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16,646 results for “four”
Genome-wide association summary statistics of chronic musculoskeletal pain at four anatomic sites and their genetically independent components
<p>The dataset contains results of a genome-wide association study of distinct chronic musculoskeletal pain conditions: back pain, knee pain, neck pain, and hip pain. Additionally, there are genome-wide association summary statistics for four genetically independent components of pain conditions, listed above. For more details, please, read the paper XXX.</p> <p>All files contain association summary statistics for genome-wide association meta-analysis of the 265,000 white British individuals from the UK Biobank and additional 191,580 individuals of European Ancestry from the UK biobank (total N = 456,580). Cases and controls were defined based on questionnaire responses. First, participants responded to “Pain type(s) experienced in the last months” followed by questions inquiring if the specific pain had been present for more than 3 months. Those who reported back, neck or shoulder, hip, or knee pain lasting more than 3 months were considered chronic back, neck/shoulder, hip, and knee pain cases, respectively. Participants reporting no such pain lasting longer than 3 months were considered controls (regardless of whether they had another regional chronic pain, such as abdominal pain, or not). Individuals who preferred not to answer were excluded from the study. Besides this, we excluded individuals who reported more than 3 months of pain all over the body.</p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilization of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.</p> <p><strong>When using downloaded data, please cite the corresponding paper and this repository:</strong></p> <ol> <li>Tsepilov et al 2020</li> </ol> <p><strong>Funding:</strong></p> <p>The work of YSA and SZS was supported by the Russian Ministry of Education and Science under the 5-100 Excellence Programme and by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project 0324-2019-0040). The work of YAT, ASSh, and EEE was supported by the Russian Foundation for Basic Research (project 19-015-00151). The contribution of LСK was funded by PolyOmica. Dr. Suri was supported by VA Career Development Award # 1IK2RX001515 from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development (RR&D) Service. Dr. Suri is a Staff Physician at the VA Puget Sound Health Care System. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.</p> <p><strong>List of files:</strong></p> <ol> <li>Back_output_done.csv: GWAS summary statistics for the chronic back pain</li> <li>gpc1_output_done.csv: GWAS summary statistics for the GIP1</li> <li>gpc2_output_done.csv: GWAS summary statistics for the GIP2</li> <li>gpc3_output_done.csv: GWAS summary statistics for the GIP3</li> <li>gpc4_output_done.csv: GWAS summary statistics for the GIP4</li> <li>Hip_output_done.csv: GWAS summary statistics for the chronic hip pain</li> <li>Knee_output_done.csv: GWAS summary statistics for the chronic knee pain</li> <li>Neck_output_done.csv: GWAS summary statistics for the chronic neck pain</li> </ol> <p><strong>Column headers:</strong></p> <ol> <li>gwas_id: uninformative field</li> <li>rs_id: dbSNP rsID (GRCh37 build) </li> <li>snp_num: uninformative field</li> <li>chr: chromosome (GRCh37 build) </li> <li>bp: position (GRCh37 build) </li> <li>ea: effect allele (coded as "1")</li> <li>ra: reference allele (coded as "0")</li> <li>eaf: effect allele frequency</li> <li>af_ref: uninformative field</li> <li>beta: effect size of effect allele</li> <li>se: standard error of effect size</li> <li>p: P-value of association (without GC correction)</li> <li>n:Total sample size</li> <li>z: Z-statistic of association</li> <li>info: uninformative field</li> <li>af_outlier: uninformative field</li> <li>pz_outlier: uninformative field</li> </ol>
ds-uct-002: Root Canal Strain: X-Ray micro-CT of four teeth before and after root canal procedure.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of four teeth before (TomoB) and after (TomoA) simulation of root canal treatment and retreatment procedures instrumented with strain-gauge, including reconstructions, for two different resolutions (TomoB and TomoA with voxel sizes of 20.0 μm and 10.5 μm, respectively).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p><strong>Details</strong>:<br> .Tomo1B/Tomo2B/Tomo3B/Tomo4B (1024) - Voxel size: 20.0 μm; Sample-source: 45.0 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#1; Beam energy: 60 kV; Power: 5 W; Exposure time: 2.0 sec; Projections: 1600.<br> .Tomo1A/Tomo2A/Tomo3A/Tomo4A (2048) - Voxel size: 10.5 μm; Sample-source: 48.2 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#2; Beam energy: 60 kV; Power: 5 W; Exposure time: 7.0 sec; Projections: 1600.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-002.txt<br> .ds-uct-002_root_canal_strain_tomo1b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo2b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo3b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo4b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo1a_10um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo2a_10um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo3a_10um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo4a_10um_8bits.zip<br> .PB_PARECER_CONSUBSTANCIADO_CEP_2650528.pdf</p>
ICESat-2 Water Depth Retrieval Comparisons for Four Supraglacial on Amery Ice Shelf, East Antarctica
<p>This archive contains the code used for analysis and producing figures for the following paper:</p> <p>Fricker, H.A., Arndt, P.S., Brunt, K.M., Datta, R.T., Fair, Z., Jasinski, M.F., Kingslake, J., Magruder, L.A., Moussavi, M., Pope, A. and Spergel, J.J., 2021. “ICESat-2 meltwater depth estimates: application to surface melt on Amery Ice Shelf, East Antarctica.” Geophysical Research Letters, 48(8), DOI: 10.1029/2020GL090550. URL: <a href="https://doi.org/10.1029/2020GL090550">https://doi.org/10.1029/2020GL090550</a><br><br>These materials are also on GitHub:<br><a href="github.com/fliphilipp/ameryMeltLakesICESat2">https://github.com/fliphilipp/ameryMeltLakesICESat2</a> </p> <p> </p> <p>The code for generating manually annotated baseline depth estimates from ICESat-2 ATL03 photon data is available here:<br><a href="https://github.com/fliphilipp/pondpicking">https://github.com/fliphilipp/pondpicking</a></p>
Genetic Relationships Between Terminal Shoot Length, Number of Flushes and Height in a Four-Year-old Progeny Test of Pinus brutia Ten.
