Find research datasets worth reusing

opencc-zeroDec 2016View details →

FIGURE 1 in Scale ontogeny in the cardinalfish family Apogonidae

FIGURE 1. SEM images of adult transforming ctenoid scales of species representing selected apogonid genera: (a) Siphamia cephalotes CSIRO B 3505, 35.5 mm. (b) Apogon coccineus SAIAB 3325, 27.9 mm. (c) Pristiapogon kallopterus SAIAB 9989, 73.5 mm. (d) Jaydia novaeguineae SAIAB 96303, 88.9 mm. (e) Cheilodipterus novemstriatus SAIAB 69182, 32.9 mm. (f) Foa sp. SAIAB 18422, 52.0 mm. (g) Fowleria variegata SAIAB 87543, 38.8 mm. (h) Phaeoptyx conklini SAIAB 60098, 43.5 mm. (i) Holapogon maximus SAIAB 55280, 154.0 mm.

Images for "Nano-scale magnetic skyrmions in metallic films and multilayers: a new twist for spintronics"

Magnetic skyrmions are chiral quasiparticles that show promise for the transportation and storage of information. On a fundamental level, skyrmions are model systems for topologically protected spin textures and can be considered as the counterpart of topologically protected electronic states, emphasizing the role of topology in the classification of complex states of condensed matter. Recent impressive demonstrations of control of individual nanometer-scale skyrmions—including their creation, detection, manipulation and deletion—have raised expectations for their use in future spintronic devices, including magnetic memories and logic gates. From a materials perspective, it is remarkable that skyrmions can be stabilized in ultrathin transition metal films, such as Fe—one of the most abundant elements on earth—if these are in contact with materials that exhibit high spin-orbit coupling. At present, research in this field is focused on the development of transition-metal-based magnetic multilayer structures that support skyrmionic states at room temperature and allow for precise control of skyrmions by spin-polarized currents and external fields.

Figure 3. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834

Figure 3. - TimelineThis Gantt chart provides an estimate of the relative timing and duration for achieving each of the Aims.

Figure 1. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834

Figure 1. - First pass at a VPDRS static graphicFigure 1 corresponds to the first self-administered MDS-UPDRS question:1.7 SLEEP PROBLEMS.Over the past week, have you had trouble going to sleep at night or staying asleep through the night? Consider how rested you felt after waking up in the morning.0: Normal: No problems.1: Slight: Sleep problems are present but usually do not cause trouble getting a full night of sleep.2: Mild: Sleep problems usually cause some difficulties getting a full night of sleep.3: Moderate: Sleep problems cause a lot of difficulties getting a full night of sleep, but I still usually sleep for more than half the night.4: Severe: I usually do not sleep for most of the night."

Figure 2. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834

Figure 2. - Prototype for the mobile phone appThis screen shows a pre-release version of Node, which will support the VPDRS/UPDRS modules. Here we present a means by which a person administering a questionnaire can securely log into and manipulate patient information locally and through cloud services and lastly an example clinician-administered UPDRS question.

opencc-by-nd-4.0Jun 2015View details →

Assessment of dairy cow welfare in small-scale farming systems dataset

This database was created as preparatory work for the Scientific Opinion on the assessment of dairy cow welfare in small-scale farming systems (EFSA, 2015) to collect data for the description and the categorisation of European Small-Scale Dairy Farms (SSDF) based on size, farming system and husbandry practices and (ii) to analyse the feasibility in SSDF of animal-based measures usually used for intensive farming. The Scientific Opinion was necessary to address specific expectations of consumers on locally produced food and acceptable animal welfare conditions in the context of the EU Strategy for the protection and welfare of animals 2012-2015. The on-farm survey was run to collect data for welfare assessment covering Austria, France, Italy and Spain. A total of 124 farms with up to 75 cows were selected based on three criteria reflecting use of local resources or enrolment in a certification scheme: (1) the type of enterprise (ownership and workers), (2) the use of inputs in the production process, including  the  use  of  local  feed  and  local  breeds,  and  (3)  the  production  type (certification schemes). From 124 dairy farms visited 119 were considered as SSDF. Among the 119 farms included in the survey as non-conventional, some of them had a very small herd size (44 had less than 25 cows and one had only 10 cows) and some of them had more animals (19 farms had between 51 and 75 dairy cows). The database includes 53 continuous and categorical farm descriptor variables, 23 continuous and categorical risk-factor variables and 47 animal-based measures in small-scale farms. The final data model used was based on data collection at farm/herd level, pen level and animal level.

