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2,318 results for “Synthesis”
20-Year Synthesis of Soil Respiration Data at Harvard Forest 1991-2008
All data on soil carbon flux (“soil respiration”) collected using chamber-based methods at Harvard Forest from a range of observational and experimental plots were collated, their units were harmonized, and geographic (locations) and environmental characteristics (soil series, drainage class, soil temperature, soil moisture, vegetation type, etc.) were identified for each observation. This yielded a dataset with 106,192 observations of soil respiration taken between 1991 and 2008. These data provide a unique resource for exploring spatial and temporal patterns in soil respiration in a range of common New England forest types. For the Giasson, et al. (2013) publication, we also used 24 site-years of eddy covariance measurements from two Harvard Forest sites (EMS and Hemlock towers) to examine the relationship between soil and ecosystem respiration. Here, we present all derived/synthetic datasets associated with the manuscript. M.-A. Giasson, A. M. Ellison, R. D. Bowden, P. M. Crill, E. A. Davidson, J. E. Drake, S. D. Frey, J. L. Hadley, M. Lavine, J. M. Melillo, J. W. Munger, K. J. Nadelhoffer, L. Nicoll, S. V. Ollinger, K. E. Savage, P. A. Steudler, J. Tang, R. K. Varner, S. C. Wofsy, D. R. Foster, and A. C. Finzi 2013. Soil respiration in a northeastern US temperate forest: a 22-year synthesis. Ecosphere 4:art140. http://dx.doi.org/10.1890/ES13.00183.1
Synthesis of Sarracenia Research in North America 1982-2018
This archive includes the raw data and code (R scripts) required to reproduce all of the analyses and figures in the book Scaling Sarracenia: Ecology of a Model System by Aaron M. Ellison and Nicholas J. Gotelli. The book is a synthesis of our 25 years of research on the northern (a.k.a. purple) pitcher plant Sarracenia purpurea viewed through the conceptual lens of “scale”: scaling ecological phenomena through levels of biological organization with a model organism, across time and space, and using macroecological relationships.
Synthesis of Hemlock Removal Experiment at Harvard Forest 2003-2019
In 2023, we synthesized the data from 6 Harvard Forest Hemlock Removal Experiment datasets (HF054, HF106, HF107, HF125, HF126 and HF161) to discern differences between the girdling and logging treatments from 2004-2019. Forest insect outbreaks cause large changes in ecosystem structure, composition, and function. Humans often respond to insect outbreaks by conducting salvage logging, which can amplify the immediate effects, but it is unclear whether logging will result in lasting differences in forest structure and dynamics when compared with forests affected only by insect outbreak. We used 15 years of data from an experimental removal of Tsuga canadensis (L.) Carr. (Eastern hemlock), a foundation tree species within eastern North American forests, and contrasted the rate, magnitude, and persistence of response trajectories between girdling (emulating mortality from insect outbreak) and timber harvest treatments.
Helical dinuclear 3d metal complexes with bis(bidentate) [S,N] ligands: synthesis, structural and computational studies
<h1>Raw data for the publication entitled:</h1> <h2>Helical dinuclear 3d metal complexes with bis(bidentate)<br>[S,N] ligands: synthesis, structural and computational<br>studies</h2> <p><em>Dalton Transactions</em>, <strong>2024</strong>, DOI: 10.1039/D4DT02395A</p> <p>Authors:<br>Jamie Allen, Jörg Saßmannshausen, Kuldip Singh, Alexander F. R. Kilpatrick*</p> <p>These folders contain the raw data which were used to prepare the above publication.</p> <h1>Information regarding the raw files of the DFT calculations.</h1> <p>The zip-files in this section containing the raw-data of the DFT calculations leading to the Zn, Co and Fe calculated structures. As filenames are notoriously bad in handling special characters, the names of the folder appear different from what is being used in the final publication. We try to provide as much information as possible to facilitate the usage of these results.