<p><strong>Description of the data</strong></p> <p>A total of 188 plus trees were selected from eight natural seed stands of <em>Pinus brutia</em> in the Aegean region of Turkey. The number of trees selected per seed stand (provenance) varied between 7 to 53 trees. Open-pollinated seeds were collected from plus trees in 1998 and in 1999. In addition, six checklots consisting of bulk seeds from natural seed stands were included in the study to estimate genetic gain and link the progeny tests across different breeding zones in the Aegean region.</p> <p>Open-pollinated progeny tests were established at three locations in the Aegean region of Turkey (Hisaronu, Izmir, and Kinik) in March 2000. One-year-old bare-root seedlings were used in the study. Randomized complete block design with four-tree row plots was used in all sites. For each plus tree (female parent), about 72 half-sib progenies were planted across three test sites. The Hisaronu site had four blocks, while two other sites had seven blocks each. Each parent tree was represented by 16 half-sib progenies at the Hisaronu site and 28 progenies in the other two sites when the trials were planted. The spacing among seedlings was 2 x 3 m at each site. Each block was split into four sets (sets in replications) to accommodate a large number of trees, with checklots included in every set. In total, about 166 half-sib progenies and checklots were planted in each block.</p> <p>At the end of the first growing year after planting, survival was assessed. It was about 52% at the Hisaronu site. Dead seedlings at the Hisaronu site were replaced with 1083 two-year-old seedlings of the same families, which were grown in a nursery near Marmaris in the Aegean region. The other two sites had 91% (İzmir) and 94% (Kinik) survival. At age four after planting (2004), tree height (cm), terminal shoot length (cm), and the number of flushes were measured. In total, approximately 12100 trees were assessed across the three locations. </p>
Processed NOMe-seq data for four human cell lines
<p>Raw NOME-seq data (Gene Expression Omnibus accession GSE57498) for the human cell lines HMEC, MCF7, PrEC, PC3 were aligned to hg19 using bwa-meth. Methylation and occupancy calls were made at WCG and GCH sites respectively using bwa-meth, BisSNP and bespoke, tailor made scripts (https://github.com/astatham/NOMe-seq-analysis).</p>
Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K
<div><strong>Summary:</strong></div> <div>Two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them.</div> <div> </div> <div><strong>The dataset includes:</strong></div> <div>Characteristics of bituminous binder, CSV raw data</div> <div> <ul> <li>bituminous binder.csv</li> </ul> </div> <div>Grading curves of tested asphalt mixtures</div> <ul> <li>AML16 Grading curves.csv</li> <li>AMP22 Grading curves.csv</li> </ul> <div>Volumetric characterizations of AML16 and AMP22 mixtures</div> <ul> <li>AML16 Volumetric characterizations.csv</li> <li>AMP22 Volumetric characterizations.csv</li> </ul> <div>Outcomes of the 4PBT experimental trial carried out on AML16 and AMP22 mixtures</div> <ul> <li>AML16 Stiffness Modulus 4PB.csv</li> <li>AMP22 Stiffness Modulus 4PB.csv</li> </ul>
FAIR Evaluations of University and College Repositories at DataCite Using MetaDIG Mappings for Four Use Cases.
<p>This spreadsheet has the results of an evaluation of FAIRness of 387 University and College DataCite repositories using techniques developed in the MetaDIG project. It is possible to compare scores from different repositories and to create rose diagrams showing the results for any of the repositories.</p>
Four angle fused dataset for Ascidian embryo imaged via light sheet
<p>Original dataset imaged and published here: </p> <pre>DOI: 10.6084/m9.figshare.8235473.v1</pre> <p>This dataset is provided as Raw dataset for training deep neural networks for segmentation tasks. The binary masks and the integer labels are provided separately.</p>
Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet
<p>Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet</p>
Description and quality metadata files for four spatial landmark datasets
<p>The files define the description and quality metadata of the spatial landmark datasets from camptocamp.org defined <a href="https://doi.org/10.5281/zenodo.6480985">here</a>.</p> <p>Two ISO standards ISO 19115-1:2014 and ISO 19157:2013, recommanded by the INSPIRE Directive for the dissemination of spatial data and the reporting of data quality are used.</p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate FGOB10Ptm-1. </p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate P-A14. </p>
Mars Target Encyclopedia - Labeled LPSC abstracts for four Mars missions
