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Supplementary Data to journal publication on 'The Foundations of the Patagonian Icefields'
<p>Partitioning and comparison of ice discharge estimates from the the Patagonian Icefields comprising associated uncertainties. For further details please refer to the notes in the individual files and/or consult the associated publication entitled 'The Foundations of the Patagonian Icefields' published in Communications Earth & Environment.</p>
A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice (Supplementary Data)
<p><strong>This is the supplemental material for:</strong></p> <p>Brooks, H.L., Miner, K.R., Kreutz, K.J., Winski, D.A., (in review). A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice. </p> <p><strong>Purpose:</strong></p> <p>This systematic literature review contextualizes current data availability and examines spatial and temporal gaps in the long-range transported Pb analyses (concentration and isotope ratios) in ice and snow samples. Additionally, we note areas of needed community improvement. It is our hope that researchers will also benefit from a queryable set of references, allowing for quick access to the records appropriate to address multiple research questions. </p> <p><strong>Available Files:</strong></p> <p><em><strong>Table A1:</strong></em> Metadata for Pb records -- Individual sample sites</p> <p><em><strong>Table A2:</strong></em> Metadata for Pb records -- Transect sample sites</p> <p><em><strong>Table A3:</strong></em> Records grouped into 23 regions</p> <p><em><strong>Supplement_fig_25Aug2024: </strong></em>Additional figures supporting main manuscript</p> <p><em><strong>Supplement_method_25Aug2024: </strong></em>Methodology used for the systematic literature review</p> <p><em><strong>Supplement_citations_25Aug2024:</strong></em> Citations for all records included in the systematic literature review</p> <p><em><strong>citations_export.bib:</strong></em> Export of all systematic literature review citation data as bibtex format. Easy import to citation managers (Zotero, Mendley, Endnote, etc)</p> <p><em><strong>indexedReferences.csv:</strong></em> CSV dump of citations_export.bib indexed with citation keys used in TableA.3</p> <p><em><strong>tables.RDS: </strong></em>TableA.1, TableA.2, and indexed References formatted for easy import into R</p> <p><em><strong>tables.sqlite: </strong></em>TableA.1, TableA.2, and indexed References formatted for SQL queries in SQLite</p> <p><em><strong>readme_tables_sqlite.md:</strong></em> Examples of SQLite queries</p> <p> </p> <p><strong>Systematic Literature Review Methodology:</strong></p> <p>To address the current spatial and temporal distribution of long-range transported Pb deposited in the cryosphere (snow-pits and ice cores), we completed a systematic literature review, following the methodology outlined by Booth et al (2016). We completed an “exhaustive coverage [search], citing all relevant literature" (Booth et al., 2016), using the search terms “Lead (Pb) isotopes and concentration in surface snow, snow pits, and ice cores”. We performed an initial comprehensive literature search on these search terms on Web of Science Collection databases in September 2020 and May 2023. Records evaluated for relevance using the title and abstract. Removal of clearly off-topic papers (e.g., the chemistry of penguin feces) gathered in the search due to the dual meaning of “lead” reduced the paper count to 326 titles. The full text of the remaining publications was evaluated with clear explicit criteria for inclusion and exclusion, based on the following criteria.</p> <ol> <li> <ol> <li>Only studies examining long-traveled background atmospheric lead signals were considered. All point source pollution studies examining the localized effects of traffic, road salt, mines, industry, power plants, human activity at base camp stations, etc, were excluded. An exception was made for samples which were taken at sufficient depths in the analyzed record to predate the pollution source or where wind trajectory did not transport pollution to the collection site regardless of close geographic proximity.