opencc-by-4.0Nov 2024View details →

Multi-scale footprinting

Data associated with the multi-scale footprinting project. (1) Tn5_NN_model.h5 Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model. (2) Tn5ModelTutorial.ipynb Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences. (3) hg38Tn5Bias.tar.gz, hg19Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, mm39_bias_v2.h5, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model. (4) dispModel.tar.gz Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting. (5) cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds Motif PWMs used in our study. (6) TFBS_model.h5 Pre-trained footprint-to-TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present. This is obsolete. For the best performance of TF binding prediction, please use our seq2PRINT-based TF binding prediction.  (7) clusterLabels.txt, clusterLabelsAllTFs.txt Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs. (8) BMMCTutorial.tar.gz Data needed for our R version tutorial. Content of this foder can be put into the /data/BMMCTutorial folder. (9) PBMC_bulk_ATAC_tutorial fragments files Files used by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. (10) PBMC_bulk_ATAC_tutorial example result TFBS bigwigs (Bcell_0_TFBS.bigwig, Bcell_1_TFBS.bigwig, Monocyte_0_TFBS.bigwig, Monocyte_1_TFBS.bigwig , Tcell_0_TFBS.bigwig, Tcell_1_TFBS.bigwig). Example result files generated by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. Here we filtered ATAC-seq peaks based on accessibility, keeping ~70k highly accessible peaks. (11) CTCF_degron.tar.gz Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details.  (12) obsBias.tsv Input data used for training the Tn5 bias model. For more details see https://github.com/buenrostrolab/PRINT/blob/main/code/predictBias.py (line 84)

opencc-by-4.0Aug 2024View details →

Global scale leaf broadband optical properties derived from CliMA Land and associated CESM simulations

Leaf level broadband reflectance and transmittance computed from leaf traits. <ul> <li>clm_refl_tran_1m_weighted.nc: monthly data (144*96 pixels)</li> <li>surfdata_CMIP6_fluspect_v3.nc: surface data to run CESM (144*96 pixels)</li> </ul> Global scale simulation results <ul> <li>research_data_coupled_future_v2.nc: CESM coupled future simulations</li> <li>research_data_coupled_history_v2.nc: CESM coupled historical simulations</li> <li>research_data_uncoupled_history_v2.nc: CESM uncoupled future simulations</li> <li>research_data_uncoupled_ssp_v2.nc: CESM uncoupled SSP245 and SSP585 simulations</li> </ul> Code changes <ul> <li>SurfaceAlbedoMod.F90: modified CLM module</li> <li>Julia-and-Python-Code.tar.gz: code used for processing the data and plot the figures</li> </ul>

opencc-by-4.0Sep 2024View details →

PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing

We introduce PDMX: a Public Domain MusicXML dataset for symbolic music processing. Refer to our <a title="PDMX Paper" href="https://arxiv.org/abs/2409.10831" target="_blank" rel="noopener">paper</a> for more information, and our <a title="PDMX GitHub Repository" href="https://github.com/pnlong/PDMX/" target="_blank" rel="noopener">GitHub repository</a> for any code-related details. Please cite both our paper and <a href="https://arxiv.org/abs/2410.02084" target="_blank" rel="noopener">our collaborators' paper</a> if you use this dataset (see our GitHub for more information). Upon further use of the PDMX dataset, we discovered a discrepancy between the public-facing copyright metadata on the <a href="https://musescore.com/">MuseScore website</a> and the internal copyright data of the MuseScore files themselves, which affected 31,221 (12.29% of) songs. We have decided to proceed with the former given its public visibility on Musescore (i.e. this is what the MuseScore website presents its users with). We have noted files with conflicting internal licenses in the license_conflict column of PDMX. We recommend using the no_license_conflict subset of PDMX (which still includes 222,856 songs) moving forward. Additionally, for each song in PDMX, we not only provide the MusicRender and metadata JSON files, but we also try to include the associated compressed MusicXML (MXL), sheet music (PDF), and MIDI (MID) files when available. Due to the corruption of 42 of the original MuseScore files, these songs lack those associated files (since they could not be converted to those formats) and only include the MusicRender and metadata JSON files. The all_valid subset of PDMX describes the songs where all associated files are valid.