<p>This data set contains annotated text versions of 1635 two-page abstracts published at the Lunar and Planetary Science Conference from 1998 to 2020 of relevance to four Mars missions. The annotations were generated using named entity recognition and relation extraction provided by the MTE processing pipeline (available at https://github.com/wkiri/MTE), followed by manual review. Annotated entities include Element, Mineral, Property, and Target. Annotated relations include <strong>Contains</strong>(Target, Element | Mineral) and <strong>HasProperty</strong>(Target, Property). The extracted information (without full texts) is also available as a database (stored in .csv files) at https://pds-geosciences.wustl.edu/missions/mte/mte.htm . The complete annotated texts are provided here as a resource for further research and experimentation on information extraction methods. For more information about the Mars Target Encyclopedia and these annotations, please see:</p> <ul> <li>"<a href="https://www.hou.usra.edu/meetings/lpsc2022/pdf/1231.pdf">Targets from the Spirit Mars Exploration Rover in the Mars Target Encyclopedia</a>", Kiri L. Wagstaff, Raymond Francis, Matthew Golombek, Steven Lu, Ellen Riloff, Leslie Tamppari, Yuan Zhuang, and Thomas Stein.<br> <em>53rd Lunar and Planetary Science Conference</em>, Abstract #1231, March 2022.</li> <li>"<a href="https://www.hou.usra.edu/meetings/lpsc2021/pdf/1278.pdf">The Mars Target Encyclopedia Now Includes Mars Pathfinder and Mars Phoenix Targets</a>", Kiri L. Wagstaff, Raymond Francis, Matthew Golombek, Steven Lu, Ellen Riloff, Leslie Tamppari, and Thomas C. Stein.<br> <em>52nd Lunar and Planetary Science Conference</em>, Abstract #1278, March 2021.</li> </ul> <p>The original PDF abstracts are available at: </p> <ul> <li>For years prior to 2000: https://www.lpi.usra.edu/meetings/LPSC${two-digit-year}/pdf/${id}.pdf</li> <li>For year 2000: https://www.lpi.usra.edu/meetings/LPSC${four-digit-year}/pdf/${id}.pdf</li> <li>For years 2001-2017 (note lower-case lpsc): https://www.lpi.usra.edu/meetings/lpsc${four-digit-year}/pdf/${id}.pdf</li> <li>For years 2018-2020: https://www.hou.usra.edu/meetings/lpsc${four-digit-year}/pdf/${id}.pdf</li> </ul> <p>where ${id} is a four-digit abstract number, starting with 1001 (if available).</p> <p>The text files provided in this archive were extracted from the PDF files using the Apache Tika PDF parsing tool. They are named as ${four-digit-year}_${id}.txt. The text is provided here so that the annotations can be viewed in context. The text content remains copyright of the original abstract authors.</p> <p>The annotations (entities and relations) are provided in the format used by the brat annotation tool. They are named as ${four-digit-year}_${id}.ann. To view the annotations in a web-based graphical form, install the brat tool (http://brat.nlplab.org/). These annotations were generated using brat v1.3. The annotation files are also human-readable and can be parsed in to be used directly in code. If the .ann file is empty, then there are no relevant annotations for the associated text file.</p> <p><strong>Contents</strong>:</p> <ul> <li>mpf.zip: 591 abstracts relating to the Mars Pathfinder mission (1998-2020)</li> <li>mer-a.zip: 397 abstracts relating to the MER-A (Spirit) rover mission (2004-2020)</li> <li>mer-b.zip: 256 abstracts relating to the MER-B (Opportunity) rover mission (2005-2020)</li> <li>phx.zip: 391 abstracts relating to the Mars Phoenix Lander mission (2009-2020)</li> </ul> <p>Each directory contains a .txt and .ann file for each abstract. The .ann file is in brat standoff format (http://brat.nlplab.org/standoff.html). Additional .conf files are provided to generate color highlighting and keyboard shortcuts. These are used by the brat tool.</p> <p>Note: the same abstract may appear in more than one mission directory, if it discusses targets from more than one mission. It will have a different .ann file for each such appearance. Within each directory, a "Target" annotation is understood to refer to a target of the relevant mission.</p> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite it as follows:</p> <p>Kiri L. Wagstaff, Raymond Francis, Matthew Golombek, Leslie Tamppari, and Steven Lu. (2022). Mars Target Encyclopedia - Labeled LPSC abstracts for four Mars missions (1.0.0.0) [Data set]. Zenodo. DOI: 10.5281/zenodo.7066107</p>
Dataset: Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses
<p>This dataset contains anonymised raw data and examples of accommodations made for neurodiverse students in four undergraduate courses in Computer Science and Software Engineering programmes. The dataset is published as a part of a book chapter in which we report the accommodations.</p> <p>Overall guidelines we followed, including their sources, are contained in <strong>guidelines.md.</strong></p> <p>The raw data for the two surveys is contained in the two Excel files <strong>survey1.xlsx</strong> and <strong>survey2.xlsx</strong>. Free-text answers have been aggregated by neurodiverse and neurotypical students and anonymised, and are available in the files<strong> survey1_freetext_neurodiverse.txt, survey1_freetext_neurotypical.txt, survey2_freetext_neurodiverse.txt, </strong>and<strong> survey2_freetext_neurotypical.txt.</strong></p> <p>The remaining files are examples of the adapted lecture slides and assignment texts. Here, files starting with WEBcourse are from a mandatory undergraduate course on web development, while files starting with SEcourse are from a mandatory undergraduate course giving an overview of Software Engineering.</p>