</li> <li> <p>Only studies of natural, undisturbed snowpacks and ice cores were examined. Studies which sampled snow from urban structures were excluded. Point source studies of emissions detail the localized effects of traffic, road salt, mines, industry, power plants, and human activity at base camp stations. While meaningful for understanding the direct emissions from various sources and developing new technology aimed at reducing source emissions, point source emission studies do not contribute to the understanding of regional and global signals. Additionally, studies examining the volcanic signal in snow following major modern eruptions were excluded, as this was classified as disturbed snow.</p> </li> <li>Studies must specify the sampling localities by providing a minimum of latitude and longitude. Where sampling locations are only referenced by colloquial names, the distance from point source pollution cannot be verified. Therefore, such studies were excluded.</li> <li> <p>Records of <sup>210</sup>Pb in snow and ice were excluded. <sup>210</sup>Pb is useful for establishing chronology in young snow and ice due to its small half life (~ 22.3 years). But it is not useful for consideration of old records and the source constraint of <sup>210</sup>Pb into the atmosphere is poorly constrained over time (Nijampurkar & Clausen, 1990). Therefore, it cannot be considered in conjunction with Pb isotopes and concentrations. Records of <sup>210</sup>Pb in snow and ice were excluded.</p> </li> <li> <p>Pb isotopes and concentrations taken from cryoconites (soil-like composites of dust, industrial soot, and microbial mats of photosynthetic bacteria) were excluded from this literature review. Cryoconites are important to glacial systems as they alter the albedo of the glacier surface, and therefore affect the glacier melt rate (Fountain et al., 2004). However, they must be considered separately from surface snow, snow pits, and ice cores due to the drastic differences in formation and biologic nature.</p> </li> <li> <p>The publication must be available to the author (<em>e.g.,</em> through the University Library, from collaborators)</p> </li> </ol> </li> </ol> <p>To ensure that the literature search conducted on the Web of Science was robust and complete, citations were checked to ensure inclusion in the literature search results and included when missing. Publications were indexed into Table A.1 and Table A.2. Following the completion of publication indexing, Table A.1 and Table A.2 were evaluated against the 23 regions (Table A.3) -- 20 from RGI 7.0 (RGI 7.0 Consortium, 2023) and 3 author defined regions -- to identify areas/papers that may have been missed in the initial search. Areas with few or no results were searched again using Google Scholar and Web of Science.</p> <p>Based on these searches, we sought to understand the current spatial and temporal coverage of these records, shed light on gaps in the previous research and make recommendations on mitigating these gaps going forward. We used tables and graphics, included in the main text and the supplement, to summarize the characteristics of the compiled records. In the main text, we discuss the limitations and gaps within the current long-range transported Pb literature, and recommend paths to mitigate these gaps. Finally, in the main text, we illustrate an example of how researchers can query this record compilation, allowing for quick access to the records appropriate to address their research questions.</p> <p><strong>Methodology Bibliography:</strong></p> <p>Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (Second edition). Sage.</p> <p>Fountain, A. G., Tranter, M., Nylen, T. H., Lewis, K. J., & Mueller, D. R. (2004). Evolution of cryoconite holes and their contribution to meltwater runoff from glaciers in the McMurdo dry valleys, Antarctica. Journal of Glaciology, 50(168), 35–45. https://doi.org/10.3189/172756504781830312</p> <p>Nijampurkar, V. N., & Clausen, H. B. (1990). A century old record of lead-210 fallout on the greenland ice sheet. Tellus Series B Chemical and Physical Meteorology, 42(1), 29–38. https://doi.org/10.1034/j.1600-0889.1990.00005.</p> <p>RGI 7.0 Consortium. (2023). Randolph glacier inventory—A dataset of global glacier outlines, version 7.0. (Version 7.0) [Dataset]. NSIDC: National Snow and Ice Data Center. https://doi.org/doi:10.5067/f6jmovy5navz</p>
Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study (Supplementary Data)