</p> <p>Thus:</p> <table> <tbody> <tr> <th>Abbreviation publication</th> <th>Abbreviation folder</th> <th>Abbreviation filename</th> </tr> </tbody> <tbody> <tr> <td>[Zn(<strong>3</strong>)<sub>2</sub>]</td> <td>Zn3-2</td> <td>SNdipp2Zn</td> </tr> <tr> <td>[Co(<strong>3</strong>) <sub>2</sub>]</td> <td>Co3-2</td> <td>SNdipp2Co</td> </tr> <tr> <td>[Fe(<strong>3</strong>) <sub>2</sub>]</td> <td>Fe3-2</td> <td>SNdipp2Fe</td> </tr> <tr> <td>[Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Zn2-2</td> <td>zn2</td> </tr> <tr> <td>[Co<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Co2-2</td> <td>co2</td> </tr> <tr> <td>[Fe<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Fe2-2</td> <td>fe2</td> </tr> </tbody> </table> <p>Some test calculations were performed as well utilizing Gaussian-09. They can be found in a folders with the suffix <em>-G09</em> or <em>-g09</em>.</p> <p>The closed shell compound [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] was investigated further. In order to look into the influence of the used Grimme dispersion correction, we re-calculated the final result without that correction. These files are in the Zn2-2-pbe0 folder. Furthermore, we used [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] and removed one of the Zn atoms and replaced the dangling bonds with H. We then fully optimized that structure. The results are in the Zn2-2-cut folder.</p> <h1> </h1> <h1>Information regarding the raw characterisation data</h1> <p>The raw characterisation data files for all nuclear magnetic resonance (NMR) spectroscopy, infrared (IR) spectroscopy, cyclic voltammetry (CV), single crystal X-ray diffraction (XRD) and solution magnetometry studies are enclosed in separate .zip files.</p>
MacroSheds: a synthesis of long-term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies
The MacroSheds dataset is an ongoing synthesis of data records from small-watershed ecosystem studies, including those managed by LTER, CZO/CZNet, NEON, and many other networks. While details of instrumentation and sampling methods vary across these studies, the types of data collected and the questions that motivate their analysis are remarkably similar. Nevertheless, little effort toward the compilation of these datasets has previously been made, and comparative watershed analyses have remained limited in scale. The MacroSheds dataset includes daily time series of streamflow (discharge) and stream chemistry, as well as precipitation and precipitation chemistry where available. Each of the 200+ watersheds included in the MacroSheds dataset is described by a comprehensive collection of watershed attributes, summarized from a diverse set of gridded data products. A subset of these watershed attributes conform as closely as possible to the specifications of the CAMELS dataset (https://ral.ucar.edu/solutions/products/camels), allowing the MacroSheds dataset to function as a small-watershed supplement to that corpus, and a resource for hydrologists as well as biogeochemists and watershed ecosystem scientists. Data paper: https://aslopubs.onlinelibrary.wiley.com/doi/full/10.1002/lol2.10325 Data dashboard for visualization: macrosheds.org R package for data access and analysis: https://github.com/MacroSHEDS/macrosheds R package vignettes: https://macrosheds.org/pages/vignettes Live dataset changelog: https://macrosheds.org/pages/changelog.html Questions: mail@macrosheds.org
Global dataset of nitrogen fixation rates across inland and coastal waters based on a coordinated synthesis effort
Biological nitrogen fixation converts inert di-nitrogen gas into bioavailable nitrogen and can be an important source of bioavailable nitrogen to organisms. This dataset synthesizes the aquatic nitrogen fixation rate measurements across inland and coastal waters. Data were derived from papers and datasets published by April 2022 and include rates measured using the acetylene reduction assay (ARA), 15N2 labeling, or the N2/Ar technique. The dataset is comprised of 4793 nitrogen fixation rates measurements from 267 studies, and is structured into four tables: 1) a reference table with sources from which data were extracted, 2) a rates table with nitrogen fixation rates that includes habitat, substrate, geographic coordinates, and method of measuring N2 fixation rates, 3) a table with supporting environmental and chemical data for a subset of the rate measurements when data were available, and 4) a data dictionary with definitions for each variable in each data table. This dataset was compiled and curated by the NSF-funded Aquatic Nitrogen Fixation Research Coordination Network (award number 2015825).