XML Data for "Four Early Chan Texts from Dunhuang - A TEI-based Edition"
<p>This zip archive contains the XML data, schema, stylesheets, and associated material that are the basis for a print edition of 48 Dunhuang manuscript witnesses of four early Chan Buddhist texts. The print edition is:</p> <p>Marcus Bingenheimer 馬德偉, Chang Po-Yung 張伯雍 (eds.): <em>Four Early Chan Texts from Dunhuang – A TEI-based Edition</em> 早期禪宗文獻四部 —— 以TEI標記重訂敦煌寫卷:楞伽師資記,傳法寶紀,修心要論,觀心論. Taipei: Shin Wen Feng 新文豐, 2018.<br> (Vol. 1: Facsimiles and Diplomatic Transcription 摹寫版 (ISBN: 978-957-17-2274-0), Vol. 2: Parallel, Punctuated and Annotated Edition 對照與點注版 (ISBN: 978-957-17-2275-7), Vol. 3: Calligraphy Practice 抄經版 (ISBN: 978-957-17-2276-4).)</p> <p>See the included README.txt for further information.</p>
Elevation modulates the phenotypic responses to light of four co-occurring Pyrenean forest tree species
<p>Data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient. The dataset contains three files:</p> <ol> <li><strong>Biomass.txt: </strong>Data on plant biomass per fraction (leaf, stem and roots) 4 years after plantation. Included variables:<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant in that plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (factor): code to identify uniquely each plant<br> - GLI (num): Global Light Index, the amount of irradiance that receives each seedling<br> - Code (factor): code to identify uniquely each plant<br> - PLB (numeric): total plant biomass (g)<br> - LFB (numeric): leaf biomass (g)<br> - STB (numeric): stem biomass (g)<br> - RTB (numeric): root biomass (g)<br> - LMF (numeric): leaf mass fraction (LFB/PLB)<br> - SMF (numeric): stem mass fraction (STB/PLB)<br> - RMF (numeric): root mass fraction (RTB/PLB)<br> - SLA (numeric): specific leaf area<br> - H (numeric): plant height (mm)<br> - D (numeric): plant diameter at root collar (mm)<br> - PB2 (numeric): total plant biomass without considering leaves (g)<br> - SF2 (numeric): stem mass fraction without considering leaves (STB/PB2)<br> - RF2 (numeric): root mass fraction without considering leaves (RTB/PB2)</li> <li><strong>init_biomass.txt:</strong> for biomass at the moment of plantation<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - N (numeric): number of plant<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (numeric): code to identify uniquely each plant<br> - PB (numeric): total plant biomass (g)<br> - LB (numeric): leaf biomass (g)<br> - SB (numeric): stem biomass (g)<br> - RB (numeric): root biomass (g)</li> <li><strong>WaterPot.txt</strong>: data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient during a period of intense drought<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant <br> - Parcela (factor): identifier ofthe plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Estacion (factor): the moment for the measurement. One level: September<br> - GLI (numeric): global light index, the ration of total irradiance received by the plant at the moment of plantation<br> - WPt (numeric): water potential (bars)</li> </ol>
Four-stages FAIR Roadmap - FAIR "Pyramid"
<p>On the basis of the experience of a community of practitioners, experts, engineers involved in the development of the EPOS Research Infrastructure, now with the ERIC status, involved in the ENVRI cluster and participating to the ENVRI-FAIR initiative, a common approach was observed, which is reflected into the re-organization FAIR principles into a four-stages roadmap which include: a) <em>data</em> stage, b) <em>metadata</em> stage, c) <em>access</em> stage and d) <em>use</em> stage.<br> These stages correspond to the actual conceptual approach driving day-to-day work of RI implementers in the solid Earth domain (EPOS). </p> <p>Data are usually the main business and wealth of scientists and data practitioners in RIs. As a consequence, the first conceptual step relates to data aspects (<em>Stage 1</em>). Once data is properly managed, RIs professional tend to conceptually tackle the challenge of data description and identification, in order to create the premises for data searchability and contextualization (<em>Stage 2</em>). Once data is properly managed, described and contextualized by means of metadata, then RIs practitioners approach the issue of making it accessible to users (<em>Stage 3</em>). In order to include functionalities that go beyond data access, for instance data analysis and processing, FAIR RIs and data stewardship systems should address a <em>fourth stage</em> concerned with services that <em>make use</em> of data (<em>Stage 1</em>) and metadata (<em>Stage 2</em>) FAIRly accessed (<em>Stage 3</em>) and produce new (meta)data as output.</p>
Dataset for "Gross primary productivity of four European ecosystems constrained by joint CO2 and COS flux measurements"
