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Ultraviolet-visible spectroscopy absorbances for dissolved organic matter from Lake Mendota from June – November 2017
Dissolved organic matter (DOM) is a complex mixture of organic compounds found in all natural waters. Its composition affects its reactivity towards numerous processes. Its composition is a function of both its source (e.g., allochthonous or autochthonous) as well as the extent of environmental processing it has undergone (e.g., chemical or biological degradation). Ultraviolet-visible (UV-vis) spectroscopy is an analytical technique commonly used to assess the composition of dissolved organic matter in water samples. Here, we present spectra from Lake Mendota samples collected from June - November in 2017 at the surface of Lake Mendota as well as at specific depths within the water column. All samples were collected near the NTL-LTER research buoy. Absorbance values are listed for wavelengths 200 - 800 nm for each sample.
Organic Matter of Seagrass Sediment in Virginia Coastal Bays 2007-2021
This data set contains measurements of sediment organic matter and bulk density from plots in the restored Z. marina meadows in Hog Island Bay and South Bay, VA. Samples were collected annually in June-July. GPS locations of sampling plots are available in the companion data set VCR11180.
Particulate organic carbon and particulate organic nitrogen concentrations and stable isotope composition of seawater sampled during the Antarctic Circumnavigation Expedition (ACE) during the Austral Summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains particulate organic carbon and particulate organic nitrogen concentrations and stable isotope composition (delta 13C and delta 15N) sampled during the Antarctic Circumnavigation Expedition (ACE) Leg 1-3. Water samples were collected from the underway seawater supply every 3 hours, filtered onto pre-combusted glass fibre filters, acidified to remove inorganic compounds and analysed for both elements on the same filter using an elemental analyser. These samples provide an estimate of the organic carbon and organic nitrogen concentration and carbon and nitrogen stable isotope composition of living and detrital particles > 0.7 micrometres in size.</p> <p><strong>Dataset contents</strong></p> <ul> <li>README.txt, metatdata, text</li> <li>data_file_header.txt, metadata, text</li> <li>ace_uw_poc_pon_blanks_20200512CURRSGCMR.csv, data file, comma-separated values</li> <li>ace_uw_poc_pon_20200512CURRSGCMR.csv, data file, comma-separated values</li> </ul>
Dataset to Manuscript: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Marcus Schiedung et al. (Biogeosciences)
<p>Dataset to manuscript: Schiedung, M., Bellè, S.-L., Sigmund, G., Kalbitz, K., and Abiven, S.: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Biogeosciences, https://doi.org/10.5194/bg-17-6457-2020, 2020.</p> <p>All parameters and variables are described in "Var_names" files.</p>
Dataset of "Tuning the morphology and energy levels in organic solar cells with metal- organic framework nanosheets"
<p>Metal-organic framework nanosheets (MONs) have proved themselves to be useful<br>additives for enhancing the performance of a variety of thin film solar cell devices. However,<br>to date only isolated examples have been reported. In this work we take advantage of the<br>modular structure of MONs in order to resolve the effect of their different structural and<br>optoelectronic features on the performance of organic photovoltaic (OPV) devices. Three<br>different MONs were synthesized using different combinations of two porphyrin-based ligands<br>meso-tetracarboxyphenyl porphyrin (TCPP) or tetrapyridyl-porphyrin (TPyP) with either zinc<br>and/or copper ions and the effect of their addition to polythiophene-fullerene (P3HT-PCBM)<br>OPV devices was investigated. The power conversion efficiency (PCE) of devices was found to<br>approximately double with the addition of MONs of Zn2(ZnTCPP), but was unchanged with<br>the addition of Cu2(ZnTPyP) and halved upon the addition of Cu2(CuTCPP) compared to<br>devices without