<p>The zip file contains supplementary data for the publication - Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study, accepted for publication in Environmental Health Perspectives (DOI: 10.1289/EHP6174).</p> <p>The description of the files are noted below:</p> <p><strong>1. Readme File for SAPALDIA Noise and Air Pollution EWAS Single Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_SingleExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_SingleExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p> </p> <p><strong>General footnote for all files:</strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from single exposure epigenome-wide linear mixed models, with random intercept at the level of participant. Each model was adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator (for Lden models) and leukocyte composition. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites.</p> <p>Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p> <p> </p> <p><strong>2. Readme File for SAPALDIA Noise and Air Pollution EWAS Multi Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_MultiExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_MultiExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p><strong>General table footnotes: </strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from multi-exposure epigenome-wide linear mixed models, with random intercept at the level of participant, and were adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator and leukocyte composition. Multi-exposure models included all five exposures (Aircraft, railway, road traffic Lden and respective truncation indicators, NO<sub>2</sub> and PM<sub>2.5</sub>) at the same time. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites. Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p>
Supplementary data for Nested sampling cross-checks using order statistics
<p>This is the raw data for tables 1 and 2 in <a href="https://arxiv.org/abs/2006.03371">Nested sampling cross-checks using order statistics</a>. The file names for the MultiNest results in table 1 are:</p> <pre><code>MN_{PROBLEM}_{number of dimensions}d_efr_{MultiNest efr parameter}.txt</code></pre> <p>The file names for the PolyChord results in table 2 are:</p> <pre><code>PC_{PROBLEM}_{number of dimensions}d_nr_{PolyChord number of repeats}.txt</code></pre> <p>where PROBLEM specifies one of four test functions described in Appendix C</p> <ul> <li>gaussian = Gaussian</li> <li>mixture = Gaussian-log-gamma mixture</li> <li>rosenbrock = Rosenbrock function</li> <li>shells = Gaussian shells</li> </ul> <p>Each file contains 100 rows (corresponding to 100 runs) and 8 columns</p> <ol> <li>log evidence</li> <li>error log evidence</li> <li>KS statistic from test on all iterations</li> <li>number of iterations</li> <li>p-value from all iterations</li> <li>Greatest KS statistic from tests on chunks of iterations</li> <li>Iteration of start of chunk at which greatest KS statistic occurred</li> <li>Bonferroni corrected p-value from greatest KS statistic from tests on chunks of iterations</li> </ol>
Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase (supplementary data)
<p>This a dataset of scanning transmission electron microscopy data showing Pt clusters nucleating in an ionic liquid. For each of the 4 movies there is the raw data (uncompressed .tif and compressed as .avi) and denoised versions (uncompressed .tif and compressed as .avi).</p> <p>This data is for the article "Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase" published in ChemNanoMat (2020), by Trond R. Henninen, Debora Keller and Rolf Erni. (https://onlinelibrary.wiley.com/doi/full/10.1002/cnma.202000503)</p> <p><strong>Movie 1:</strong> Homogeneous nucleations of two clusters in a suspended thin film of ionic liquid. </p> <p><strong>Movie 2: </strong>Heterogeneous nucleation of a ca 8-9 atom cluster near the edge of a nanodroplet supported on a carbon film.</p> <p><strong>Movie 3: </strong>Heterogeneous nucleation of multiple clusters in a nanodroplet. Shortly after nucleation, they coalesce to form disordered nanoclusters.</p> <p><strong>Movie 4:</strong> Heterogeneous nucleation and dissolution cycles of spherical particles in a nanodroplet.</p>
Supplementary data to accompany Information flow, cell types and stereotypy in a full olfactory connectome