opencc-by-4.0Nov 2023View details →

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

Data accompanying the manuscript "Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation". Note: For ATAC fragment files, e,caQTL full cis scan summary files, clustering objects, please see the CMDGA portal (https://cmdga.org/search/?searchTerm=stephen-parker%3AVarshney2024) For raw data including fastq files, please see dbGaP repo phs001048.v3.p1 Data in this repository includes: Filename: Description 1. list of 8,666 genes for which exon-only counts were considered. See methods section "Adjusting RNA counts for overlapping gene annotations" in the manuscript. 2. nucleus_sample_cluster_map.tsv: nucleus-sample-cluster map with other QC info.  # index: nucleus identified syntax <modality>.<batch>.NM.<10X channel>.<barcode>  # UMAP_1, UMAP_2: UMAP coordinates for visualization # modality: rna or atac # batch: processing batch identifier # hqaa_umi: high quality autosomal alignments (HQAA) for atac nuclei, unique molecular identifier (UMI) for tna  # fraction_mitochondrial: fraction of reads mapping to the mitochondrial genome # cohort: sample cohort # tss_enrichment: TSS enrichment for atac nuclei # coarse_cluster_name: cluster name 3. peaks.tar.gz: snATAC peak features including: # consensus-summits.bed: consensus summits along with the cell type that the summits was highest in. # narrow peaks in clusters # consensus summit feature (summit +- 150bp) identified in each cluster - these were used in GWAS enrichments. 4. snrna-cell-type-specific-genes.tsv: Normalized expression scores for genes in each cell-type cluster 5. eqtl_permute.tar.gz: Permutation scan eQTL in each cell-type cluster. Columns:  # variant: syntax <chrom>:<hg38 pos>:<ref>:<alt> # effect_allele: effect allele (was the alt allele) # other_allele: non-effect allele # feature: gene name # featureCoordinates_tss: gene TSS # p-value: nominal p value # beta: slope/beta of the linear regression. Keyed on the alt allele # se: standard error of the slope # snp: SNP ID # strand: gene strand # n_variants_tested: number of variants tested for the gene # distance_var_pheno: distance of the variant with the gene TSS # n_effective_tests: number of effective tests # p_beta: beta distribution adjusted p value # qvalue: qvalue (Storey) 6. caqtl_permute.tar.gz: # Permutation scan caQTL in each cell-type cluster. Columns:  # variant: syntax <chrom>:<hg38 pos>:<ref>:<alt> # effect_allele: effect allele (was the alt allele) # other_allele: non-effect allele # feature: peak feature coordinates # p-value: nominal p value # beta: slope/beta of the linear regression. Keyed on the alt allele # se: standard error of the slope # snp: SNP ID # n_variants_tested: number of variants tested for the gene # distance_var_pheno: distance of the variant with the gene TSS # n_effective_tests: number of effective tests # p_beta: beta distribution adjusted p value # qvalue: qvalue (Storey) 7. eqtl_credible_sets.tar.gz: # eQTL credible set. The file name denotes the egene and the signal hit id. Bed file columns:  # 1: snp chromosome # 2: snp start # 3: snp end # 4: snp chrom_pos_ref_alt # 5: Bayes Factor  # 6: PIP # 7: SNP rsid 8. caqtl_credible_sets.tar.gz: # caqtl credible set. The file name denotes the capeak and the signal hit id. Bed file columns:  # 1: snp chromosome # 2: snp start # 3: snp end # 4: snp chrom_pos_ref_alt # 5: Bayes Factor  # 6: PIP # 7: SNP rsid 9. cicero_all.tar.gz # Cicero coaccessibility results. Columns # Peak 1: Macs2 narrowpeak coordinate for peak 1 # Peak 2: Macs2 narrowpeak coordinate for peak 2 # coaccess: Cicero coaccessibility score 10. cicero_gene_tss.tar.gz: Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name. Columns # Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name.Columns # Peak 1: Macs2 narrowpeak coordinate for peak 1 # gene_name: Assigned gene # Peak 2: Macs2 narrowpeak coordinate for peak 2 # coaccess: Cicero coaccessibility score ##  Peak1 is the narrowpeak in the TSS region, peak2 is the distal peak 11. mash.tar.gz Mashr results for e/caQTL - lfsr, posterior means and posterior SD for each tested eSNP-eGene, caSNP-caPeak pair.  12. cellregmap.tar.gz: Cellregmap results for endothelial nucleus-level eQTL scans. ## Persistent genetic effect beta_g was calculated in a simple association model.  ## An interaction model was fit to test for GxC effect. columns: # rho1, g2, e1, and eps2 are variance component measures outputs from CellRegMap corresponding to interaction, genetic, environment and residual variance components.  # p_nominal: nominal p from cellRegMap # kind: model kind in CellRegMap - simple association or interaction # beta_g:  Persistent genetic effect # gene_name: gene name for eQTL or peak feature name for caQTL # context: context used either factors (continuous) or subclusters (discrete) # snp: index snp for which model is fit. This is the most significant identified snp from our standard e,caQTL scans. chrom-hg38pos-rsid 13. coloc-eqtl-caqtl.tsv: # Summary of eQTL-caQTL coloc in each cluster. Columns: # nsnps: Number of SNPs in the region # eqtl_hit: SNP with the highest Bayes factor in the SuSiE eQTL credible set # caqtl_hit: SNP with the highest Bayes factor in the SuSiE caQTL credible set # PP.H0.abf: Coloc posterior probability for no signal # PP.H1.abf: Coloc posterior probability for signal in dataset 1 # PP.H2.abf: Coloc posterior probability for signal in dataset 2 # PP.H3.abf: Coloc posterior probability for different signals in datasets 1 and 2 # PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2 # idx1: Index of the SuSiE credible set for dataset 1 # idx2: Index of the SuSiE credible set for dataset 2 # cluster: cluster name # egene: eGene name # capeak: caPeak coordinates 14. cit-mrs-summary.tsv:  Summary from CIT and MR Steiger directionality tests. Columns: # cluster: cluster name # egene: eGene name # capeak: caPeak coordinates # eqhit: SNP with the highest Bayes factor in the SuSiE eQTL credible set # cahit: SNP with the highest Bayes factor in the SuSiE caQTL credible set # p.cit_c_c-e: P value for CIT causal cahit-ca-to-e model # q.cit_c_c-e: q value for CIT causal cahit-ca-to-e model # p.cit_rc_c-e: P value for CIT reverse-causal eqhit-ca-to-e model  # q.cit_rc_c-e: value for CIT reverse-causal eqhit-ca-to-e model  # p.cit_c_e-c: P value for CIT causal eqhit-e-to-ca model # q.cit_c_e-c: q value for CIT causal eqhit-e-to-ca model # p.cit_rc_e-c: P value for CIT reverse-causal cahit-e-to-ca model  # q.cit_rc_e-c: q value for CIT reverse-causal cahit-e-to-ca model  # cit_direction: Direction inferred from CIT   # correct_causal_direction--ca-to-e: MR Steiger directionality test - is ca-to-e direction correct? # correct_causal_direction--e-to-ca: MR Steiger directionality test - is e-to-ca direction correct? # sensitivity_ratio--ca-to-e: MR Steiger Sensitivity ratio for ca-to-e model  # sensitivity_ratio--e-to-ca:  MR Steiger Sensitivity ratio for e-to-ca model # steiger_test--ca-to-e: MR Steiger directionality test P value for ca-to-e model # steiger_test--e-to-ca: MR Steiger directionality test P value for e-to-ca model # steiger_q--ca-to-e: MR Steiger directionality test q value for ca-to-e model # steiger_q--e-to-ca: MR Steiger directionality test q value for e-to-ca model # mrs_direction: Direction inferred from MR Steiger # direction: Direction inferred requiring consistent results between CIT and MR Steiger directionality test 15. coloc-gwas-eqtl.tsv and 16. coloc-gwas-caqtl.tsv # Summary of e/caQTL coloc with GWAS in each cluster. Columns: # nsnps: Number of SNPs in the region # gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set # eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set # caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set # PP.H0.abf: Coloc posterior probability for no signal # PP.H1.abf: Coloc posterior