nanosheets. Our analysis indicates that there are three different mechanisms<br>by which MONs can influence the photoactive layer – light absorption, energy level alignment,<br>and morphological changes. Analysis of external quantum efficiency, UV-vis photoelectron<br>spectroscopy data found that MONs have similar effects on light absorption and energy level<br>alignment. However, atomic force and Raman microscopy studies revealed that the nanosheet<br>thickness and lateral size are crucial parameters in enabling the MONs to act as beneficial<br>additives resulting in an improvement of the OPV device performance. We anticipate this<br>study will aid in the design of MONs and other 2D materials for future use in other light<br>harvesting and emitting devices.</p>
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset
<p>High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.</p> <p><strong>Nomenclatures:</strong></p> <p>LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)</p> <p><strong>Dataset Directory Organization</strong></p> <p>CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications</p> <p>1. CR dataset: computaion-ready (<em>N</em> = 10,367)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 6,603)</li> <li> FSR: free solvent removed (<em>N</em> = 3,764)</li> </ul> <p>2. NCR: not computaion-ready (<em>N</em> = 8,714)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 5,417) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 2,597)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 958)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,859)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> <li> FSR: free solvent removed (<em>N</em> = 3,297) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 1,646)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 463)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,185)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> </ul> <p>2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases</p> <p><strong>November, 24 2024</strong></p> <ul> <li>Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.</li> </ul> <p><strong>November, 13 2024</strong></p> <ul> <li>Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on <a href="https://pubs.rsc.org/en/content/articlelanding/2020/ra/d0ra02498h">Chen & Manz</a> (RSC Adv., 2020,10, <a>26944-26951</a>) and <a href="https://github.com/kjappelbaum/mofchecker">MOFChecker </a>program by <a href="https://github.com/kjappelbaum">Kevin M. Jablonka</a>)</li> <li>ML-predicted DDEC6 partial atomic charges based on <a href="https://github.com/mtap-research/PACMAN-charge">PACMAN</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)</li> <li>This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)</li> </ul>
Raw data mzXML and MATLAB code for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea
<p>MATLAB code and raw data mzXML for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea.</p> <p>Seawater samples were collected during three distinct periods: early winter (December 2019), late winter (March 2021), and spring (May 2021). The sampling transect extended from the northern Barents Sea into the Nansen Basin (76°N – 83°N) as part of <em>The Nansen Legacy</em> project (Research Council of Norway, RCN #276730). The molecular composition of dissolved organic matter (DOM) was analyzed using an Orbitrap mass spectrometer.</p>
Organized actors at the biodiversity science-policy-society interface
<p>This database was developed in the context of the Deliverable 2.1 of the BioAgora project 'Developing the Science Service for European Research and Biodiversity Policymaking' (<a href="https://bioagora.eu/)">https://bioagora.eu/)</a>. BioAgora is a collaborative European project funded by the Horizon Europe programme (Horizon Europe research and innovation programme, grant agreement No. 101059438). The project's main outcome is intended to be the development of a Science Service for Biodiversity, the principal EU mechanism to connect research and knowledge on biodiversity to the needs of policy making through a continuous dialogue. The ultimate goal of BioAgora and of the Science Service is to support the implementation of the Biodiversity Strategy for 2030, and more broadly the sustainability transition required by the EU Green Deal. The BioAgora project was launched in July 2022 for a duration of 5 years. It gathers a Consortium of 22 partners, from 13 European countries, led the Finnish Environment Institute (Syke). Partners represent a diversity of actors coming from academia, public authorities, SMEs, and associations. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. </p> <p>In order to develop the database, a thorough desk search was conducted to compile an extensive, albeit not exhaustive, list of organizations operating at the science-policy-society interface in the context of biodiversity and sustainability. In collecting the list, we focused on actors operating at EU level, although we also included particularly relevant international, regional or national organized actors. The desk search built upon the work already developed in the context of two pan-European projects, funded by the Seventh framework programme of the European Community: ‘Developing a Knowledge Network for European Expertise on biodiversity and ecosystem services to inform policy making and economic sectors (KNEU, 2010-2014, grant 265299) and ‘Establishing a European Knowledge and Learning Mechanism to Improve the Policy-Science-Society Interface on Biodiversity and Ecosystem Services’ (Eklipse, 2016-2020, grant 690474). The two above-mentioned projects preceded the BioAgora project in that they aimed at understanding and improving the effectiveness of the biodiversity science-policy(-society) interface in Europe. Such projects had thus already compiled extensive databases of relevant organizations in Europe (including national and international actors, in addition to EU level actors), and quantified the relevance of such organizations based on votes cast by project members and based on interviews with key organizations. The database developed through the desk search conducted was further refined with suggestions for relevant organizations provided by BioAgora’s participants and by the representatives of the organizations interviewed during the other steps of the data collection. The data collection processes started in September 2022 and was updated until June 2024. Note that the categories for network types (Columns E-F) are not mutually exclusive. For further details about the development of the database please see Deliverable 2.1 (<a href="https://bioagora.eu/deliverables/">https://bioagora.eu/deliverables/</a>). </p>
GIXD data of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3), processed q-space maps
<p>This dataset contains grazing incidence x-ray diffraction (GIXD) maps projected in q-space and polar projection. The underlying raw data is published in <a href="https://doi.org/10.5281/zenodo.6683616">10.5281/zenodo.6683616</a> and processed with <a href="https://doi.org/10.5281/zenodo.6683658">10.5281/zenodo.6683658</a>. This data describes a time series of diffraction images acquired with 10 Hz.</p> <p> </p> <p>Parameters of the provided data:</p> <ul> <li> <p>Q-space-maps</p> </li> </ul> <p> </p> <ul> <li> <ul> <li> <p>Horizontal axis (Q<sub>xy</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (Q<sub>z</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Resolution: 1350x1350 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> <li> <p>Polar data</p> <ul> <li> <p>Horizontal axis (||<strong>q</strong>||) range: (0, 4.53) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (ф) range: (0, 90) deg</p> </li> <li> <p>Resolution: 512x1024 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> </ul>
In-situ grazing-incidence X-ray diffraction data of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) via employing an isopropanol antisolvent. Raw Data