<p>Supplemental file 1</p> <p>Layers assigned by the probabilistic graph traversal model. bodyId refers to neurons’ unique ID in ne- uPrint. layer mean contains the mean layer after 10,000 iterations of the main model (Figure 2). layer - olf mean and layer th mean contain the mean layers from running the traversal model with ORNs and THN/HRNs, respectively (Figure S2).</p> <p>S1 hemibrain neuron layers.csv</p> <p>Supplemental file 2</p> <p>Sensory meta-information related to each glomerulus. Columns: glomerulus (canonical name for one of the 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli), laterality (whether the glomerulus receives bilateral or only unilateral innervation from ALRNs), expected cit (a citation that describes the expected number of RNs in this glomerulus), expected RN female 1h (number of expected RNs in one hemi- sphere), expected RN female SD (standard deviation in the expected number of RNs), missing (qualitative assessment of glomeruli truncation), RN frag (if the RNs in that glomerulus are fragmented), receptor (the OR or IR expressed by cognate ALRNs (Bates et al., 2020; Task et al., 2020)), odour scenes (the general ‘odour scene(s)’ which this glomerulus may help signal (Mansourian and Stensmyr, 2015; Bates et al., 2020)), key ligand(the ligand that excites the cognate ALLRN or receptor the most, based on pooled data from multiple studies (Mu ̈nch and Galizia, 2016)), valence (the presumed valence of this odour chan- nel (Badel et al., 2016)). Exists as hemibrain glomeruli summary in our R package hemibrainr.</p> <p>S2 hemibrain olfactory information.csv</p> <p>Supplemental file 3</p> <p>File listing all identified antennal lobe receptor neurons (ALRNs) in the hemibrain, including information shown in neuPrint. See above for column explanations. Exists as rn.info in our R package hemibrainr.</p> <p>S3 hemibrain ALRN meta.csv</p> <p>Supplemental file 4</p> <p>All the hemibrain neurons we have classed as antennal lobe local neurons (ALLNs). See above for column explanations. Exists as alln.info in our R package hemibrainr.</p> <p>S4 hemibrain ALLN meta.csv</p> <p>Supplemental file 5</p> <p>All the hemibrain neurons we have classed as antennal lobe projection neurons (ALPNs). See above for column explanations. In addition, across dataset cluster refers to the clustering with left and right FAFB PNs; is canonical indicates whether that ALPN is one of the well studied “canonical” uPNs. Exists as pn.info in our R package hemibrainr.</p> <p>40</p> <p>S5 hemibrain ALPN meta.csv</p> <p>Supplemental file 6</p> <p>All the hemibrain neurons we have classed as third-order olfactory neurons (TOONs) including lateral horn neurons (LHNs), as well as wedge projection neurons (WEDPNs), lateral horn centrifugal neurons (LHCENT) and other projection neuron classes (Figure 1). See above for column explanations. Exists as ton.info in our R package hemibrainr.</p> <p>S6 hemibrain TOON meta.csv</p> <p>Supplemental file 7</p> <p>All the hemibrain neurons we have classed as neurons that descend to the ventral nervous system (DNs). See above for column explanations. Exists as dn.info in our R package hemibrainr.</p> <p>S8 hemibrain DN meta.csv</p> <p>Supplemental file 8</p> <p>The root point in hemibrain voxel space, for each hemibrain neuron. This is either the location of the soma, or the tip of a severed cell body fibre tract, where possible. Exists as hemibrain somas in our R package hemibrainr.</p> <p>S8 hemibrain root points.csv</p> <p>Supplemental file 9</p> <p>The start points for different neuron compartments. Nodes downstream of this position in the 3D structure of the neuron indicated with bodyid, belong to the compartment type designated by Label. A product of running flow centrality on hemibrain neurons, exists as hemibrain splitpoints in our R package hemi- brainr.</p> <p>S9 hemibrain compartment startpoints.csv</p> <p>Supplemental file 10</p> <p>3D triangle mesh for the hemibrain surface as a .obj file. This mesh was generated by first merging individual ROI meshes from neuPrint and then filling the gaps in between in a semi-manual process. It also exists as hemibrain.surf in our R package hemibrainr.</p> <p>S10 hemibrain raw.obj</p> <p>Supplemental file 11</p> <p>3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALRN presynapses.</p> <p>41</p> <p>Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed).</p> <p>S11 hemibrain AL glomeruli meshes RN-based.zip</p> <p>Supplemental file 12</p> <p>3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALPN presynapses.</p> <p>Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed).</p> <p>These meshes are also available as hemibrain al.surf in our R package hemibrainr. S12 hemibrain AL glomeruli meshes PN-based.zip</p>
