>FNC_yPSY_ret (UNIBA2): mean FNC matrix on yPSY during retrieval (RISE) fMRI</li> <li>FNC_yPSY_nback (UNIBA2): mean FNC matrix on yPSY during working memory (2-Back) fMRI</li> <li>FNC_yPSY_emo (UNIBA2): mean FNC matrix on yPSY during emotion recognition (Faces) fMRI</li> <li>FNC_oSCZ_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ averaged across sessions</li> <li>FNC_oSCZ_rest (UNIBA2, LIBD): mean FNC matrix on oSCZ during resting state fMRI</li> <li>FNC_oSCZ_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during encoding (PEAR/RISE) fMRI</li> <li>FNC_oSCZ_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during retrieval (PEAR/RISE) fMRI</li> <li>FNC_oSCZ_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during working memory (2-Back) fMRI</li> <li>FNC_oSCZ_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during emotion recognition (Faces/FMT) fMRI</li> <li>PlotMatrixFNC.m (Script): code used to generate FNC plots. Example of FNC plots are included in Figure 1.</li> </ol> - Connectogram <ol> <li>MeanMatrix.mat: matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Matlab object.</li> <li>MeanMatrix.txt: matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Text file.</li> <li>ConnectogramNeuroMark_1.0.m (Script): code used to generate Figure 2. Graphical and statistical options are provided. Please refer to <a href="https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf">https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf</a> for further details.</li> </ol> - ResFNCAgeDifferences <ol> <li>data_example.RData (Data): example data frame for AgeDiff code.</li> <li>AgeDiff_real.R (Script): code to test age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li>AgeDiff_permutation.R (Script): code to permute age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li>ResFNCAgeDifference.RData: summary statistics obtained on UNIBA1, UNIBA2, LIBD cohorts included in the manuscript.</li> <li>boxplot_permutation_p_LIBD.pdf, boxplot_permutation_p_UNIBA1.pdf, boxplot_permutation_p_UNIBA2.pdf: error bars showing results obtained after permutation (10,000 iteration) across IC pairs.</li> </ol> - dataFNC <ol> <li>data_final.RData: de-identified individuals' aggregate FNC data for each cohort (UNIBA1, UNIBA2, LIBD, PNC, ABCD, UKB)</li> <li>plotData.R (Script): code used to plot FNC differences across groups (Figure 3). Code used to assess group differences - Wilcoxon Rank sum test - is included.</li> </ol> - ResFNCRiskDifferences <ol> <li>res_cerebellar_occipitoparietal_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjustedFDR<0.05) on the cerebellar-occipitoparietal FNC</li> <li>res_dorsolateralPFC_sensorimotor_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjustedFDR<0.05) on medial PFC-sensorimotor FNC</li> <li>res_medialPFC_sensorimotor_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjustedFDR<0.05) on dorsolateral PFC-sensorimotor FNC</li> </ol> - ResFNCxPRS <ol> <li>ResMetanalysisPRS_FNC_R.csv: summary statistics of metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups</li> <li>ResMetanalysisPRS_FNC_children_R.csv: summary statistics of  metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li>t2r.R (Script): code for t to R2 transform</li> <li>MetanalysisAveragedFNC_PRS.R (Script): code used perform metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> <li>MetanalysisAveragedFNC_PRS_children.R (Script): code used perform metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li>MetanalysisPRS_FNC_sessions_R.R (Script): code used perform metanalysis on the session-specific FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> </ol>   The present work includes data from UK Biobank (ID #41655), Adolescence Brain Cognitive Development (ABCD; ID #12036) Study, and Philadelphia Neurodevelopmental Cohort (PNC; ID #20998). Data are publicly accessible under approved request at the following links: - UK Biobank: <a href="https://www.ukbiobank.ac.uk/">https://www.ukbiobank.ac.uk/</a> - ABCD: <a href="https://abcdstudy.org/">https://abcdstudy.org/</a> - PNC: <a href="https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html">https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html</a> The ABCD data used in this report came from NIMH Data Archive DOI: https://doi.org/10.15154/1528793. Additional code for MRI data processing and Independent Component Analysis are publicly available at the following link: <a href="https://trendscenter.org/software/">https://trendscenter.org/software/</a>   Please, check https://doi.org/10.5281/zenodo.7853030 for future updates. For any data inquiries please contact: <ul> <li>Roberta Passiatore: Roberta.Passiatore@uniba.it</li> <li>Giulio Pergola: Giulio.Pergola@uniba.it - Giulio.Pergola@libd.org</li> <li>Vince D Calhoun: VCalhoun@gsu.edu</li> <li>Alessandro Bertolino: Alessandro.Bertolino@uniba.it</li> </ul>   Funding This work received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 798181 awarded to G.P. (PI) and A.B., D.R.W.; from the European Union funding within the MUR PNRR Extended Partnership initiative on Neuroscience and Neuropharmacology (Project no. PE00000006 CUP H93C22000660006 “MNESYS, A multiscale integrated approach to the study of the nervous system in health and disease”) to G.B., A.R., A.B., and G.P.; from the funding initiative Horizon Europe Seeds 2021 (Next Generation 8 EU–MUR D.M. 737/2021) for the project S68 CUP: H99J21017550006 to G.P. (PI) and L.A.A., G.B., A.R., A.B.; from the Apulian regional government for the project: “Early Identification of Psychosis Risk” to A.B.; from the Research Projects of National Relevance (PRIN) 2017 Prot. 2017K2NEF4 awarded to G.P.; from a Collaboration Grant from Exprivia Spa to G.P. under the ministerial decree D.M. n. 352/22 and A.B. under a collaboration agreement; from a Collaboration Grant from ITEL Telecomunicazioni Srl awarded to A.B.; from the 2021 Helmholtz Information and Data Science Academy Grant No. 12429 awarded to R.P. and J.D.; and from the NIH grant #R01MH118695 awarded to V.D.C. The collection of the MRI and genetic data for the LIBD cohort was supported by direct funding from the Intramural Research Program of the NIMH to the Clinical Brain Disorders Branch (PI: D.R.W., protocol 95-M-0150).