probability for signal in dataset 1 # PP.H2.abf: Coloc posterior probability for signal in dataset 2 # PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2 # PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2 # idx1: Index of the SuSiE credible set for dataset 1 # idx2: Index of the SuSiE credible set for dataset 2 # cluster: cluster name # egene: eGene name # capeak: caPeak coordinates # p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization # trait: GWAS trait name # gwas_locus: GWAS locus name for the coloc test - a 250kb left and right flanking genomic window on this SNP was considered for testing coloc between all pairs of GWAS/QTL signals identified in this region   # traitname: Expanded GWAS trait name # variable_type: GWAS type  # source: Source of GWAS - either UKBB or other study 17. supplementary_tables.xlsx: Supplementary tables from the manuscript. Information included in sheets: 1. "marker_genes": Marker genes known from literature used to annotate clusters 2. "n_nuclei": n pass-QC nuclei per modality-sample-cluster 2. "snrna_GO_enrichment": GO term enrichment: matrix of cluster vs top 2 GO terms 3. "qtl_scan_info":  e/caQTL scan info cluster: cluster ntested_eqtl: N genes tested for eQTL nsig_eqtl: N significant (5% FDR) eGenes n_pheno_pcs_eqtl: N phenotype PCs considered for eQTL ratio_eqtl: Ratio of N eGenes/N genes tested nsig_caqtl:  N peaks tested for caQTL ntested_caqtl: N significant (5% FDR) caPeaks n_pheno_pcs_caqtl: N phenotype PCs considered for caQTL ratio_caqtl: Ratio of N caPeaks/N peaks tested nsamples_eqtl: N samples for eQTL nsamples_caqtl: N samples for caQTL 4. "gwas_trait_list": GWAS trait info trait: GWAS trait ID traitname: GWAS trait description variable_type: GWAS type. case/control (cc), continuous_irnt=continuous inverse-normal transformed source: GWAS source doi: GWAS study DOI 5. "traits_in_ldsc_baseline" - list of annotations included in the baseline model for LDSC 6. "gwas_enrichment_in_peaks" GWAS enrichment in cluster peaks (S-LDSC) 7. "gwas_enrichment_in_qtl_peaks" GWAS enrichment in QTL peaks (fGWAS) # fGWAS results comparing GWAS enrichment in type 1 annotations CI_lower_ln, estimate_ln, CI_upper_ln: natural log of lower confidence interval, estimate, and upper confidence interval trait: trait id traitname: trait name annotation: annotation sig: 1 if CIs don't overlap 0, otherwise 0 8. t2d_gwas_caqtl_coloc and 9. t2d_gwas_eqtl_coloc: Summary of e,caQTL coloc with T2D GWAS in each cluster, along with target gene nominations. Columns: nsnps: Number of SNPs in the region gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set PP.H0.abf: Coloc posterior probability for no signal PP.H1.abf: Coloc posterior probability for signal in dataset 1 PP.H2.abf: Coloc posterior probability for signal in dataset 2 PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2 PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2 idx1: Index of the SuSiE credible set for dataset 1 idx2: Index of the SuSiE credible set for dataset 2 cluster: cluster name egene: eGene name capeak: caPeak coordinates p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization trait: GWAS trait id diamante_gwas_locus: GWAS signal from the DIAMANTE 2018 study. Some signals that our SuSiE runs identified were not present in the original study in which case this column is NA traitname: Expanded GWAS trait name capeak_in_tss: caPeak in TSS + 1kb upstream region of a gene gene_target_standard_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from all samples for co-accessibility gene_target_allelic_cicero:  caPeak coaccessible with TSS peak of a gene considering nuclei from samples homozygous for the caSNP allele associated with increased accessibility gwashit_nominal_egene: gwas_hit nominally associated with these genes nominated in the columns capeak_in_tss, gene_target_standard_cicero, and  gene_target_allelic_cicero 10. MPRA results for the C2CD4A locus