<p>Data of measurements and model output of the publication "Gross primary productivity of four European ecosystems constrained by joint CO<sub>2</sub> and COS flux measurements".</p> <p>Data consists of micrometeorological data, COS and CO<sub>2</sub> flux measurements for 4 sites including filters for the fluxes.</p> <p>The sites include: a managed temperate mountain grassland in Austria (18.06.-21.08.2015), a Mediterranean savanna ecosystem in Spain(29.04.-24.05.2016)), a Temperate beach forest in Denmark(07.06.-03.07.2016) and an agricultural soy bean field in Italy(03.07.-01.08.2017).</p> <p>Version 2: param2950** are now correct (were filled with the same values for all field sites) </p> <p>For additional information please contact: <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a></p>
Dataset for the preprint: "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich"
<p>Dataset supporting the submission of the manuscript titled "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich" to the to the international journal "Biogeosciences".</p> <p><strong>Meteorological data</strong></p> <p>Hourly aggregated meteorological dataset for the urban area of Zurich, originating from two urban stations: Kaserne (8°32'/47°23'), which is a station of the Swiss national air pollution monitoring network NABEL, and Hardau II (8°30'/47°23'), which is a station established for the ICOS-Cities project. Zurich Kaserne is located in a large courtyard. Wind and global radiation are measured on top of a four-storey building. Wind is measured at 35 m and global radiation at 27 m above ground. Hardau II station is established on the top of a high-rise building (110 m a.g.l.). Meteorological observation gaps were filled using Copernicus ERA5-Land data.</p> <p>Data format: comma separated values (csv)</p> <p>Time step: 60 min (aggregated)</p> <p>Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period</p> <p>Period: 01/2022–09/2023</p> <p>Monthly mean atmospheric CO2 concentration data derived from the ICOS-Cities Hardau II station (07/2022–09/2023) and the Beromunster station (11/2012–02/2022). Observation gaps were filled using Copernicus ERA5-Land data.</p> <p>Further information on the dataset can be found in the submitted manuscript. </p> <p>Data format: comma separated values (csv)</p> <p>Time step: Monthly (mean)</p> <p>Time stamp: yyyy-MM-dd </p> <p>Period: 11/2012–09/2023</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Acronym</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Height above ground</strong></p> </td> <td> <p><strong>Location</strong></p> </td> <td> <p><strong>Geographic location</strong></p> </td> </tr> <tr> <td> <p>Global radiation</p> </td> <td> <p>G</p> </td> <td> <p>W m-2</p> </td> <td> <p>27 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Air temperature</p> </td> <td> <p>Tair</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Relative humidity</p> </td> <td> <p>RH</p> </td> <td> <p>%</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Air pressure</p> </td> <td> <p>P</p> </td> <td> <p>hPa</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Wind speed</p> </td> <td> <p>u</p> </td> <td> <p>m s-1</p> </td> <td> <p>35 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Precipitation</p> </td> <td> <p>R</p> </td> <td> <p>mm</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Downward longwave radiation</p> </td> <td> <p>LW</p> </td> <td> <p>W m-2</p> </td> <td> <p>110 m</p> </td> <td> <p>Hardau II, ERA-5</p> </td> <td> <p>8°30'/47°23'</p> </td> </tr> <tr> <td> <p>Soil temperature</p> </td> <td> <p>Tsoil</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>-0.15 m</p> </td> <td> <p>Parks</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Soil water content</p> </td> <td> <p>SWC</p> </td> <td> <p>m3 m-3</p> </td> <td> <p>-0.15 m</p> </td> <td> <p>Parks</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Atmospheric CO2 concentration</p> </td> <td> <p>CO2</p> </td> <td> <p>ppmv</p> </td> <td> <p>2 m</p> </td> <td> <p>Hardau II, Beromunster, ERA-5</p> </td> <td> <p>8°30'/47°23', 8°10'/47°11</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>In-situ ecophysiological data</strong></p> <p>In-situ ecophysiology measurements performed on park trees and lawns in the city of Zurich during the ICOS-Cities project.</p> <p>LAI (leaf area index) was measured in dense <em>Platanus</em> sp. tree stands, found only in Bullingerhof and Hardaupark, during sunny conditions using a ceptometer (SS1 SunScan, Delta-T Devices).</p> <p>Sap flow was measured at six trees (<em>Platanus</em> sp., <em>Tilia</em> sp.), at Bullingerhof, Hardaupark and Fritschiwiese, with heat pulse sap flow sensors (3 x 3 cm probes, Implexx Sense), providing continuous measurements at 10-min sampling intervals. Daily aggregated sap flux densities (cm3 cm−2 d−1) were calculated from the 10-min data using the sensor inner thermistors, averaged for the six sampled trees.</p> <p>Soil and grass respiration were measured using a portable CO2 soil efflux system equipped with a 20 cm diameter survey chamber (LI-8200-01S, LI-COR Biosciences) and a CO2/H2O analyser (LI-870, LI-COR Biosciences). The observations originate from a total of 10 soil collars (Bullingerhof, Hardaupark, Fritschiwiese, Heiligfeld) separated to undisturbed grass collars (Reco, μmol CO2 m-2 s-1) and collars where the aboveground grass was clipped (Rsoil, μmol CO2 m-2 s-1).