Find research datasets worth reusing

opencc-by-4.0Jan 2022View details →

Supplementary material for "Spatio-temporal modelling of abundance from multiple data sources in an integrated spatial distribution model"

Abstract Aim: In biodiversity monitoring, observational data are often collected in multiple, disparate schemes with greatly varying degrees of standardization and possibly at different spatial and temporal scales. Technical advances also change the type of data over time. The resulting heterogeneous data sets are often deemed to be incompatible. Consequently, many available data sets may be ignored in practical analyses. Here, we propose a more efficient use of disparate biodiversity data to assess species distributions and population trends. Location: Switzerland (Europe) Taxon: Birds Methods: We developed an integrated, hierarchical species distribution model with a joint likelihood for all data sets using a shared state process (e.g., latent species abundance or occurrence), but distinct observation process for each data set. We show how the abundance submodel of a binomial N-mixture model can fuse four different data types (count, detection/non-detection, presence-only, and absence-only data) and enable improved inferences about spatio-temporal patterns in abundance. As case studies, we use data from multiple avian biodiversity monitoring schemes. In the first, the goal is estimating abundance-based species distribution maps. In the second, we infer trends in population abundance across time. Results: Accuracy and precision of abundance estimates increased when combining data from different sources compared to using a single data source alone. This is particularly valuable when data from each single data source is too sparse for reliable parameter estimation. Main conclusions: We show that exploiting the complementary nature of "cheap", but abundant, citizen-science data and less abundant, but more information-rich, data from structured monitoring programs might be ideal to estimate distribution and population trends more accurately, especially for rare species. Joint likelihoods allow to include a wide variety of different data sets to (1) combine all the available information and to (2) mitigate weaknesses of one by the strength of another.

Supplementary data for "Many-Body van der Waals Interactions in Wet MoS2 Surfaces"

Supplementary data for "Many-Body van der Waals Interactions in Wet MoS2 Surfaces"

Supplementary data and code to: One site with multiple tags versus multiple sites with single tags: optimising the determination of localisation spaces by pseudocontact shifts

Supplementary data and code to the publication: One site with multiple tags versus multiple sites with single tags: optimising the determination of localisation spaces by pseudocontact shifts Includes: Python scripts for use with paramagpy to calculate localisation spaces. Experimental NMR data and calculation scripts for Ubiquitin

Supplementary Data - Hadean isotopic fractionation of xenon retained in deep silicates - by Rzeplinski et al. Nature 2022

Supplementary data (mass spectrometry detailed results) of Hadean isotopic fractionation of xenon retained in deep silicates, Nature 2022.

opencc-by-4.0Jan 2022View details →

The benefits of coastal adaptation through conservation of foreshore vegetation Supplementary data

This data consists of the first global scale assessment of the benefits of conserving foreshore vegetation as a means of adaptation to future projections of change in coastal flood risk. In doing so, we extend the current knowledge on the economic feasibility of implementing global scale Nature-based Solutions. We show that globally foreshore vegetation can contribute to a large decrease in both absolute and relative flood risk (13% of present-day, and 8.5% of future conditions in 2080 of global flood risk). Although the analysis of the results give a first proxy of the flood risk reduction benefits of conserving foreshore vegetation at the global scale, it shows promising results for including nature-based and hybrid adaptation measures in coastal adaptation schemes.

dryad32/100

Gamma-gamma revisited supplementary data

This archive contains datasets, images, raw data-analysis results files, and software code listing for the analyses undertaken for the purpose of evaluating James A. Ford's interpretations of his Gamma-gamma village archeological simulation. Results of our investigations are reported in full in MacLeod, N., and Nash, Brendan, S., 2021, Ford's Gamma-Gamma village simulation revisited: highlighting the need for a new middle-range theory of archaeological types: Archaeometry, v. 63, p. 1081–1104.

opencc-zeroFeb 2022View details →

Supplementary data for "Incorporation of phosphatidylserine improves efficiency of lipid based gene delivery systems"

Supplementary data for the manuscript entitled "Incorporation of phosphatidylserine improves efficiency of lipid based gene delivery systems".