opencc-by-4.0Feb 2023View details →

A Pixel-scale Corrected Nighttime Light Dataset (PCNL, 1992-2024) Combining DMSP-OLS and NPP-VIIRS

<h1>Updated to 2024! Welcome to download!</h1> We proposed a set of DMSP-OLS and NPP-VIIRS inter-correction methods and produced a pixel-scale corrected nighttime light dataset (PCNL). Two sets of reliable global nighttime light datasets were chosen as the basis. CCNL-DMSP (1992-2013) is a consistent and corrected nighttime light dataset produced from DMSP-OLS. CCNL-DMSP mainly solved three problems of DMSP-OLS, namely, interannual inconsistency, saturation and blooming. The annual VNL-VIIRS dataset available on the Earth Observation Group website was also used, and the monthly median masked data of V21/V22 was selected. Using filtering and employing outlier removal, VNL-VIIRS has removed sunlit, moonlit and cloudy pixels, and has discarded biomass burning pixels. PCNL shows a great temporal and spatial consistency at both the pixel scale and the regional scale.   Please refer to the paper for detailed information. <div> <div>Li, S., Cao, X*., Zhao, C., Jie, N., Liu, L., Chen, X., Cui, X., (2023). Developing a Pixel-Scale Corrected Nighttime Light Dataset (PCNL, 1992–2021) Combining DMSP-OLS and NPP-VIIRS. Remote Sensing, 15, 3925. <a href="https://doi.org/10.3390/rs15163925" target="_blank" rel="noopener">https://doi.org/10.3390/rs15163925</a></div> </div>   Other recent publications using PCNL: Li, S., Cao, X.*, (2024). Monitoring the modes and phases of global human activity development over 30 years: Evidence from county-level nighttime light.  International Journal of Applied Earth Observation and Geoinformation, 126, 103627. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jag.2023.103627" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jag.2023.103627</a> <div> <div> <div>Li, S., Cao, X.*, Liu, L., Li, A., (2025). Inequality of divided and shared socio-economic resources in 15-minute cities of China. Geography and Sustainability, 100337. <a href="https://doi.org/10.1016/j.geosus.2025.100337" target="_blank" rel="noopener">https://doi.org/10.1016/j.geosus.2025.100337</a></div> </div> </div>

opencc-by-4.0Nov 2024View details →

Supplemental dataset to "Cloud sync in response to wave-like large-scale forcings"

This dataset deposits the supplemental materials for the manuscript "Cloud sync in response to wave-like large-scale forcings". <a href="https://zenodo.org/api/records/14164798/draft/files/math_note.pdf/content" target="_blank" rel="noopener noreferrer">math_note.pdf</a>  A hand-written math derivation note for equations in the appendices.  <a href="https://zenodo.org/uploads/15304862" target="_blank" rel="noopener noreferrer">movie_wL_006_T24hours.avi</a>  A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=24 hours experiment (the reference CM1 simulation). <a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_006_T18hours.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_006_T18hours.avi</a>  A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=18 hours experiment. <a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_006_T12hours.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_006_T12hours.avi</a>  A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=12 hours experiment. <a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_000.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_000.avi</a>  A movie of near-surface (z=25m) water vapor mixing ratio, without large-scale wave-like forcing. <a href="https://zenodo.org/api/records/15304862/draft/files/input_sounding/content" target="_blank" rel="noopener noreferrer">input_sounding</a>  The initial sounding for all CM1 simulations.  <a href="https://zenodo.org/api/records/15304862/draft/files/namelist.input/content" target="_blank" rel="noopener noreferrer">namelist.input</a>  The namelist file for launching all CM1 simulations.  <a href="https://zenodo.org/api/records/15304862/draft/files/postprocessing_CM1.zip/content" target="_blank" rel="noopener noreferrer">postprocessing_CM1.zip</a> The postprocessing code of the CM1 simulations, including intermediate output files (.mat) in data postprocessing.   <a href="https://zenodo.org/api/records/14164798/draft/files/microscopic_model.zip/content" target="_blank" rel="noopener noreferrer">microscopic_model.zip</a>  The MATLAB code for the microscopic model.  <a href="https://zenodo.org/api/records/15304862/draft/files/cm1.F/content" target="_blank" rel="noopener noreferrer">cm1.F</a>  The CM1 script where the large-scale vertical velocity is programmed. You can copy it directly to your CM1/src/ path.    Feel free to send an email to Dr. Hao Fu (haofu@nju.edu.cn) if you have any questions!

opencc-by-4.0Apr 2023View details →

StemGMD: A Large-Scale Audio Dataset of Isolated Drum Stems for Deep Drums Demixing - part 1