Dataset of "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"
<p>Novel simple and efficient method for synthesis of high entropy sulfides of iron group metals (Cr, Fe, Ni, Co, Zn) is describedThe created material was investigated as a catalyst for electrochemical water splitting in acidic, neutral and alkaline pH. Investigation of the electrocatalytic activity of the synthesized material shows its high efficiency for overall water splitting in alkaline media. </p>
Dataset of "Asparagine-Modified Magnetic Graphene Oxide: An Efficient and Green Nanocatalyst for Synthesis of 5-oxodihydropyrano[3,2-c]chromenes and dihydropyrano[2,3- c]pyrazole derivatives and the Density functional theory calculation".
<p>The primary focus of this study involved the fabrication of a novel nanocatalyst Fe3O4-supported asparagine functionalized graphene oxide (Fe3O4@GO-N-(Asparagine)). The catalyst was synthesized through a four-step procedure.</p>
Data set for Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types
<p>Dataset used in the publication: " Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types". The dataset gathers estimates of ecosystem standing stocks (biomass, organic carbon, detritus), fluxes (GPP, ER, NEP) and process rates (decomposition and carbon uptake rates) for eight broad ecosystem types (forest, grassland, agroecosystem, desert, stream, lake, pelagic and benthic marine ecosystems) in five broad climatic zones (arctic, boreal, arid, temperate, tropical, arid).</p> <p>The scripts to produce the figures and the statistics of the publication are released along with the txt version of the data, which file is uploaded when running the script.</p>
Overview of the time series in the PALMOD 130k marine palaeoclimate data synthesis
<p>Palaeoclimate time series in the PALMOD 130k marine palaeoclimate data synthesis v1.0.1. This table lists the site names and location, parameters including additional information as well as the source of the data and the original publications where the data were presented.</p>
Diffraction images used to solve the structures published in the article "From 1,4-Disaccharide to 1,3-Glycosyl Carbasugar: Synthesis of a Bespoke Inhibitor of Family GH99 Endo-α-mannosidase"
<p>Raw diffraction images used for generating the structures published in the article "From 1,4-Disaccharide to 1,3-Glycosyl Carbasugar: Synthesis of a Bespoke Inhibitor of Family GH99 Endo-α-mannosidase" (available <a href="https://doi.org/10.1021/acs.orglett.8b03260">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries. An additional 720 degree dataset is provided, which has been collected from the same crystal as PDB 6HMH. This dataset has not been used to solve the structure presented in the paper. It works very well as an example of sulfur SAD phasing.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Binaural room scanning files for sound field synthesis localization experiment
<p>Binaural room scanning files that were used together with the SoundScape Renderer to perform the localization experiments described in Wierstorf [1].</p> <p>The results of the corresponding listening experiments are summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening test results for sound field synthesis localization experiment
<p>Result files from the the localization experiments described in section 5.1 of Wierstorf [1].</p> <p>The results are visually summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Combined continuous nanoparticle synthesis with chromatographic size classification
<p>In this paper, we report a combination of the continuous flow synthesis of gold nanoparticles (AuNPs) with subsequent purification and narrowing of the particle size distribution (PSD) by size-exclusion chromatography (SEC) by adapting the flow rates of synthesis and classification. First, we show scalability of chromatographic classification with respect to column dimension and the absence of irreversible nanoparticle adhesion on the column material. Two different syntheses lead to a large and widely distributed and a small and narrowly distributed AuNP dispersion, which are classified by a semipreparative column. The PSDs of individual fractions are characterized by analytical SEC. The broadly distributed AuNP dispersion was classified into three fractions with distinct PSDs. For the narrowly distributed AuNPs, the separation is almost independent of the mobile phase flow rate: coarse and fine fractions with almost identical PSDs and separation efficiency curves are observed irrespective of the flow rate. Even NP samples with narrow PSDs can be classified into multiple fractions with tailored PSDs while simultaneously removing dissolved impurities from the dispersion. With our study, we demonstrate the potential of a direct combination of continuous NP synthesis with chromatographic classification for the optimization of final PSDs and the simultaneous purification of nanoparticulate dispersions.</p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)– Project-ID 416229255 – SFB 1411</p>