Supplementary Data: Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China using Machine Learning Emulation.

Temporary duplicate of: Conibear, L., Reddington, C. L., Silver, B. J., Chen, Y., Arnold, S. R., & Spracklen, D. V. (2022). Supplementary Data: Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China using Machine Learning Emulation. University of Leeds. [Dataset]. https://doi.org/doi.org/10.5518/1055

Supplementary Figure 1: The road to FAIR genomes: a gap analysis of NGS data generation and sharing in the Netherlands

Supplementary Figure 1: a flow chart conceptualizing the gap analysis. A generic NGS process diagram was created, based on a commonly used care workflow (Step 1). Next, a questionnaire about the inventory of (meta)data standards and retrieval of gaps was drafted (Step 2), which together with the process diagram was used as a basis for the subsequent interviews (Step 3). In parallel with the first three steps, a short literature review was performed (Step 4). The interviews were processed and current gaps were identified, anonymized and classified (Step 5). Finally, the results are shared with the community through presentations, publications and suggestions for next steps for addressing the identified gaps.

Bestbiosurf supplementary files, data sources, and scripts

Sources archive containing predicted NRPS clusters, networks, and supporting code for the Bestbiosurf consortium project https://www.cobiotech.eu/funded-projects/1st-call/bestbiosurf

dryad32/100

Code, data and supplementary material from: Analyzing phylogenetic trees with a tree lattice coordinate system and a graph polynomial

This dataset includes code, used and generated data for the experiments in the paper Analyzing Phylogenetic Trees with a Tree Lattice Coordinate System and a Graph Polynomial, and the supplementary material of the paper. The paper introduces representation and comparison methods for rooted unlabelled phylogenetic trees based on a tree lattice that serves as a coordinate system for rooted binary trees with branch lengths and a graph polynomial that fully characterizes tree shapes. The methods provide fast, accurate, likelihood-free, distance-based appraoches to analyzing phylogenetic trees and allow analysis for large trees with more than a thousand taxa. The dataset has three sections. The first section is the implementation of the introduced methods for phylogenetic tree analysis as an R package, inlcuding the source code and the binary of the package. This packege provides basic functions for tree analysis and can be used or edited for related analysis. The secoond section includes all code for analyses performed in the the paper, and all the data used and generated in the analyses. The third section contains the supplementary material for the paper, which includes supplememntary results and figures. For details, please refer to the paper and the README file.

Supplementary material 1 from: Gutiérrez EE, Helgen KM, McDonough MM, Bauer F, Hawkins MTR, Escobedo-Morales LA, Patterson BD, Maldonado JE (2017) A gene-tree test of the traditional taxonomy of American deer: the importance of voucher specimens, geographic data, and dense sampling. ZooKeys 697: 87-131. https://doi.org/10.3897/zookeys.697.15124

Gazetteer : Authors: Eliécer E. Gutiérrez, Kristofer M. Helgen, Molly M. McDonough, Franziska Bauer, Melissa T. R. Hawkins, Luis A. Escobedo-Morales, Bruce D. Patterson, Jesús E. Maldonado

Supplementary material 3 from: Gutiérrez EE, Helgen KM, McDonough MM, Bauer F, Hawkins MTR, Escobedo-Morales LA, Patterson BD, Maldonado JE (2017) A gene-tree test of the traditional taxonomy of American deer: the importance of voucher specimens, geographic data, and dense sampling. ZooKeys 697: 87-131. https://doi.org/10.3897/zookeys.697.15124

Supplementary information figure : Authors: Eliécer E. Gutiérrez, Kristofer M. Helgen, Molly M. McDonough, Franziska Bauer, Melissa T. R. Hawkins, Luis A. Escobedo-Morales, Bruce D. Patterson, Jesús E. Maldonado

Supplementary material 2 from: Gutiérrez EE, Helgen KM, McDonough MM, Bauer F, Hawkins MTR, Escobedo-Morales LA, Patterson BD, Maldonado JE (2017) A gene-tree test of the traditional taxonomy of American deer: the importance of voucher specimens, geographic data, and dense sampling. ZooKeys 697: 87-131. https://doi.org/10.3897/zookeys.697.15124