Supplementary Data for MOCCASIN: A method for correcting known and unknown confounders in RNA-Seq-based splicing analysis
<p>Contents</p> <ol> <li><strong>moccasin_paper_env.yaml</strong>: conda environment file with R and Python packages and modules needed to reproduce analyses.</li> <li><strong>FigureReproduction.zip</strong>: data and code to reproduce main and supplemental figures.</li> <li><strong>MOCCASIN_ExampleDataset.zip</strong>: A small subset of the simulated data with example code to run MOCCASIN.</li> <li><strong>encode_corrected.zip</strong>: Folder with batch-corrected ENCODE differential splicing quantifications (dPSI).</li> </ol> <p> </p> <p> </p> <p>(1) <strong>moccasin_paper_env.yaml</strong></p> <p>Use the moccasin_paper_env.yaml file to create a conda environment from which all analyses for the paper can be reproduced.</p> <pre><code class="language-bash"># need to first install conda. See here: # https://docs.conda.io/en/latest/miniconda.html # Next, create a conda environment: conda env create --name moccasin_paper_env --file moccasin_paper_env.yaml --force # Activate the environment: conda activate moccasin_paper_env</code></pre> <p><br> The only Python packages not included in this environment are MAJIQ & VOILA. Please see majiq.biocipers.org for installation instructions.</p> <p> </p> <p> </p> <p>(2) <strong>FigureReproduction.zip</strong></p> <p>Within FigureReproduction are folders with code and data to reproduce the main and supplemental figures of the publication. Each folder contains data, script(s) and a README.txt with instructions on how to reproduce figures.</p> <p> </p> <p> </p> <p>(3) <strong>MOCCASIN_ExampleDataset.zip</strong></p> <p>Within this folder is an example dataset to test MOCCASIN. The README.txt file contains detailed line-by-line instructions for how to run MOCCASIN and do post-MOCCASIN analyses. In this example, we show how to run MOCCASIN on a group of .majiq samples with one known confounding effect. Also demonstrated is how to run an "explore unknown residuals" analysis as described in the detailed methods in the supplemental of the paper. </p> <p> </p> <p> </p> <p>(4) <strong>encode_corrected.zip</strong></p> <p>Includes a file called ENCODE_BeforeAndAfterMOCCASIN.voila.tsv.zip which includes LSV quantifications before and after MOCCASIN. Each row in the file represents a junction from an LSV. Each column header starts with the prefix "BeforeMOCCASIN" or "AfterMOCCASIN" and headers ending in dPSI corresponds to the dPSI of an ENCODE knockdown vs control experiment. </p>
Supplementary Materials for "On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective"
<p>Supplementary materials with all used benchmark scripts, plot scripts, benchmark results and PIConGPU example data for the submission to "The 1st International Workshop on Data Reduction for Big Scientific Data (DRBSD-1)" held in conjunction with ISC 2017 in Frankfurt, Germany.</p>
Supplementary data for draft genome of a member of the ascomycotal fungal genus Pseudopithomyces (family Didymosphaeriaceae)
<p><span lang="EN-US">We update our previous draft genome of a member of genus <em>Pseudopithomyces</em> (previously annotated as <em>Pseudopithomyces maydicus</em> strain SBW1, now reannotated as <em>Pseudopithomyces sp</em>. strain SBW1. The new draft genome is based on a hybrid assembly utilising both ONT and Illumina data. The draft genome is comprised of 43 contigs with a total length of 39.65Mbp. We predict 13,669 protein coding gene models, of which 4241 (31%) were annotated to KEGG Orthology. Taxonomic assignment to <em>Pseudopithomyces sp.</em> was supported by comparative analysis of extracted ITS regions, mitochondrial DNA sequence and whole genome comparisons using <em>k</em>-mer sketches. </span></p> <p> </p> <p><span lang="EN-US">The following items of Additional Data Files are made available in this repository:</span></p> <p><span lang="EN-US">Additional Data File 1: contigs.fasta</span></p> <p><span lang="EN-US">FASTA file of entire assembly. </span><span lang="EN-US"> </span></p> <p> </p> <p><span lang="EN-US">Additional Data File 2: draft_genome.fasta</span></p> <p><span lang="EN-US">FASTA file of draft whole genome sequence.