We introduce StemGMD, a new large-scale dataset of isolated drum stems that builds upon the extensive MIDI collection found in <a href="https://magenta.tensorflow.org/datasets/groove">Magenta's Groove MIDI Dataset (GMD)</a>. GMD is a 13.6-hour corpus of expressive drum performances executed by ten drummers on a Roland TD-11 electronic drum kit. It contains 1150 MIDI files along with the corresponding full-kit audio mixtures. As a first step in creating StemGMD, we mapped the 22 different MIDI pitches found in the original files onto nine canonical instruments through the reduction scheme proposed  in J. Gillick, A. Roberts, J. Engel, D. Eck, and D. Bamman, "Learning to groove with inverse sequence transformations," in International Conference on Machine Learning (ICML), vol. 97, 2019, pp. 2269–2279. Each of the nine resulting MIDI channels was manually synthesized as a 16-bit/44.1 kHz stereo WAV file using ten realistic-sounding acoustic drum kits sourced from the <a href="https://support.apple.com/en-me/guide/logicpro/lgsi2fb2509e/mac">Logic Pro X sample libraries</a>, i.e., Bluebird, Brooklyn, Detroit Garage, East Bay, Heavy, Motown Revisited, Portland, Retro Rock, Roots, and SoCal. As a result, StemGMD contains 1224 hours of audio, which correspond to more than 136 hours of full-kit mixtures. Moreover, StemGMD also contains single hits for each of the drum pieces at ten different velocities ranging from 30 to 127. To the best of our knowledge, StemGMD is the largest publicly available dataset of drums to date. Moreover, it is the first collection of single-instrument clips from all nine pieces in a canonical drum kit, making it well-suited for training deep drums demixing models.   *** THIS IS PART 1 OF 2 *** Download part 2 here: <a href="../records/7882857">https://zenodo.org/records/7882857 </a> (now available!)  After downloading both parts, run unzip_StemGMD.sh to build the dataset from the split archive files. Once unzipped, StemGMD will take just over 1.13 TB of memory.   The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0) License</a>. ____________________________ We employed the dataset in our paper titled "Toward Deep Drum Source Separation," published in Pattern Recognition Letters.  <div> <div> <div> <div> Please, cite this work as: A. I. Mezza, R. Giampiccolo, A. Bernardini, and A. Sarti, "Toward Deep Drum Source Separation," Pattern Recognition Letters, vol. 183, pp. 86-91, 2024, doi: 10.1016/j.patrec.2024.04.026. <pre>@article{mezza2024, title = {Toward deep drum source separation}, author = {Alessandro Ilic Mezza and Riccardo Giampiccolo and Alberto Bernardini and Augusto Sarti}, journal = {Pattern Recognition Letters}, volume = {183}, pages = {86-91}, year = {2024}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2024.04.026} }</pre> </div> </div> </div> </div>

opencc-by-4.0Oct 2023View details →

An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')

These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units. The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).

Fig.ç4.C aligus longiramus sp. nov., holotype, female (KMNH IvR 500,511). A, leg 2, dorsal view; B, leg 3, dorsal view; C, leg 4, dorsal view; D, leg 5, ventral view. Scale bars: 0.1 mm. in Occurrence of Caligid Copepods (Crustacea) in Plankton Samples Collected from Japan and Ŋailand, with the Description of a New Species

Fig.ç4.C aligus longiramus sp. nov., holotype, female (KMNH IvR 500,511). A, leg 2, dorsal view; B, leg 3, dorsal view; C, leg 4, dorsal view; D, leg 5, ventral view. Scale bars: 0.1 mm.

Fig.ç3.C aligus longiramus sp. nov., holotype, female (KMNH IvR 500,511). A, maxilla, dorsal view; B, maxilliped, dorsal view; C, sternal furca, dorsal view; D, leg 1, ventral view; E, exopod of leg 1 enlarged, ventral view. Scale bars: 0.1 mm. in Occurrence of Caligid Copepods (Crustacea) in Plankton Samples Collected from Japan and Ŋailand, with the Description of a New Species

Fig.ç3.C aligus longiramus sp. nov., holotype, female (KMNH IvR 500,511). A, maxilla, dorsal view; B, maxilliped, dorsal view; C, sternal furca, dorsal view; D, leg 1, ventral view; E, exopod of leg 1 enlarged, ventral view. Scale bars: 0.1 mm.