Name and DNA sequences of pairs of primers used for amplification and sequencing of the CYTB gene : Data type: molecular data

opencc-by-4.0Mar 2022View details →

Supplementary files for "Building bridges between natural and social science disciplines: A standardised methodology to combine data on ecosystem quality trends"

Data sources and mapping methodology

opencc-by-4.0Mar 2022View details →

Supplementary material for "Modeling styles in conceptual data modeling: Reflecting observations in a series of multimodal studies'"

This material supplements the following conference publication: Rosenthal, K., Wagner, S., Strecker, S. (2022) Modeling styles in conceptual data modeling: Reflecting observations in a series of multimodal studies

opencc-by-4.0Mar 2022View details →

Supplementary data for the article "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."

Supplementary figures, tables and raw data for the article " "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants." Food sharing can occur in both social and non-social species but is crucial in eusocial species in which only some group members collect food. This food collection but also intranidal food distribution through trophallactic (i.e. mouth-to-mouth) exchanges are fundamental issues in eusocial insects. However, the behavioural rules underlying the regulation and the dynamics of food intake and the resulting networks of exchanges are poorly understood. In this study, we provide new insights into the behavioural rules underlying the structure of trophallactic networks and food dissemination dynamics within the colony. We build a simple data-driven model that implements interindividual variability and division of labour to investigate the processes of food accumulation/dissemination inside the nest, both at the individual and collective levels. We also test the alternative hypotheses (no variability and no division of labour). Division of labour with inter-individual variability predicts contrary to other models the food dynamics and exchange networks. We establish the links between the interindividual heterogeneity of the trophallactic behaviours, the food flow dynamics and network of trophallactic events. Despite the relative simplicity of the model rules, efficient trophallactic networks may emerge as the ones observed in ants leading to better understanding of evolution of such societies.