Dataset of "Synthesis and Characterization of Soluble Pyridinium Containing Copolyimides"
<p>Anion selective polymer membrane based on the copolyimides of ionene based on ODPA, BIS P and DAP were synthetized. Copolyimides were prepared by thermal imidization followed by quaternization. Characterisation by FTIR, NMR, SEM, EDX , TGA, DSC and EIS were performed. It was shown that content of the DAP in the membrane has significant effect on the stability of the membrane.</p>
Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals
<p><strong>Supplementary Material for:</strong></p> <p>Emerling C.A., Springer M.S., Gatesy J., Jones Z., Hamilton D., Xia-Zhu D., Collin M.A., and Delsuc F. (2021). Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals.<strong><em> Open Research Europe</em></strong> 1:75. doi:10.12688/openreseurope.13795.1.</p> <p> </p> <p><strong>Supplementary File Legends:</strong></p> <p><strong>- Supplementary_Figure_S1.pdf:</strong> <em>AANAT</em> PAML ‘master model’ showing branch categories, corresponding to “Model 1: 24 ratio” in Supplementary Table S7.</p> <p><strong>- Supplementary_Figure_S2.pdf:</strong> <em>ASMT</em> PAML ‘master model’ showing branch categories, corresponding to “Model 2: 24 ratio” in Supplementary Table S8.</p> <p><strong>- Supplementary_Figure_S3.pdf:</strong> <em>MTNR1A</em> PAML ‘master model’ showing branch categories, corresponding to “Model 1: 27 ratio” in Supplementary Table S9.</p> <p><strong>- Supplementary_Figure_S4.pdf:</strong> <em>MTNR1B</em> PAML ‘master model’ showing branch categories, corresponding to “Model 1: 46 ratio” in Supplementary Table S10.</p> <p><strong>- Supplementary_Figure_S5.pdf:</strong> RAxML <em>AANAT</em> gene tree. Numbers at nodes correspond to bootstrap support values.</p> <p><strong>- Supplementary_Figure_S6.pdf: </strong>RAxML <em>ASMT</em> gene tree. Numbers at nodes correspond to bootstrap support values.</p> <p><strong>- Supplementary_Figure_S7.pdf: </strong>RAxML <em>MTNR1A</em>+<em>MTNR1B</em> tree. Numbers at nodes correspond to bootstrap support values.</p> <p><strong>- Supplementary_Figure_S8.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> exon 2 in cetaceans. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S9.pdf: </strong>Supporting data showing the inactivation of <em>ASMT</em> in spalacids and <em>Fukomys damarensis</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S10.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in hyracoids and <em>Cyclopes didactylus</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S11.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in sirenians. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S12.pdf: </strong>Supporting data showing the inactivation of <em>AANAT</em> in sirenians and a polymorphic premature stop codon in exon 5 of <em>ASMT</em> in <em>Trichechus manatus</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S13.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in <em>Condylura cristata</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S14.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in <em>Phataginus tricuspis</em>. Read Supplementary Table S14 for further details.</p> <p><strong>- Supplementary_Figure_S15.pdf: </strong>PAML <em>AANAT</em> results, Model 1: 24 ratio (see Supplementary Table S7).</p> <p><strong>- Supplementary_Figure_S16.pdf: </strong>PAML <em>ASMT</em> results, Model 2: 24 ratio (see Supplementary Table S8).</p> <p><strong>- Supplementary_Figure_S17.pdf: </strong>PAML <em>MTNR1A</em> results, Model 1: 27 ratio (see Supplementary Table S9).</p> <p><strong>- Supplementary_Figure_S18.pdf: </strong>PAML <em>MTNR1B</em> results, Model 1: 46 ratio (see Supplementary Table S10).