<p>The dataset contains 400 diffraction images from a 40 second in-situ grazing-incidence wide-angle X-ray scattering measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate. The crystallization is initiated via employing an isopropanol antisolvent during the spin-coating of the perovskite precursor solution. 40 µL of MAPbBr3 solution (4:1 DMF/DMSO solvent mixture) was applied on plasma-cleaned glass substrate in a chamber with kapton windows. The two-phase spin-coating regime included 10 seconds at 1000 rpm followed by 30 seconds at 2000 rpm, 200 µL of antisolvent was dispensed at t = 30 s.</p> <p> </p> <p> </p> <p>The data was acquired at the P08 Beamline at PETRA III (DESY Hamburg). Acquisition parameters:</p> <p> </p> <ul> <li> <p>X-ray wavelength: 0.6888 nm</p> </li> <li> <p>Sample detector distance: 809 mm</p> </li> <li> <p>Incidence angle: 0.5 deg.</p> </li> <li> <p>Detector model: XRD 1621 CN3 EHS</p> </li> <li> <p>Acquisition rate : 10 frames per second (10 Hz)</p> </li> <li> <p>Direct beam position (pixels): 545, 222</p> </li> </ul>
Optimizing laboratory cultures of <i>Gammarus fossarum</i> (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology
<p>Supplemental code and data for Alther, Krähenbühl, Bucher & Altermatt (2022) 'Optimizing laboratory cultures of <em>Gammarus fossarum</em> (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology' (DOI: 10.1016/j.scitotenv.2022.158730). The repository folder contains three text files and a corresponding R script.</p> <p>Rerunning the analysis and producing figures requires two raw data files: LabdataAK_v6_210616_Daylength_input.txt and Nutrition_Exp_KaplanMeier_v1_input.txt. In order to reproduce the analysis and figures, run 'AmphipodHusbandry_20220919.R'. Make sure that your working directory is the folder containing all data files, easily achieved by (re)starting R (or R Studio) by double-clicking the R script file in the folder. The analysis script will produce all the figures from the paper, organized in a folder 'Results' and a subfolder 'Supplement'. Figures are prepared as pixel graphics (PNG).</p> <p>The R script was tested in R ver. 4.1.1 (Windows 10, version 21H1), 4.1.3 (macOS 11.6), and 4.2.0 (Ubuntu 22.04. Required packages are survival (version 3.2-13 worked), survminer (version 0.4.9 worked), and vioplot (version 0.3.7 worked).</p>
PARAGON 1 - KM2112 - Total Organic Carbon In Situ Measurements
<p>This dataset contains measurements of total organic carbon concentrations (TOC) collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from daily collections of whole seawater at 150 m using trace metal clean techniques. Samples for measurements of TOC concentrations were collected by aliquoting 40 mL of whole seawater into pre-combusted borosilicate vials. Samples were acidified with 27 µL of 12N HCl (Optima grade, Fisher), capped with Teflon-lined silicone septa lids, and stored in the dark at room temperature until analysis on shore. Total organic carbon concentrations were determined by high temperature combustion on a modified Shimadzu TOC analyzer according to Carlson et al. (2010). Timestamp is in UTC.</p>
PARAGON 1 - KM2112 - Total Organic Carbon Timecourse Incubations
<p>This dataset contains measurements of total organic carbon concentrations (TOC) collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from time-course incubation experiments initiated with whole seawater collected at 150 m using trace metal clean techniques and modified with various additions of organic carbon, iron, and/or nitrogen. Samples for measurements of TOC concentrations were collected at multiple time points for each experimental biological replicate by aliquoting 40 mL of whole seawater from polycarbonate incubation bottles into pre-combusted borosilicate vials. Samples were acidified with 27 µL of 12N HCl (Optima grade, Fisher), capped with Teflon-lined silicone septa lids, and stored in the dark at room temperature until analysis on shore. Total organic carbon concentrations were determined by high temperature combustion on a modified Shimadzu TOC analyzer according to Carlson et al. (2010). Timestamp is in UTC. Version 2 corrects formatting errors in the timestamp.</p>
Dataset of "Elucidation of factors shaping reactivity of 5'-deoxyadenosyl – a prominent organic radical in biology"
