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Natural Land Zones in New England in 2006 from 2010 Wildlands and Woodlands Report
This GIS dataset shows different Wildland and Woodland zones in New England and is intended to illustrate how the percentage of protected land could vary between New England landscapes while achieving the total acreage goals as expressed in the Wildlands and Woodlands 2010 Report. This is layer is intended to be conceptual and not prescriptive. The zones were created by expressing the percent natural landcover for a given 500m pixel using a 25 x 25-cell neighborhood. The landuse categories considered to be natural and the breakpoints for each zone are given in the Methods section of this document. The data are provided in vector format as an ESRI shapefile. The projection is NAD 1983 Albers. The Wildlands and Woodlands webpage, http://www.wildlandsandwoodlands.org/, includes a downloadable high-resolution copy of the report and an extensive set of resources that complement and support the arguments and facts presented in it. These include citations, metadata, and downloadable high-quality images of all the graphs and maps. The website will be updated to provide further information on many of the findings, activities, and recommendations provided in the report. Wildlands and Woodlands Report: Foster, D. R., Lambert, K. F., Kittredge, D. B., Donahue, B. M, Hart, C. M., Labich, W. G., Meyer, S., Thompson, J. , Buchanan, M., Levitt, J. N., Pershel, R., Ross, K., Elkins, G., Daigle, C., Hall, B., Faison, E. K., D'Amato, A. W., Forman, R. T. T., Del Tredici, P., Irland, L. C., Colburn, B. A., Orwig, D. A., Aber, J. D., Berger, A., Driscoll, C. T., Keeton, W. S., Lilieholm, R. J., Pederson, N., Ellison, A. M., Hunter, M. L., Fahey, T. J. 2017. Wildlands and Woodlands, Farmlands and Communities: Broadening the Vision for New England.
Dataset of reports about MOF-based SERS substrates since 2011 until March 2023. Structure, characteristics, analytes, and performances.
<p>This dataset was generated to aid the creation of a review article addressing the use of Metal-Organic Frameworks (MOF)-based Surface Enhanced Raman Spectroscopy (SERS) platforms for the detection of Volatile Organic Compounds (VOCs).</p> <p>This dataset was generated employing the Web of Science database, encompassing manuscripts published up to March 2023. A literature search was initially conducted using a combination of keywords, including "MOF," "Metal-Organic Framework," "SERS," "Surface Enhanced Raman Spectroscopy," and "Surface Enhanced Raman Scattering." This search spanned the "Topic" category, enabling exploration across title, abstract, author keywords, and keyword-plus fields.</p> <p>From the initial pool of 238 documents, review articles and duplicates were systematically excluded, resulting in a refined collection of 182 articles. Subsequently, articles not concurrently addressing MOF and SERS or those utilizing MOF as sacrificial templates were further excluded, resulting in a final subset of 72 articles. From this curated set, relevant parameters were extracted, resulting in 229 entries for the dataset. </p> <p>Characteristics about the structure (in terms of MOF type and configuration; Plasmonic element type and configuration), target analyte (including type, phase, and incubation time), measurement specifications (in terms of laser, laser power, exposure time), and performance of the MOF-based SERS substrates were collected.</p> <p>Listed references 1-72 correspond with the manuscript number in the dataset.</p> <p>Listed references 73-80 correspond with references for selected examples of MOF pore diameters.</p>
Land Zones in New England 1940-2070 from 2017 Wildlands and Woodlands Report
This dataset contains two GIS datalayers, one CSV file and an associated R script used to produce figures for the 2017 Wildlands and Woodlands report. The “wedge diagram” data (HF360-01-landcover-data.csv) shows actual percent landcover estimates across New England from 1940 to 2010 and straight-line trends needed between 2010 to 2070 to reach the Wildlands and Woodland vision by 2060. The published diagram can be seen in figure 1 of the 2017 Wildlands and Woodland report. In hf360-03-community-forests.zip, the GIS layer shows towns that contain community forests as determined by the authors of the report. In hf360-04-wildland-woodland-zones.zip, the GIS layer shows Wildland and Woodland Zones as illustrated in the report. It was created by starting with the Wildland and Woodland zones from the 2010 report and adding in areas with high agricultural land use. These types are meant to be conceptual and not prescriptive. Wildlands and Woodlands Report: Foster, D. R., Lambert, K. F., Kittredge, D. B., Donahue, B. M, Hart, C. M., Labich, W. G., Meyer, S., Thompson, J. , Buchanan, M., Levitt, J. N., Pershel, R., Ross, K., Elkins, G., Daigle, C., Hall, B., Faison, E. K., D'Amato, A. W., Forman, R. T. T., Del Tredici, P., Irland, L. C., Colburn, B. A., Orwig, D. A., Aber, J. D., Berger, A., Driscoll, C. T., Keeton, W. S., Lilieholm, R. J., Pederson, N., Ellison, A. M., Hunter, M. L., Fahey, T. J. 2017. Wildlands and Woodlands, Farmlands and Communities: Broadening the Vision for New England.