</span></p> <p> </p> <p><span lang="EN-US">Additional Data File 3: ITS_full.fasta</span></p> <p><span lang="EN-US">FASTA file containing full length ITS sequences from contig 23 and contig 42.</span></p> <p><span lang="EN-US"> </span></p> <p><span lang="EN-US">Additional Data File 4: 2NJ47W4U013-Alignment.txt</span></p> <p><span lang="EN-US">Text file containing BLASTN alignments for ITS region contained on contig 23</span></p> <p><span lang="EN-US"> </span></p> <p><span lang="EN-US">Additional Data File 5: 2NJXH2GE013-Alignment.txt</span></p> <p><span lang="EN-US">Text file containing BLASTN alignments for ITS region contained on contig 42</span></p> <p><span lang="EN-US"> </span></p> <p><span lang="EN-US">Additional Data File 6: 2NM79EUG016-Alignment.txt</span></p> <p><span lang="EN-US">Text file containing BLASTN alignments for the mitochondrial genome from contig 40 </span></p> <p><span lang="EN-US"> </span></p> <p><span lang="EN-US">Additional Data File 6: sourmash_bc10_hy_pm1_3.txt</span></p> <p><span lang="EN-US">Text file containing the MASH similarities of the draft genome compared to 18,883 fungal genomes.</span></p>
Supplementary data to: "A European Monsoon-like climate in a Warmhouse World"
<p>Supplementary information belonging to the manuscript titled "A European Monsoon-like climate in a Warmhouse World" by Nick van Horebeek and colleagues, containing the following information:</p> <ul> <li>"Campanile_D47_sample_data_calc.csv" - A file containing all clumped isotope measurements carried out for this study</li> <li>"Campanile_D47_season_data_calc.xlsx" - A file containing seasonal means and uncertainties of temperature and d18Osw based on clumped isotope measurements carried out for this study</li> <li>"Campanile_d18O_season_data_calc.csv" - A file containing all incrementally sampled oxygen and carbon isotope values with seasonal characterization.</li> <li>"Intra-growthline_variability_edit.png" - An image showing the variability in d18O values repeatedly measured in the same location in the shell</li> <li>"Campanile_Winter_growth_stop_images.zip" - A folder containing all shell images used in the publication</li> <li>"Campanile_Data_figure_S1.xlsx" - A file containing the dataset needed to produce the supplementary figure showing variability in oxygen isotope values (S1).</li> <li>"SI_Campanile_d18O_d13C_depth_rev1.png" - A plot showing the variability in d18O and d13C values along the shell</li> <li>"Campanile_clumped_season_plot_rev3.r" - Script used to process clumped isotope data for seasonal statistics and plotting</li> <li>"Campanile_clumped_d18O_plots_rev2.r" - Script used to process and plot seasonal statistics and isotope data + uncertainty against shell age.</li> </ul>
Strong sequence dependence in RNA/DNA hybrid strand displacement kinetics supplementary data and code
<p>Supplementary data and code needed to replicate figures and results for the paper: Strong sequence-dependence in RNA/DNA hybrid strand displacement kinetics - Francesca G. Smith, John P. Goertz, Molly M. Stevens and Thomas E. Ouldridge. README is included to explain each folder and file in the repository.</p>
[Supplementary Information] Can LCA be FAIR? – Assessing the status quo and opportunities for FAIR data sharing
<p>This is the supplementary information related to a the manuscript - 'Can LCA be FAIR?' - Assessing the status quo and opportunities for FAIR data sharing. The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR data principles (Findability, Accessibility, Interoperability and Re-use).</p><p>The supplementary information consists of three files:</p><p><strong>SI 1</strong> - How the life cycle inventory is shared in relation to the FAIR data principles in 25 peer reviewed LCA journal articles between 2018 -2022.</p><p><strong>SI 2</strong> - Review of ten data management plans of EU Horizon Europe projects in relation to LCA to assess the recommendations on the implementation of FAIR principles.</p>
Supplementary data for the article: Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges
<p>This repository provides the supplementary data to the paper titled <a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener"><em>"Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges"</em></a>, published 2024 in <em>Resources, Conservation and Recycling</em>.</p> <h4><strong>Contents</strong></h4> <p>The repository is split in 3 parts and comprises the following files (more details are provided in the <em>README.md</em>):</p> <p><strong>A_Database of reviewed studies:</strong></p> <ul> <li>contains the detailed review data, meant for readers to use as an overview file to gather studies relevant to them. It also includes an overview of all data sources that the reviewed studies used.</li> </ul> <p><strong>B_Scientific supplement to paper:</strong></p> <ul> <li>Contains all data relevant to the related publication Harpprecht et al. (2024), such as studies screened , FAIR data analysis, or analyzed impact trends.</li> </ul> <p><strong>C_Data for figures in paper:</strong></p> <ul> <li>This file contains all the data for Figures 3, 4 and 5 in tabular form, representing impact trends, scenario variables, scenario modelling approaches and data sources used.</li> </ul> <h4><strong>Summary</strong></h4> <p>These files allow to reproduce the results of our study. In this work, we systematically reviewed studies which assessed future environmental impacts of metal supply chains. Our review yielded 40 publications covering 15 metals: copper, iron, aluminium, nickel, zinc, lead, cobalt, lithium, gold, manganese, neodymium, dysprosium, praseodymium, terbium, and titanium. We evaluated their results regarding future impact trends, and their methods, i.e., modelling approaches, scenario variables, and data sources of scenario variables. We identified 15 scenario variables. The most common variables are background electricity mix, ore grade, recycling shares, demand, and energy efficiency. We identified 229 unique data sources for the reviewed scenario variables.</p> <h4><strong>Related publication</strong></h4> <p>More details on the data and its interpretation as well as the scientific context are provided in the publication itself:</p> <p><a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener">Harpprecht, C., Miranda Xicotencatl, B., van Nielen, S., van der Meide, M., Li, C. , Li, Z., Tukker, A., Steubing, B. (2024). <em>Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges.</em> Resources, Conservation and Recycling.</a></p> <h4><strong>Funding </strong></h4> <p>Carina Harpprecht received funding from the Energy Program of the German Aerospace Center in 2022. Zhijie Li received funding from the European Institute of Innovation and Technology (EIT) under the project Valomag (Project No. 14049).</p> <h4><strong>License</strong></h4> <p>CC-BY 4.0 license for DLR (German Aerospace Center)</p>
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p><p>This dataset provides supplementary data to "A reactive neural network framework for water-loaded acidic zeolites". It contains trained Neural Network Potentials (NNP and ΔNNP model), scripts, and all energy and force data used in this work at the (Δ)NNP, ReaxFF, and DFT (SCAN+D3(BJ) and ωB97X-D3(BJ)) level. Energy and forces are stored as ASE trajectory files (traj), readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOH.db) file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>.</p><ol><li>"aimd_simulations.zip" - VASP INCAR file, XDATCAR and traj file for 10 ps AIMD run (Supplementary Figure 6) and NNP level (re-)calculated energies/forces ("aimd_nnp_recalc.traj")</li><li>"biased_dynamics.zip" - VASP/Plumed input and output files for DFT (SCAN+D3(BJ)) and NNP level biased dynamics including traj files (Supplementary Figure 12)</li><li>"database_input.zip" - structure (cif) files of the initial structures used for database generation (Supplementary Table 1)</li><li>"delta_nnp.zip" - (pytorch) ΔNNP model (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>) together with example scripts </li><li>"error_stats.zip" - traj files of all generalization tests (Figure 1 and Supplementary Figure 4) storing energies/forces at the SCAN+D3(BJ), ReaxFF, and NNP level as well as traj files with ΔNNP and ωB97X-D3(BJ) energies/forces for a subset taken from biased dynamics runs (Supplementary Figure 11)</li><li>"md_simulations.zip" - NNP level MD trajectories of all generalization test (Figure 1 and Supplementary Figure 4) runs including an example script for an MD run</li><li>"neb_calculations.zip" - traj files and example scripts for NEB calculations at the (Δ)NNP along with the corresponding DFT energy/force data (SCAN+D3(BJ) and ωB97X-D3(BJ))</li><li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li><li>"silica_database.zip" - output files of the single-point (SP) and optimization test runs (Supplementary Figure 1) of pure silica structures together with an example structure optimization script </li><li>"SiAlOH.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li></ol>