<p>This study investigates the factors modulating the reactivity of 5'-deoxyadenosyl (5'dAdo•) radical, a potent hydrogen atom abstractor, present in the active sites of radical SAM enzymes, but otherwise undergoing a rapid self-decay in aqueous solution. Here, we compare hydrogen atom abstraction (HAA) reactions between native substrates of radical SAM enzymes and 5'dAdo• in aqueous solution and in two enzymatic microenvironments and reveal that HAA efficiency of 5'dAdo• depends on (i) formation of 5'dAdo• in a pre-ordered complex with a substrate, which attenuates the unfavorable effect of substrate:5'dAdo• complex formation, (ii) hindering the conformational change associated with self-decay by performing the reaction in a tight cavity. The enzymatic cavity, however, does not have a strong effect on the HAA activity of 5'dAdo•. We performed an analysis of HAA performed by 5'dAdo• based on the three-component thermodynamic model incorporating the diagonal effect of the free energy of reaction, and the off-diagonal effect of asynchronicity and frustration. The study is based on the straightforward relationship between the off-diagonal thermodynamic effects and the electronic-structure descriptor – the redistribution of charge between the reactants during the reaction. It allows to access HAA-competent redox and acidobasic properties of 5'dAdo• that are otherwise unavailable due to its instability upon one-electron reduction and protonation. The results show that all reactions feature a favourable thermodynamic driving force and tunneling, the latter of which lowers systematically barriers by ~2 kcal mol-1. In addition, most of the reaction experience a favourable off-diagonal thermodynamic contribution. In HAA reactions, 5'dAdo• acts as a weak oxidant as well as a base, also 5'dAdo•-promoted HAA reactions proceed with quite low degree of asynchronicity of proton and electron transfer. Finally, the study elucidates the crucial and dual role of asynchronicity. It directly lowers the barrier as a part of the off-diagonal thermodynamic contribution, but also indirectly increases the non-thermodynamic part of the barrier by controlling the adiabatic coupling between proton and electron transfer. The latter signals that the reaction proceeds as a hydrogen atom transfer rather than a proton-coupled electron transfer.</p>
PARAGON 2 - KM2209 - Total Organic Carbon Timecourse Incubations
<p>This dataset contains measurements of total organic carbon concentrations (TOC) collected during the PARAGON 2 expedition (KM2209) in the North Pacific Subtropical Gyre. Measurements come from time-course incubation experiments initiated with whole seawater collected at 150 m using trace metal clean techniques and modified with various additions of organic carbon, iron, and/or nitrogen. Samples for measurements of TOC concentrations were collected at multiple time points for each experimental biological replicate by aliquoting 40 mL of whole seawater from polycarbonate incubation bottles into pre-combusted borosilicate vials. Samples were acidified with 27 µL of 12N HCl (Optima grade, Fisher), capped with Teflon-lined silicone septa lids, and stored in the dark at room temperature until analysis on shore. Total organic carbon concentrations were determined by high temperature combustion on a modified Shimadzu TOC analyzer according to Carlson et al. (2010). Timestamp is in UTC.</p>
AIMEl-DB: Atomic Properties for 44K small organic molecules
<h3>AIMEl-DB: Atomic Properties for 44K small organic molecules</h3> <p>This dataset comprises atomic properties of 44K (44 470) molecules selected from the QM9 database. The file names are based on the same indexing system used for QM9. </p> <p>This dataset includes four types of files:</p> <ul> <li><strong>.com Files<br></strong>Input files for Gaussian 16. Simple-point energy calculations were carried out using the keywords<br><code># B3LYP/6-31G(2df,p) scf=(maxcycle=9999) nosymm output=wfx</code><br><br></li> <li><strong>.log Files<br></strong>Output files from Gaussian 16 calculation with the aformentioned parameters.