MiRoR15-P1-Tools used to assess the quality of peer review reports: a methodological systematic review
<p>Database, data extraction form, R codes and protocol related to: Superchi C, González JA, Solà I, Cobo E, Hren D, Boutron I. <em>Tools used to assess the quality of peer review reports: a methodological systematic review</em>. BMC Med Res Methodol. 2019;19(48):1–14. DOI: <a href="https://doi.org/10.1186/s12874-019-0688-x">https://doi.org/10.1186/s12874-019-0688-x</a></p> <p> </p>
ABG-IMRHT and Ames-2000K IR line lists for N2O as reported in "Accurate N2O IR Line Lists with Consistent Empirical Line Positions: ABG-IMRHT and Ames-2000K"
<p><strong>[2025-06-17, v2.0</strong>, updated from v1.1 (<a href="https://doi.org/10.5281/zenodo.14834513">10.5281/zenodo.14834513</a>)]<br>(1). This upgraded version is recommended for analysis involving B1b or ABG(-IMRHT) related line lists, or E' > 10,000 cm<sup>-1</sup>. <br>(2). Energy level lists computed on the B1b PES and related 296K ABG-IMRHT line intensity and line lists, and B1b-based 1000-3000K line list files have been updated, along with n2olist.f90.v1.4. <br>(3). The full 296 K IR line lists of <strong>all 12 stable isotopologues</strong> are provided in "n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz", in which the line intensities assume 100% abundance for every isotopologue.<br>(4). Please note that the B1b PES-based energy levels in "empirical.corrections.for.ABG-IMRHT.zip" are still valid, but the B1b PES based energy levels are not updated in the files inside that .zip. </p> <p>[<strong>Link to <a href="https://www.sciencedirect.com/science/article/pii/S0022407325001645">the N2O paper</a> </strong>published at JQSRT (2025) 343, 109502, part of VSI:HITRAN2024, doi:<a href="https://doi.org/10.1016/j.jqsrt.2025.109502">10.1016/j.jqsrt.2025.109502</a>]</p> <p>[Updated from v1.0 (<a href="https://doi.org/10.5281/zenodo.14174307">10.5281/zenodo.14174307</a>). The J=151-210 transitions of <sup>14</sup>N<sub>2</sub><sup>16</sup>O were missing from v1.0 files ]</p> <p><strong>1. Second generation of Ames-296K IR line list for "natural" Nitrous Oxide (N<sub>2</sub>O), denoted ABG-IMRHT</strong>, which was computed from Ames-B1b PES refinement (using Benjamin Schröder's ab initio PES Comp I, doi:10.1515/zpch-2015-0622) and 2023 dmsG-10K accurately fitted from CCSD(T)/aug-cc-pV(T,Q,5)Z dipoles. In this major upgrade to Ames-296K N<sub>2</sub>O line list (10.1080/00268976.2023.2232892 and 10.5281/zenodo.7888194), rovibrational energy levels computed from NOSL-296 EH model were adopted to match and replace ~100,000 <sup>14</sup>N<sub>2</sub><sup>16</sup>O levels. More consistent empirical corrections are determined for multiple isotopologues from comparison with RITZ (IAO), MARVEL (ExoMol), HITRAN, and JPL datasets. The ABG-IMRHT line list provides the most reliable and consistent IR intensity predictions and accurate line positions in the range of 0 - 10,000 cm<sup>-1</sup>. All 12 stable isotopologues are included. </p> <p><strong>2. Ames-2000K</strong> IR line list provides complete, reliable and consistent IR predictions in the 0 - 15,000 cm<sup>-1</sup> range. It includes transitions of 12 isotopologues. Their intensities are scaled by corresponding terrestrial "natural" abundances. Ames-2000K is a composite list, including 4 component lists, to achieve better accuracy and reliability needs at both shorter and longer wavelengths: </p> <ul> <li>#1. a hot line list of <sup>14</sup>N<sub>2</sub><sup>16</sup>O, computed on Ames-1 PES and 2023 dmsG-wgt2d, J'<210, E'<25,000 cm<sup>-1</sup>, T=1000 / 1500 / 2000 / 3000 K. It occupies 24 GB in compressed .xz format. </li> <li>#2. 