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Mexican cartels form a network of alliances and rivalries

Mexican cartels lose many members due to conflict with other cartels and arrests. Yet, despite their losses, cartels managed to increase violence for years. We address this puzzle by leveraging data on homicides, missing persons and arrests in Mexico for the past decade, along with information on cartel interactions. We model recruitment, state incapacitation, conflict and saturation as sources of cartel size variation. Results show that by 2022, cartels counted 160,000–185,000 units, becoming a top employer. Recruiting at least 350 people per week is essential to avoid their collapse due to aggregate losses. Furthermore, we show that increasing incapacitation would increase both homicides and cartel members. Conversely, reducing recruitment could substantially curtail violence and lower cartel size.

opencc-zeroJul 2023View details →

Spatialized metabolomic annotation combining MALDI imaging and molecular network

These data are linked to a publication "Spatialized metabolomic annotation combining MALDI imaging and molecular network" where we studied the in situ chemical diversity of fruits of the species Sextonia rubra (Mez.) Van der Werff (Lauraceae) using mass spectrometry imaging techniques and the annotation of molecular species detected by molecular networks using MetGem software. This repository contains: MS1 and MS2 raw data, ion mapping of the fruit according to different tissues, total molecular networks and a script for processing the acquired data to reproduce this approach.

opencc-by-4.0Aug 2023View details →

Dataset for: Star Photometry for DECaLS and SDSS Images Based on Convolutional Neural Networks

The dataset consists of two parts, the training dataset and the comparison dataset. The training dataset and the comparison dataset are each divided into three parts, namely the simulation dataset, the SDSS dataset and the DECaLs dataset. The simulation dataset is simulated by PhoSim software and the original size of the simulation data is 512x512 pixels. The SDSS dataset is from DR12 and the ObjId of the target as well as other parameters are given in the csv file.The DECaLs dataset is from DR9.