Supplementary Materials for "Accelerating data sharing and re-use in volume electron microscopy"
<p>The deposition contains supporting materials for "Accelerating data sharing and re-use in volume electron microscopy" Comment</p> <ul> <li>Sample preparation protocol for cell monolayers optimized for serial block face scanning electron microscopy</li> <li>Supporting movies showing models of biological specimens imaged using volume electron microscopy</li> </ul>
Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration
<p>Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration</p> <p>Data contains the georeferenced map plate of our geologic map that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the Planetary Science Journal publication and the Zenodo dataset.</strong></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>The Planetary Science Journal</em>, <em>5</em>(6), 147. <a href="https://iopscience.iop.org/article/10.3847/PSJ/ad2c04">https://iopscience.iop.org/article/10.3847/PSJ/ad2c04</a></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Supplementary Data for Wueller et al. (2024): Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>Zenodo Dataset</em>. <a href="https://doi.org/10.5281/zenodo.10693820" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10693820</a></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Mapping Scale is 1:100,000</p> <p>Print Scale is 1:1,000,000</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact lwueller@uni-muenster.de</p> <p>Lukas Wueller, Institut für Planetologie, Universität Münster, Germany, June 2024</p>
Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
Patterns of object play behaviour and its functional implications in free-flying ravens (supplementary data)
<p>This resource contains the processed data sets and R scripts associated with the article titled "Patterns of object play behaviour and its functional implications in free-flying ravens" authored by Awani Bapat, Anna E Kempf, Salome Friry, Palmyre H Boucherie, Thomas Bugnyar, published in Scientific Reports on 02-01-2025.</p>
Mixed DG-FEM for the Darcy-Brinkman-Stokes model: supplementary simulation data
<p>This dataset contains simulation results used in the publication<em> "Stable across regimes: A mixed DG method for Darcy-Brinkman-Stokes type flows"</em>. Detailed descriptions of the individual cases can be found in the paper.</p> <p>The simulation outputs are enriched with the respective inputs used to set up the finite element simulations. Setups include definition of the mesh (sizes), material and numerical parameters. Setups are given as Python for scripted inputs (e.g. function definitions) and human-readable <em>.yaml</em> files for simple parameters. <br><br>Simulation outputs are written in paraview .vtk and .vtu files, which are contained in the <em>outputs/MODEL_NAME/paraview</em> folder of the respective simulation. <em>MODEL_NAME</em> corresponds to the model. See also the <em>readme.md.</em></p> <p>The additional folder <em>figure_collection</em> contains the raw result plots from the publication, along with the respective simulation inputs used to obtain the figure.</p>
Supplementary Data to Kinetic oxygen isotope fractionation between water and aqueous OH- during hydroxylation of CO2
<p>In this dataset, we provide analytical data to <strong>Kinetic oxygen isotope fractionation between water and aqueous OH<sup>-</sup> during hydroxylation of CO<sub>2</sub></strong> by Bajnai and Herwartz (2021). Also deposited here is the R code that was used to generate the figures in the manuscript.</p>
ScienceDex guides
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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.