<br><br></li> <li><strong>.wfx Files<br></strong>Wave function files from Gaussian 16 calculation. These files were used as inputs for QTAIM calculations. <br><br></li> <li><strong>.sumviz Files<br></strong>Output file from AIMAll software. The keywords used for the calculations were<br><code>aimqb -nogui -scp=false -nproc=8 -naat=4 input.wfx</code><br>Each .sumviz file contains more than 30 properties based on the Quantum Theory of Atoms in Molecules (QTAIM).<br><br></li> <li><strong>.csv Files<br></strong>These files contain the results of a in-house treament of .sumviz data. They cointain two calculated atomic properties:<br><br> <ol> <li>Total magnitude of the dipole moment, |mu|</li> <li>Total magnitude of the quadrupole moment, |Q|</li> </ol> </li> </ul> <p> and two extracted atomic properties:<br><br> 3. Electronic Population, N<br> 4. Atomic Energy, E</p> <p> </p> <p>The <code>aimel_merged_44k.csv</code> presents the concatenation of the 44 470 <strong>csv Files. </strong></p> <p>Additionaly, the <code>aimel_merged_38k.csv</code> presents the concatenation of the 38 876 <strong>csv Files. </strong>This file corresponds to the version 1.0 of the dataset. </p> <p><br>If you find this dataset useful, please cite the original paper:</p> <p>Meza-González, B., Ramírez-Palma, D.I., Carpio-Martínez, P. <em>et al.</em> Quantum Topological Atomic Properties of 44K molecules. <em>Sci Data</em> <strong>11</strong>, 945 (2024). https://doi.org/10.1038/s41597-024-03723-0</p> <p> </p> <p> </p>
Multi-organ Abdominal CT Reference Standard Segmentations
<p>DenseVNet Multi-organ Segmentation on Abdominal CT</p> <p>This dataset includes the multi-organ abdominal CT reference segmentations publicly released in conjunction with the IEEE Transactions on Medical Imaging paper "Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks" <a href="#1">[1]</a>.</p> <p>The data comprises reference segmentations for 90 abdominal CT images delineating multiple organs: the spleen, left kidney, gallbladder, esophagus, liver, stomach, pancreas and duodenum.</p> <p>The abdominal CT images and some of the reference segmentations were drawn from two data sets: <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">The Cancer Image Archive (TCIA) Pancreas-CT data set</a> [<a href="#2">2</a>-<a href="#4">4</a>] and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> [<a href="#5">5</a>-<a href="#6">6</a>]. The Pancreas-CT data set comprises abdominal CT acquired at the National Institutes of Health Clinical Center from pre-nephrectomy healthy kidney donors or patients with neither major abdominal pathologies nor pancreatic cancer lesions. Segmentations of the pancreas are included with this data set; images were manually labeled slice-by-slice by a medical student, and verified/modified by an experienced radiologist. The BTCV data set comprises abdominal CT acquired at the Vanderbilt University Medical Center from metastatic liver cancer patients or post-operative ventral hernia patients. Segmentations of the spleen, right and left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, portal vein and splenic vein, pancreas, right adrenal gland, left adrenal gland are included in this data set; images were manually labeled by two experienced undergraduate students, and verified by a radiologist on a volumetric basis using the MIPAV software.</p> <p>Segmentations that were not present in the original data sets were performed interactively using Matlab 2015b and ITK-SNAP 3.2 by an image research fellow under the supervision of a board-certified radiologist with 8 years of experience in gastrointestinal CT and MRI image interpretation. Segmentations that were present in the original data sets were edited to ensure a consistent segmentation protocol across the data set.</p> <p>Terms of use</p> <p>The terms of use of this data set include the terms of use of both the <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">TCIA Pancreas-CT data set</a> (see tabs for data links and terms of use) and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> (<a href="https://doi.org/10.7303/syn3193805">terms of use</a>; after <a href="https://www.synapse.org/#!Synapse:syn3193805/wiki/217753">registration</a>, you can <a href="https://www.synapse.org/#!Synapse:syn3376386">access the data</a>). If you use these reference segmentations, please cite the above manuscript and the references below. Because these data include manual segmentations of images from the Beyond the Cranial Vault challenge test data, they may not be used to develop submissions for the challenge.