Fig.ç25.A mblyops sp. 4, immature male (NSMT-Cr 21370). A, eyeplate (right, dorsal); B, eyeplate (right, lateral); C, antennular peduncle (le, ventral); D, antennal scale (right, dorsal); E, uropod and telson (dorsal); F, proximal part of uropodal endopod (le, ventral); G, posterior part of telson (dorsal). in The Genus Amblyops (Crustacea: Mysida: Mysidae: Erythropinae) from East Asia and Australia, with Descriptions of Ten New Species

Fig.ç25.A mblyops sp. 4, immature male (NSMT-Cr 21370). A, eyeplate (right, dorsal); B, eyeplate (right, lateral); C, antennular peduncle (le, ventral); D, antennal scale (right, dorsal); E, uropod and telson (dorsal); F, proximal part of uropodal endopod (le, ventral); G, posterior part of telson (dorsal).

Fig.ç24.A mblyops sp. 3, male (NSMT-Cr 21369). A, anterior part of body (dorsal); B, eyeplate (right, dorsal); C, antennal scale (le, ventral); D, pair of genital organs; E, sternal process and proximal parts of genital organs; F, rst pleopod (le); G, proximal part of uropodal endopod (le, ventral). in The Genus Amblyops (Crustacea: Mysida: Mysidae: Erythropinae) from East Asia and Australia, with Descriptions of Ten New Species

Fig.ç24.A mblyops sp. 3, male (NSMT-Cr 21369). A, anterior part of body (dorsal); B, eyeplate (right, dorsal); C, antennal scale (le, ventral); D, pair of genital organs; E, sternal process and proximal parts of genital organs; F, rst pleopod (le); G, proximal part of uropodal endopod (le, ventral).

Fig.ç9.A mblyops okinawensis sp. nov., holotype, female (NSMT-Cr 21353). A, anterior part of body (dorsal); B, eyeplate (right, dorsal); C, eyeplate (right, lateral); D, antennular peduncle (right, dorsal); E, antennal scale (right, dorsal); F, antennal peduncle (right, lateral); G, mandibular palp (right); H, maxilla (right), I, uropod and telson (dorsal); J, proximal half of uropodal endopod (right, ventral); K, posterior part of telson (dorsal). in The Genus Amblyops (Crustacea: Mysida: Mysidae: Erythropinae) from East Asia and Australia, with Descriptions of Ten New Species

Fig.ç9.A mblyops okinawensis sp. nov., holotype, female (NSMT-Cr 21353). A, anterior part of body (dorsal); B, eyeplate (right, dorsal); C, eyeplate (right, lateral); D, antennular peduncle (right, dorsal); E, antennal scale (right, dorsal); F, antennal peduncle (right, lateral); G, mandibular palp (right); H, maxilla (right), I, uropod and telson (dorsal); J, proximal half of uropodal endopod (right, ventral); K, posterior part of telson (dorsal).

Fig.ç12.A mblyops paci cus sp. nov., A, E, F, H–J, holotype, male (NSMT-Cr 21355); B–D, G, one of paratypes, female (NSMT-Cr 21357). A, B, anterior part of body (dorsal); C, eyeplate (right, dorsal); D, antennular peduncle (right, dorsal); E, antenna (right, dorsal); F, apical part of antennal scale (right, dorsal); G, antenna (right, dorsal); H, mandible and mandibular palp (right), I, outer lobe of maxillule (right); J, maxilla (right). in The Genus Amblyops (Crustacea: Mysida: Mysidae: Erythropinae) from East Asia and Australia, with Descriptions of Ten New Species

Fig.ç12.A mblyops paci cus sp. nov., A, E, F, H–J, holotype, male (NSMT-Cr 21355); B–D, G, one of paratypes, female (NSMT-Cr 21357). A, B, anterior part of body (dorsal); C, eyeplate (right, dorsal); D, antennular peduncle (right, dorsal); E, antenna (right, dorsal); F, apical part of antennal scale (right, dorsal); G, antenna (right, dorsal); H, mandible and mandibular palp (right), I, outer lobe of maxillule (right); J, maxilla (right).