</p> <p>Further information on the dataset can be found in the submitted manuscript.</p> <p>Data format: comma separated values (csv)</p> <p>Time stamp: yyyy-MM-dd</p> <p>Period: 04/2022–09/2023</p> <p> </p> <p><strong>Land cover map</strong></p> <p>Land cover map of part of Zurich urban area. Datasets used to derive this map:</p> <ul> <li>· Land Use Cadastre of the Canton of Zurich (https://www.geolion.zh.ch/geodatensatz/show?gdsid=443)</li> <li>· Urban Atlas (https://doi.org/10.2909/fb4dffa1-6ceb-4cc0-8372-1ed354c285e6)</li> <li>· Vegetation Height Model (VHM) from the Swiss federal forest inventory (https://opendata.swiss/de/dataset/vegetationshohenmodell-lfi)</li> <li>· Forest Mixture from the Swiss Federal Forest Inventory (https://opendata.swiss/de/dataset/waldmischungsgrad-lfi)</li> </ul> <p>Further information on the dataset can be found in the submitted manuscript.</p> <p> </p> <p>Data specifications:</p> <p>CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected</p> <p>Spatial Extent: 461972.1, 5246490.4 : 463972.1, 5248490.4</p> <p>Temporal Extent: 2023</p> <p>Units: meters</p> <p>Width: 2000</p> <p>Height: 2000</p> <p>Bands: 1</p> <p>Pixel Size: 1,-1</p> <p>Data type: Float32 - Thirty two bit floating point</p> <p>GDAL Driver Description: GTiff</p> <p>GDAL Driver Metadata: GeoTIFF</p> <p> </p> <p>Legend:</p> <p>30 Grass</p> <p>40 Crops</p> <p>50 Paved</p> <p>60 Buildings</p> <p>70 Deciduous trees</p> <p>80 Water</p> <p> </p> <p><strong>CO2 fluxes</strong></p> <p>Hourly mean CO2 fluxes estimated by the models diFUME, JSBACH, SUEWS and VPRM for the trees and lawns of the Zurich urban parks: Bullingerhof, Hardaupark, Fritschiwiese and Heiligfeld. GPP stands for gross primary productivity, Reco for ecosystem respiration and NEE for net ecosystem exchange. All fluxes are in units: μmol CO2 m-2 s-1.</p> <p>The parameter sets used by each model are presented in the Tables below. Further information on the dataset can be found in the submitted manuscript.</p> <p>Parameters used by diFUME model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>A_max</p> </td> <td> <p>15</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>maximum leaf gross photosynthetic rate</p> </td> </tr> <tr> <td> <p>a</p> </td> <td> <p>0.045</p> </td> <td> <p>mol CO<sub>2</sub> mol<sup>-1</sup> PAR</p> </td> <td> <p>quantum yield for CO2 assimilation</p> </td> </tr> <tr> <td> <p>a_1</p> </td> <td> <p>25</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient in Leuning (1995) model</p> </td> </tr> <tr> <td> <p>b_</p> </td> <td> <p>0.65</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient in β-factor formula</p> </td> </tr> <tr> <td> <p>b_1</p> </td> <td> <p>5</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient</p> </td> </tr> <tr> <td> <p>D_o</p> </td> <td> <p>0.3</p> </td> <td> <p>kPa</p> </td> <td> <p>empirically determined coefficient for the VPD scalar inside Leuning (1995) model</p> </td> </tr> <tr> <td> <p>D_sc</p> </td> <td> <p>1</p> </td> <td> <p>N/A</p> </td> <td> <p>daylight scalar for dark respiration inhibition during day (1: no inhibition)</p> </td> </tr> <tr> <td> <p>E_0</p> </td> <td> <p>487.75</p> </td> <td> <p>K</p> </td> <td> <p>temperature sensitivity parameter for soil respiration</p> </td> </tr> <tr> <td> <p>g_o</p> </td> <td> <p>0.01</p> </td> <td> <p>mol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>residual stomatal conductance for CO2 (g_s when Anet = 0, PAR = 0).</p> </td> </tr> <tr> <td> <p>Q_10</p> </td> <td> <p>1.85</p> </td> <td> <p>N/A</p> </td> <td> <p>temperature sensitivity of leaf respiration</p> </td> </tr> <tr> <td> <p>R_(l,ref)</p> </td> <td> <p>1.53</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>reference leaf respiration at Tair = 25 °C</p> </td> </tr> <tr> <td> <p>R_(S,ref)</p> </td> <td> <p>2.49</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>reference soil respiration at Tsoil = 10 °C</p> </td> </tr> <tr> <td> <p>T_opt</p> </td> <td> <p>23</p> </td> <td> <p>°C</p> </td> <td> <p>optimum air temperature for gross photosynthesis</p> </td> </tr> <tr> <td> <p>T_0</p> </td> <td> <p>-46</p> </td> <td> <p>°C</p> </td> <td> <p>low-temperature limit for soil respiration</p> </td> </tr> <tr> <td> <p>T_(ref,S)</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>reference soil temperature for soil respiration</p> </td> </tr> <tr> <td> <p>T_(ref,l)</p> </td> <td> <p>25</p> </td> <td> <p>°C</p> </td> <td> <p>reference air temperature for leaf respiration</p> </td> </tr> <tr> <td> <p>W</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>width of the bell-shape curve at f(T_air ) = 0.5</p> </td> </tr> <tr> <td> <p>θ_ref</p> </td> <td> <p>0.4</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>saturated soil volumetric water content </p> </td> </tr> <tr> <td> <p>θ_g</p> </td> <td> <p>0.1</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>minimum soil volumetric water content, limit to stomatal conductance</p> </td> </tr> <tr> <td> <p>θ_0</p> </td> <td> <p>0.04</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>minimum