</p> <p>References</p> <p>[1] Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC. Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Transactions on Medical Imaging, 2018.</p> <p>[2] Roth HR, Farag A, Turkbey EB, Lu L, Liu J, and Summers RM. (2016). Data From Pancreas-CT. The Cancer Imaging Archive. <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU</a></p> <p>[3] Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation. N. Navab et al. (Eds.): MICCAI 2015, Part I, LNCS 9349, pp. 556–564, 2015. <a href="http://arxiv.org/pdf/1506.06448.pdf">http://arxiv.org/pdf/1506.06448.pdf</a></p> <p>[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. <a href="http://doi.org/10.1007/s10278-013-9622-7">http://doi.org/10.1007/s10278-013-9622-7</a></p> <p>[5] Xu Z, Lee CP, Heinrich MP, Modat M, Rueckert D, Ourselin S, Abramson RG, and Landman BA, "Evaluation of six registration methods for the human abdomen on clinically acquired CT," IEEE Trans. Biomed. Eng., vol. 63, no. 8, pp. 1563–1572, 2016.<a href="http://doi.org/10.1109/TBME.2016.2574816">http://doi.org/10.1109/TBME.2016.2574816</a></p> <p>[6] Landman BA, Xu Z, Igelsias JE, Styner M, Langerak TR, and Klein A, "MICCAI multi-atlas labeling beyond the cranial vault - workshop and challenge," 2015, <a href="https://doi.org/10.7303/syn3193805">https://doi.org/10.7303/syn3193805</a></p> <p>File format Labels are in NIfTI format with the following label definitions. Labels marked with * are only available in the BTCV data set.</p> <ol> <li>spleen</li> <li>right kidney*</li> <li>left kidney</li> <li>gallbladder</li> <li>esophagus</li> <li>liver</li> <li>stomach</li> <li>aorta*</li> <li>inferior vena cava*</li> <li>portal vein and splenic vein*</li> <li>pancreas</li> <li>right adrenal gland*</li> <li>left adrenal gland*</li> <li>duodenum</li> </ol> <p>Subjects included in the dataset</p> <p>The data comprises segmentation volumes for 90 cases, and the cropping coordinates (cropping.csv) used in the manuscript. The abdominal CT can be obtained from the links above. The reference standard segmentations may be incomplete outside of the specified cropping region. The cases are listed by their subject identifiers in their original data set:</p> <p> </p> <p><span class="math-tex">\(\begin{bmatrix} 1 & TCIA & Pancreas-CT & 0002\\ 2 & TCIA & Pancreas-CT & 0003\\ 3 & TCIA & Pancreas-CT & 0004\\ 4 & TCIA & Pancreas-CT & 0005\\ 5 & TCIA & Pancreas-CT & 0006\\ 6 & TCIA & Pancreas-CT & 0007\\ 7 & TCIA & Pancreas-CT & 0008\\ 8 & TCIA & Pancreas-CT & 0009\\ 9 & TCIA & Pancreas-CT & 0010\\ 10 & TCIA & Pancreas-CT & 0011\\ 11 & TCIA & Pancreas-CT & 0012\\ 12 & TCIA & Pancreas-CT & 0013\\ 13 & TCIA & Pancreas-CT & 0014\\ 14 & TCIA & Pancreas-CT & 0016\\ 15 & TCIA & Pancreas-CT & 0017\\ 16 & TCIA & Pancreas-CT & 0018\\ 17 & TCIA & Pancreas-CT & 0019\\ 18 & TCIA & Pancreas-CT & 0020\\ 19 & TCIA & Pancreas-CT & 0021\\ 20 & TCIA & Pancreas-CT & 0022\\ 21 & TCIA & Pancreas-CT & 0024\\ 22 & TCIA & Pancreas-CT & 0025\\ 23 & TCIA & Pancreas-CT & 0026\\ 24 & TCIA & Pancreas-CT & 0027\\ 25 & TCIA & Pancreas-CT & 0028\\ 26 & TCIA & Pancreas-CT & 0029\\ 27 & TCIA & Pancreas-CT & 0030\\ 28 & TCIA & Pancreas-CT & 0031\\ 29 & TCIA & Pancreas-CT & 0032\\ 30 & TCIA & Pancreas-CT & 0033\\ 31 & TCIA & Pancreas-CT & 0034\\ 32 & TCIA & Pancreas-CT & 0035\\ 33 & TCIA & Pancreas-CT & 0038\\ 34 & TCIA & Pancreas-CT & 0039\\ 35 & TCIA & Pancreas-CT & 0040\\ 36 & TCIA & Pancreas-CT & 0041\\ 37 & TCIA & Pancreas-CT & 0042\\ 38 & TCIA & Pancreas-CT & 0043\\ 39 & TCIA & Pancreas-CT & 0044\\ 40 & TCIA & Pancreas-CT & 0045\\ 41 & TCIA & Pancreas-CT & 0046\\ 42 & TCIA & Pancreas-CT & 0047\\ 43 & TCIA & Pancreas-CT & 0048\\ 44 & Synapse & BeyondTheCranialVault & 0001\\ 45 & Synapse & BeyondTheCranialVault & 0002\\ 46 & Synapse & BeyondTheCranialVault & 0003\\ 47 & Synapse & BeyondTheCranialVault & 0004\\ 48 & Synapse & BeyondTheCranialVault & 0005\\ 49 & Synapse & BeyondTheCranialVault & 0006\\ 50 & Synapse & BeyondTheCranialVault & 0007\\ 51 & Synapse & BeyondTheCranialVault & 0008\\ 52 & Synapse & BeyondTheCranialVault & 0009\\ 53 & Synapse & BeyondTheCranialVault & 0010\\ 54 & Synapse & BeyondTheCranialVault & 0021\\ 55 & Synapse & BeyondTheCranialVault & 0022\\ 56 & Synapse & BeyondTheCranialVault & 0023\\ 57 & Synapse & BeyondTheCranialVault & 0024\\ 58 & Synapse & BeyondTheCranialVault & 0025\\ 59 & Synapse & BeyondTheCranialVault & 0026\\ 60 & Synapse & BeyondTheCranialVault & 0027\\ 61 & Synapse & BeyondTheCranialVault & 0028\\ 62 & Synapse & BeyondTheCranialVault & 0029\\ 63 & Synapse & BeyondTheCranialVault & 0030\\ 64 & Synapse & BeyondTheCranialVault & 0031\\ 65 & Synapse & BeyondTheCranialVault & 0032\\ 66 & Synapse & BeyondTheCranialVault & 0033\\ 67 & Synapse & BeyondTheCranialVault & 0034\\ 68 & Synapse & BeyondTheCranialVault & 0035\\ 69 & Synapse & BeyondTheCranialVault & 0036\\ 70 & Synapse & BeyondTheCranialVault & 0037\\ 71 & Synapse & BeyondTheCranialVault & 0038\\ 72 & Synapse & BeyondTheCranialVault & 0039\\ 73 & Synapse & BeyondTheCranialVault & 0040\\ 74 & Synapse & BeyondTheCranialVault & 0061\\ 75 & Synapse & BeyondTheCranialVault & 0062\\ 76 & Synapse & BeyondTheCranialVault & 0063\\ 77 & Synapse & BeyondTheCranialVault & 0064\\ 78 & Synapse & BeyondTheCranialVault & 0065\\ 79 & Synapse & BeyondTheCranialVault & 0066\\ 80 & Synapse & BeyondTheCranialVault & 0067\\ 81 & Synapse & BeyondTheCranialVault & 0068\\ 82 & Synapse & BeyondTheCranialVault & 0069\\ 83 & Synapse & BeyondTheCranialVault & 0070\\ 84 & Synapse & BeyondTheCranialVault & 0074\\ 85 & Synapse & BeyondTheCranialVault & 0075\\ 86 & Synapse & BeyondTheCranialVault & 0076\\ 87 & Synapse & BeyondTheCranialVault & 0077\\ 88 & Synapse & BeyondTheCranialVault & 0078\\ 89 & Synapse & BeyondTheCranialVault & 0079\\ 90 & Synapse & BeyondTheCranialVault & 0080\\ \end{bmatrix}\)</span></p>
Concentration of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water year 2019 (1 Oct 2018 - 30 Sep 2019)
These data were collected to support monitoring of the Upper Clark Fork River restoration, and data collection was funded by the US NSF Long Term Research in Environmental Biology (LTREB) program and the US NSF EPSCoR funded Montana Consortium for Research on Environmental Water Systems. The LTREB monitoring project consists of monthly or bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are Aurora Total Organic Carbon combustion analyses of the concentration of organic carbon dissolved in filtered samples of well-mixed river thalweg water. Data are from the 2019 water year (1 Oct 2018 to 30 Sep 2019). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.
Salt River Wetlands denitrification rate, dissimilatory nitrate reduction to ammonium rate, dissolved organic carbon concentration in June 2016 as well as soil porosity and bulk density
Raw and derived data used to calculate denitrification and dissimilatory nitrate to ammonium (DNRA) from push-pull experiments with added isotopically labelled nitrate. Experiments were conducted in 2016 in the Salt River Accidental Wetlands in three different patch types: Unvegetated, dominated by Ludwigia peploides, and dominated by Typha species (T. domingensis and T. latifolia). Data include start and end of incubation concentration of nitrate, ammonium, atom percent 15N in ammonium, dissolved organic carbon, excess mass 29-N2, and excess mass 30-N2. Soil data was collected from the same patch types including soil moisture, porosity, and bulk density.
Flume Erosion Testing Data of Root-Permeated and Organic Matter Amended Soil Samples Using Three Streambank Boundary Conditions.
The data published here is expected to accompany one publicly available dissertation (Chapter 6 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Artificial Roots and Soil Microorganisms Increase Soil Resistance to Fluvial Erosion
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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.