</p> <p><strong>- Supplementary_Table_S1.xlsx: </strong>List of species examined in this study and the sources of the genes. Source key: WGS: Sequences derived from NCBI's Whole Genome Shotgun database, with accession prefix provided; Whole Genome Sequencing of Short Reads: whole genomes were sequenced using short-read technologies. The methodologies varied for the species, and will be or have been published with other projects, so please contact the author(s) for information on the specific methodology and samples used (Xenarthrans, <em>Proteles cristatus</em>, <em>Otocyon megalotis</em>: Frédéric Delsuc, e-mail: Frederic.Delsuc@umontpellier.fr; Crocodylians: John Gatesy, e-mail: jgatesy@amnh.org; <em>Dugong dugon</em>: Mark Springer, e-mail: mark.springer@ucr.edu; SRA: sequences derived from NCBI's Sequence Read Archive; GenBank: sequences derived from NCBI's nucleotide collection; Bowhead Whale Genome Resource: sequences derived from http://www.bowhead-whale.org; Ensembl: sequences derived from Ensembl genome browser (www.ensembl.org)l; Discovar de novo: sequences derived genomes assembled via Discovar de novo (<a href="https://software.broadinstitute.org/software/discovar/blog/">https://software.broadinstitute.org/software/discovar/blog/</a>). Coverage: indicates coverage of the whole genome (reported in NCBI or other source) or individual genes (derived from short read mapping). Scaffold and contig N50: reported in NCBI or other source.</p> <p><strong>- Supplementary_Table_S2.xlsx: </strong>Accession numbers and functionality of <em>AANAT</em> in species examined. If Accession # indicated as “New”, sequence generated for this study and can be found in Supplementary Dataset S1. Parentheses after accession number indicates coordinates for sequence on the contig / scaffold. Exon colors code for the following: green = putatively functional; yellow = missing (e.g., negative BLAST results, negative mapping results); pink = one or more inactivating mutations found. Abbreviations for mutations are as follows: del = deletion; ins = insertion; start = start codon mutation; stop = premature stop codon; ? = ambiguity whether the mutation is shared among all members of the clade. Abbreviations in brackets following an inactivating mutation indicate shared inactivating mutation. Key for each abbreviation follows: Bacu = <em>Balaenoptera acutorostrata</em>; BALA = Balaenidae; BALAEN = Balaenopteridae; Bbon = <em>Balaenoptera bonaerensis</em>; CAB = <em>Cabassous</em>; Ccap = <em>Cebus capucinus</em>; CETA = Cetacea; CHLAM = Chlamyphoridae; CHOL = <em>Choloepus</em>; Cjac = <em>Callithrix jacchus</em>; CING = Cingulata; DASY = Dasypodidae; DELP = Delphinidae; DERM = Dermoptera; Erob = <em>Eschrichtius robustus</em>; INIA = <em>Inia</em>; FOLI = Folivora; GALE = <em>Galeopterus</em>; LIPO = <em>Lipotes</em>; Lobl = <em>Lagenorhynchus obliquidens</em>; MANI = Manidae; MONO = Monodontidae; MYRM = Myrmecophagidae; MYST = Mysticeti; NPP = Not present in <em>Platanista</em> or Physeteroidea, but present in other Odontocetes; NPZ = Not present in Ziphiidae, but present in other Odontocetes; Oorc = <em>Orcinus orca</em>; PEUT = Tolypeutinae; PHOC = Phocoenidae; PHOL = Pholidota; PHOR = Chlamyphorinae; PILO = Pilosa; PHYS = Physeteroidea; PONT = <em>Pontoporia</em>; Schi = <em>Sousa chinensis</em>; SIRE = Sirenia; Tadu = <em>Tursiops aduncus</em>; TOLY = <em>Tolypeutes</em>; VERM = Vermilingua; XEN = Xenarthra.</p> <p><br> <strong>- Supplementary_Table_S3.xlsx: </strong>Accession numbers and functionality of <em>ASMT</em> in species examined. See Table S2 caption for details.</p> <p><strong>- Supplementary_Table_S4.xlsx: </strong>Accession numbers and functionality of <em>MTNR1A</em> in species examined. See Table S2 caption for details.</p> <p><strong>- Supplementary_Table_S5.xlsx: </strong>Accession numbers and functionality of <em>MTNR1B</em> in species examined. See Table S2 caption for details.</p> <p><strong>- Supplementary_Table_S6.xlsx: </strong>Codon frequency model selection. These are the results from one ratio dN/dS analyses using different codon frequency models. AIC = Akaike Information Criterion.</p> <p><strong>- Supplementary_Table_S7.xlsx: </strong>Results of <em>AANAT</em> PAML dN/dS analyses for mammals. Model: BG = branch(es) grouped with background; fixed 1 = branch(es) fixed at 1. p’-value: p-value after Holm-Bonferroni correction for multiple testing. Model Comparison: if model comparison yields statistically significant differences (p < 0.05), model comparison bolded and given green background; if model comparison is still significant after Holm-Bonferroni correction, asterisk (*) added. For most models, w only shown for branch(es) of interest. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S1.</p> <p><strong>- Supplementary_Table_S8.xlsx: </strong>Results of <em>ASMT</em> PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S2.</p> <p><strong>- Supplementary_Table_S9.xlsx: </strong>Results of <em>MTNR1A</em> PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S3.</p> <p><strong>- Supplementary_Table_S10.xlsx: </strong>Results of <em>MTNR1B</em> PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S4.</p> <p><strong>- Supplementary_Table_S11.xlsx: </strong>Results of PAML analyses for sauropsids.</p> <p><strong>- Supplementary_Table_S12.xlsx: </strong>Results of BLASTing and mapping short reads from <em>Alligator mississippiensis</em> RNA sequencing experiments.</p> <p><strong>- Supplementary_Table_S13.xlsx: </strong>Supporting data for validating putative inactivating mutations. Validating data came from four general sources of information: mutations shared by more than one species within a clade, mutations shared by two sources of sequencing data for the same species, mutations validated by coverage of mapped short reads and statistically elevated dN/dS ratio estimates. For additional details, see Supplementary Tables S2–S5 and S7–S10, as well as Figure 2 and Supplementary Figures S8–S18.</p> <p><strong>- Supplementary_Dataset_S1.txt:</strong><strong> </strong>Genomic alignments in fasta format used to determine the pseudogene/functional status of all four melatonin genes in different taxonomic groups.</p> <p><strong>- Supplementary_Dataset_S2.txt:</strong><strong> </strong>Alignment of <em>AANAT</em> in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML. </p> <p><strong>- Supplementary_Dataset_S3.txt: </strong>Alignment of <em>ASMT</em> in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML. </p> <p><strong>- Supplementary_Dataset_S4.txt: </strong>Alignment of <em>MTNR1A</em> and <em>MTNR1B</em> in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML. </p> <p><strong>- Supplementary_Dataset_S5.txt:</strong><strong> </strong>Codon alignments of <em>AANAT</em> used in selection pressure analyses with PAML. </p> <p><strong>- Supplementary_Dataset_S6.txt: </strong>Codon alignments of <em>ASMT</em> used in selection pressure analyses with PAML.</p> <p><strong>- Supplementary_Dataset_S7.txt:</strong><strong> </strong>Codon alignments of <em>MTNR1A</em> used in selection pressure analyses with PAML.</p> <p><strong>- Supplementary_Dataset_S8.txt: </strong>Codon alignments of <em>MTNR1B</em> used in selection pressure analyses with PAML.</p> <p><strong>- Supplementary_Dataset_S9.txt: </strong>Tree topologies in newick format used in selection pressure analyses with PAML.</p>
Dataset of the publication "Halide Perovskites as Disposable Epitaxial Templates for the Phase-Selective Synthesis of Lead Sulfochloride Nanocrystals"
<p>This dataset provides the raw data associated with the publication "Halide Perovskites as Disposable Epitaxial Templates for the Phase-Selective Synthesis of Lead Sulfochloride Nanocrystals".It contains:</p> <ul> <li>A readme file meant to help the user navigate the database</li> <li>The raw data associated with all the plots and charts found in the Main Text and in the Supplementary information.</li> <li>The raw data collected during the 3D electron diffraction experiments on Pb<sub>3</sub>S<sub>2</sub>Cl<sub>2</sub> Nanocrystals. </li> <li>The CIF files of all the crystal structures refined in the work</li> <li>An atomistic model of the Pb<sub>4</sub>S<sub>3</sub>Cl<sub>2</sub>/CsPbCl<sub>3</sub> interface, which can be visualized with the freeware software Vesta. </li> </ul>
Synthesis, Structure and Redox Properties of Single-atom Bridged Diuranium Complexes Supported by Aryloxides
<p>This upload contains raw data (NMR, X-Ray Diffraction, Electrochemistry, SQUID and Elemental Analysis) files for the article</p>
Graphic Illustration of our Digital Collections Data and Tracking Disease Workshop Session: Discussion and Synthesis
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Discussion section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Workshop Discussion Synthesis: A Public Health Perspective
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded and synthesized the key public health themes from our participant discussion session at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.