soil volumetric water content, limit to soil respiration</p> </td> </tr> </tbody> </table> <p> </p> <p>Parameters used by JSBACH model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Trees</strong></p> </td> <td> <p><strong>Lawn</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>J_max</p> </td> <td> <p>104.5</p> </td> <td> <p>148.6</p> </td> <td> <p>μmol(CO<sub>2</sub>) m<sup>-2</sup>(leaf) s<sup>-1</sup></p> </td> <td> <p>Maximum electron transport rate at 25 °C</p> </td> </tr> <tr> <td> <p>T_alt</p> </td> <td> <p>4.0–4.5</p> </td> <td> <p> </p> </td> <td> <p>°C</p> </td> <td> <p>Alternation temperature</p> </td> </tr> <tr> <td> <p>θ_cap</p> </td> <td> <p>0.32– 0.39</p> </td> <td> <p>0.32– 0.34</p> </td> <td> <p>m m<sup>-1</sup></p> </td> <td> <p>Volumetric soil field capacity</p> </td> </tr> <tr> <td> <p>θ_pwp</p> </td> <td> <p>0.13– 0.21</p> </td> <td> <p>0.135–0.165</p> </td> <td> <p>m m<sup>-1</sup></p> </td> <td> <p>Volumetric wilting point</p> </td> </tr> <tr> <td> <p>V_max</p> </td> <td> <p>55.0</p> </td> <td> <p>78.2</p> </td> <td> <p>μmol(CO<sub>2</sub>) m<sup>-2</sup>(leaf) s<sup>-1</sup></p> </td> <td> <p>Maximum carboxylation rate at 25 °C</p> </td> </tr> <tr> <td> <p>z_root</p> </td> <td> <p>0.5</p> </td> <td> <p>0.12</p> </td> <td> <p>m</p> </td> <td> <p>Root depth</p> </td> </tr> <tr> <td> <p>CC</p> </td> <td> <p>1.25</p> </td> <td> <p>1.25</p> </td> <td> <p>N/A</p> </td> <td> <p>Relative cost to produce one carbon</p> </td> </tr> <tr> <td> <p>f_faeces</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>N/A</p> </td> <td> <p>Fraction of carbon from herbivore faeces that goes into the green litter pool</p> </td> </tr> <tr> <td> <p>f_leaf</p> </td> <td> <p>0.4</p> </td> <td> <p>0.4</p> </td> <td> <p>N/A</p> </td> <td> <p>A fixed fraction of canopy maintenance respiration that makes up the dark respiration</p> </td> </tr> <tr> <td> <p>k</p> </td> <td> <p>0.1</p> </td> <td> <p>0.09</p> </td> <td> <p>N/A</p> </td> <td> <p>LAI growth rate during growth phase</p> </td> </tr> <tr> <td> <p>LAI_max</p> </td> <td> <p>3.6–4.1</p> </td> <td> <p>3.0</p> </td> <td> <p>m2 m-2</p> </td> <td> <p>Maximum leaf area index</p> </td> </tr> <tr> <td> <p>p</p> </td> <td> <p>veg:</p> <p>0.004</p> <p>rest:</p> <p>0.1</p> </td> <td> <p>growth:</p> <p>0.1</p> <p>dry:</p> <p>0.015</p> </td> <td> <p>N/A</p> </td> <td> <p>LAI shedding rate (trees: vegetative and rest phase; grass: growth and dry season)</p> </td> </tr> <tr> <td> <p>r_d</p> </td> <td> <p>0.605</p> </td> <td> <p>0.8602</p> </td> <td> <p>μmol(CO2) m-2(leaf) s-1</p> </td> <td> <p>Dark respiration at 25 °C, fraction of Vmax</p> </td> </tr> </tbody> </table> <p> </p> <p>Parameters used by SUEWS model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Trees</strong></p> </td> <td> <p><strong>Lawn</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>f_i</p> </td> <td> <p>0.21</p> </td> <td> <p>0.18</p> </td> <td> <p>N/A</p> </td> <td> <p>Fraction of each vegetation type i</p> </td> </tr> <tr> <td> <p>F_(pho,max,i)</p> </td> <td> <p>8.3</p> </td> <td> <p>8.92</p> </td> <td> <p>μmol m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>Maximum potential photosynthesis</p> </td> </tr> <tr> <td> <p>LAI_(max,i)</p> </td> <td> <p>4.8</p> </td> <td> <p>3</p> </td> <td> <p>m<sup>2</sup> m<sup>-2</sup></p> </td> <td> <p>Full leaf-on summertime value</p> </td> </tr> <tr> <td> <p>LAI_(min,i)</p> </td> <td> <p>0.66</p> </td> <td> <p>1.6</p> </td> <td> <p>m<sup>2</sup> m<sup>-2</sup></p> </td> <td> <p>Leaf-off wintertime value</p> </td> </tr> <tr> <td> <p>T_L</p> </td> <td> <p>-10</p> </td> <td> <p>-10</p> </td> <td> <p>°C</p> </td> <td> <p>Lower air temperature limit</p> </td> </tr> <tr> <td> <p>T_H</p> </td> <td> <p>55</p> </td> <td> <p>55</p> </td> <td> <p>°C</p> </td> <td> <p>Upper air temperature limit</p> </td> </tr> <tr> <td> <p>G_5</p> </td> <td> <p>30</p> </td> <td> <p>30</p> </td> <td> <p>°C</p> </td> <td> <p>Parameter related to temperature dependence</p> </td> </tr> <tr> <td> <p>G_3</p> </td> <td> <p>0.66</p> </td> <td> <p>0.538</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter related to VPD dependence</p> </td> </tr> <tr> <td> <p>G_4</p> </td> <td> <p>0.89</p> </td> <td> <p>0.87</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter related to VPD dependence</p> </td> </tr> <tr> <td> <p>G_6</p> </td> <td> <p>0.36</p> </td> <td> <p>0.55</p> </td> <td> <p>mm<sup>-1</sup></p> </td> <td> <p>Parameter related to soil moisture dependence</p> </td> </tr> <tr> <td> <p>G_2</p> </td> <td> <p>477</p> </td> <td> <p>263.5</p> </td> <td> <p>W m<sup>-2</sup></p> </td> <td> <p>Parameter related to dependence</p> </td> </tr> <tr> <td> <p>Δθ_WP</p> </td> <td> <p>132.5</p> </td> <td> <p>143</p> </td> <td> <p>mm</p> </td> <td> <p>Wilting point deficit</p> </td> </tr> <tr> <td> <p>K_(↓max)</p> </td> <td> <p>1200</p> </td> <td> <p>1200</p> </td> <td> <p>W m<sup>-2</sup></p> </td> <td> <p>Maximum incoming shortwave radiation</p> </td> </tr> <tr> <td> <p>a_i</p> </td> <td> <p>0.78</p> </td> <td> <p>1.7</p> </td> <td> <p>N/A</p> </td> <td> <p>Empirical soil