opencc-by-4.0May 2018View details →

Supplementary Data - Nanopore cDNA With and Without mtDNA

Update (v1.5.8-SUPcaller): added scripts and data files for mt-Nd1 comparisons for different basecaller versions: <ul> <li>LAST_gci_*.csv - gap-compressed identity statistics</li> <li>compStats_*.csv - called base-level statistics</li> <li>process.txt - script example file demonstrating how to generate statistics</li> <li>smealgol.r - R script to generate gap-compressed identities from LAST output</li> <li>est_vs_actual.png - density plots comparing gap-compressed identity with mean estimated accuracy from the caller</li> <li>GCI_albacore_guppy.png - violin plots comparing gap-compressed identity with different callers</li> <li>BaseQ_vs_GCI.png - scatter plots comparing gap-compressed identity with mean estimated accuracy from the caller</li> </ul> Update (v1.5.7-Nd1Reads): added mt-Nd1 read subset from CGDec2017 run: <ul> <li>4T1-WT_reads_CGDec2017_singlePlusMulti.tar</li> </ul> Update (v1.5.6-countAnalysis): added / updated a script to convert transcript mappings to a count table: <ul> <li>count_analysis.r</li> </ul> Update (v1.5.5-Int): added intermediate (post-demultiplexing) demonstration dataset: <ul> <li>SampleDESet_60k_demultiplexed.tar.gz</li> </ul> Also updated count tables and DESeq2 results: <ul> <li>raw_counts_2019-Nov-29.csv</li> <li>DE_orig_VST_GRCm38_CG_GL261_2019-Nov-29.csv.gz</li> <li>DE_shrunk_VST_GRCm38_CG_GL261_2019-Nov-29.csv.gz</li> </ul> Update (v1.5.4-MT): updated demonstration dataset (now 40Mb) to include mitochondrial transcripts, and exclude non-target sequences from chimeric reads: <ul> <li>reads_SampleDESet_sampled_60k.fastq.gz</li> <li>Mm_subset.GRCm38.ensembl_v98.fa</li> </ul> Update (v1.5.3-Ensembl): added/updated Ensembl reference transcripts, including non-coding RNA: <ul> <li>ensembl_mm10_geneFeatureLocations.txt.gz - genome annotation</li> <li>Mus_musculus.GRCm38.ensembl_v98.fa.gz - transcript sequences</li> </ul> Update (v1.5.2-Demo): added 60Mb demonstration dataset for teaching / learning purposes (reads_SampleDESet_sampled_60k.fastq.gz). This dataset can be used for testing demultiplexing, or carrying out differential expression analysis on nanopore reads. There are 15k reads sampled from each run (subset on ~300 genes), plus 7.5k chimeric reads from the Dec17 run, plus 7.5k reads sampled from from CG009/BC08. The gene subset includes ~100 genes differentially-expressed between 4T1WT and 4T1ρ0 cell lines, plus an additional ~200 genes with minimal differential expression observed between any 4T1 cell lines (i.e. a housekeeping gene set): <ul> <li>BC01, BC02, BC04, BC06 - 4T1 cell line reads from three sequencing runs (005, 005, 004, Dec17)</li> <li>BC03, BC05, BC07 - 4T1ρ0 (without mtDNA) cell line reads from three sequencing runs (005, 004, Dec17)</li> <li>BC08 - 4T1ρ0SC (from subcutaneous tumour) cell line reads from one sequencing run (009)</li> </ul> [note: v1.5.1-Demo has the wrong sequence ID for BC08 reads] Update (v1.5.0-Called): added full 4T1 call set (called using guppy high-accuracy v3.2.2), gene transcript count tables, and differential expression results Update (v1.4.1-Chimeric): added more example called chimeric reads of various [unclassified] categories Update (v1.4.0-Chimeric): added example chimeric reads, one formed [presumably] during sample prep ["conjugation"], another formed in-silico ["double read"] Update (v1.3.0-DESeq2): added count table and metadata file for DESeq2 analysis Update (v1.2.1-10k): added 10k of FASTQ reads from a more recent run (part of the same differential expression project). Supplementary data to go with David Eccles' poster about nanopore cDNA sequencing with and without mitochondrial DNA. Reference mouse mtDNA and mtDNA reads from 4T1 cells (from MinION cDNA sequencing, and from SRA run SRR6747859). <ul> <li>fwd_4T1_BC06.correctedReads.uniqueOnly.fasta.gz -- Canu-corrected cDNA reads from Wildtype 4T1 strain, filtered as transcript-forward orientation</li> <li>rev_4T1_BC06.correctedReads.uniqueOnly.fasta.gz -- Canu-corrected cDNA reads from Wildtype 4T1 strain, filtered as transcript-reverse orientation</li> <li>fwd_4T1_BC07.correctedReads.uniqueOnly.fasta.gz -- Canu-corrected cDNA reads from 4T1ρ0 strain (no mitochondrial DNA), filtered as transcript-forward orientation</li> <li>rev_4T1_BC07.correctedReads.uniqueOnly.fasta.gz -- Canu-corrected cDNA reads from 4T1ρ0 strain (no mitochondrial DNA), filtered as transcript-reverse orientation</li> </ul> Other intermediate data files are also included, see `4way_mapping_process.txt` for more details. The mpileup differential coverage script can be found <a href="https://github.com/gringer/bioinfscripts/blob/master/mpileupDC.pl">here</a>.

opencc-by-4.0Apr 2022View details →

Single-cell RNA-seq supplementary data

These files are supplementary data for the article Laczik M, Erdős E,  Ozgyin L, Hevessy Zs, Csősz É, Kalló G, Nagy T, Barta E, Póliska Sz, Szatmári I and Bálint BL: Extensive proteome and functional genomic profiling of variability between genetically identical human B-lymphoblastoid cells. The files are outputs from the 10x Genomics software Cell Ranger and Loupe Browser, they contain QC data for the single cell sequencing described in the article, and also comparative analyses between 3 datasets (GM22648old, GM22648new, GM22649). For further details please see the article.