and vegetation respiration coefficient</p> </td> </tr> <tr> <td> <p>b_i</p> </td> <td> <p>0.08</p> </td> <td> <p>0.06</p> </td> <td> <p>N/A</p> </td> <td> <p>Empirical soil and vegetation respiration coefficient</p> </td> </tr> <tr> <td> <p>ω_(1,GDD,i)</p> </td> <td> <p>0.04</p> </td> <td> <p>0.04</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(2,GDD,i)</p> </td> <td> <p>0.0005</p> </td> <td> <p>0.0005</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(1,SDD,i)</p> </td> <td> <p>-1.5</p> </td> <td> <p>-1.5</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(1,SDD,i)</p> </td> <td> <p>0.0025</p> </td> <td> <p>0.0025</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>GDD</p> </td> <td> <p>300</p> </td> <td> <p>300</p> </td> <td> <p>days</p> </td> <td> <p>The growing degree days (GDD) needed for full capacity of the leaf area index</p> </td> </tr> <tr> <td> <p>SDD</p> </td> <td> <p>-300</p> </td> <td> <p>-300</p> </td> <td> <p>days</p> </td> <td> <p>The senescence degree days (SDD) needed to initiate leaf off</p> </td> </tr> <tr> <td> <p>T_(base,GDD)</p> </td> <td> <p>5</p> </td> <td> <p>5</p> </td> <td> <p>°C</p> </td> <td> <p>Base Temperature for initiating growing degree days (GDD) for leaf growth</p> </td> </tr> <tr> <td> <p>T_(base,SDD)</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>Base temperature for initiating senescence degree days (SDD) for leaf off</p> </td> </tr> </tbody> </table> <p> </p> <p><span>Parameters used by VPRM model</span></p> <table> <tbody> <tr> <td> <p><strong><span>Parameter</span></strong></p> </td> <td> <p><strong><span>Trees</span></strong></p> </td> <td> <p><strong><span>Lawn</span></strong></p> </td> <td> <p><strong><span>Units</span></strong></p> </td> <td> <p><strong><span>Description</span></strong></p> </td> </tr> <tr> <td> <p><span>λ</span></p> </td> <td> <div> <p><span>-0.16</span></p> </div> </td> <td> <div> <p><span>-0.13</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>light use efficiency</span></p> </div> </td> </tr> <tr> <td> <p><span>PAR_0</span></p> </td> <td> <div> <p><span>356.99</span></p> </div> </td> <td> <div> <p><span>545.61</span></p> </div> </td> <td> <div> <p><span>μmol m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>half-saturation value</span></p> </div> </td> </tr> <tr> <td> <p><span>α</span></p> </td> <td> <div> <p><span>0.22</span></p> </div> </td> <td> <div> <p><span>0.40</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup> /<sup>0</sup>C</span></p> </div> </td> <td> <div> <p><span>empirical coefficient</span></p> </div> </td> </tr> <tr> <td> <p><span>β</span></p> </td> <td> <div> <p><span>1.09</span></p> </div> </td> <td> <div> <p><span>0.42</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>empirical coefficient</span></p> </div> </td> </tr> <tr> <td> <p><span>T_max</span></p> </td> <td> <div> <p><span>40</span></p> </div> </td> <td> <div> <p><span>40</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>maximum temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_min</span></p> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>2</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>minimum temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_opt</span></p> </td> <td> <div> <p><span>20</span></p> </div> </td> <td> <div> <p><span>18</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>optimal temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_low</span></p> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>to account for the persistence of soil respiration in winter</span></p> </div> </td> </tr> </tbody> </table> <p><span> </span></p> <p>Data format: comma separated values (csv)</p> <p>Time step: 60 min (aggregated)</p> <p>Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period</p> <p>Period: 01/2022–09/2023</p>
5405 MHz SigMF baseband recording of RCM-2 (Radarsat Constellation) using PlutoPlus SDR and four log periodic array (LPA) antennas
<p>This dataset contains a recording of the <a href="https://www.asc-csa.gc.ca/eng/satellites/radarsat/technical-features/radarsat-comparison.asp">RCM-2</a> (<a href="https://en.wikipedia.org/wiki/RADARSAT_Constellation">Radarsat Constellation</a>) satellite as it passed over Berkeley, California. The recording was made on 2024-08-13 and is about 15 seconds long, containing acquisition of signal pulses and loss of signal at the tail end of the recording. The dataset is stored in SigMF format. The data files are compressed with xz to reduce their size. The IQ sample rate is 20.0 Msps and the center frequency is 5405 MHz.</p> <p>The linear antenna array used to record contained four HT5 antennas, labeled as: "HT5 antenna UWB log-periodic antenna 1300MHz-10GHz". These four antennas were spaced 21 cm apart. The input from these four antennas was combined with a SP-TX-4B splitter/combiner using equal lengths of LMR400 coax, then amplified using an LNA labeled as "RF AMP 04A: TQP3M9037 0.1-6GHz". The LNA was powered via a +5 volt bias-tee. A "Pluto+" or Pluto Plus SDR was used to sample, with SatDump software. </p> <p> </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.