1000 K line lists of #2-#12 minor isotopologues, computed on Ames-1 PES and DMS, J'<150, E'<0.125 au - zpe (iso 2-6) or 0.08 -0.10 au - zpe (iso 7-12), S<sub>1000K </sub>> 10<sup>-34</sup> cm/molecule, size-reduction with 99.9% intensity conservation in cm<sup>-1</sup> bins. </li> <li>#3. a hot line list of <sup>14</sup>N<sub>2</sub><sup>16</sup>O, computed on Ames-B1b PES and 2023 dmsG-10Kcm<sup>-1</sup>, J'<150, E'<16,000 cm<sup>-1</sup>, T=1000 / 1500 / 2000 / 3000 K.</li> <li>#4. ABG-IMRHT IR line list at 296 K, J'<150, E'<16,000 cm<sup>-1</sup>, with best empirical line positions and highly consistent intensity predictions up to 10,000 cm<sup>-1</sup>. Coverage beyond 10,000 cm<sup>-1 </sup>is limited to strong lines.</li> </ul> <p><strong>3. List of files:</strong> (decompress .xz files first, "xz -dkf -T0 file.xz")</p> <ul> <li>IAO_N2O_levels.tar.xz: rovibrational energy levels of 6 N<sub>2</sub>O isotopologues, as computed using global Effective Hamiltonian (EH) models developed by Dr. Sergey Tashkun from IAO (Institute of Atmospheric Optics, Tomsk, Russia, <a href="https://www.iao.ru/">https://www.iao.ru/</a>). <br><br></li> <li>N2O.Ames-B1b.PES.and.Ames-2023.DMS.zip: Ames-B1b PES subroutine & coefficient file, see the note in n2opes2.f90; Ames 2023 dmsG subroutine and coefficients for fits up to 10K/12K/15K/17K/20K/25K cm<sup>-1</sup>, along with fitting residuals and compared to Ames-1 style (dmsC) coeffs and residuals. The geometry set has ~80 points in each 100 cm<sup>-1</sup>. <br><br></li> <li>n2olist.f90.v1.4: the main Fortran program for Ames-2000K generation, customizable, see the note at its beginning. <br>[ default Ames-2000K = Ames-1 (12 iso) + [B1b (446) + ABG-IMRHT (12 iso)] if (E'<15,000 cm-1 & J<=150) ]<br><br></li> <li>empirical.corrections.for.ABG-IMRHT.zip: subroutine and paired/corrected energy level lists used in the empirical correction of energy levels computed on B1b PES [<em>this file is not updated from v1.1 to v2.0</em>]<br><br></li> <li>n2o.partition.1-4000K.iso.1-12.scaled: partition sum for 12 isotopologues, input file required by n2olist.f90, excluding g_n<br>n2o.partition.1-4000K.iso.1-12.scaled.with.degeneracy.txt: same as above, g_n included, see the note inside<br><br></li> <li>list.of.n2o.xz.files : list of .xz files to read/regenerate from, under subdir "xz", input file required by n2olist.f90<br>ames.n2o.xx000-yy000.cm-1.xz: compressed data files for Ames-2000K (line list component #1+#2)<br><br></li> <li>n2o.iso1-12.levels.Ames-1.dat.xz: energy levels of 12 N<sub>2</sub>O isotopologues on Ames-1 PES, input file required by n2olist.f90<br><br></li> <li>n2o.446.B1b-PES.levels.xz: energy levels of <sup>14</sup>N<sub>2</sub><sup>16</sup>O computed on Ames-B1b PES, input file to n2olist.f90<br><br></li> <li>n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.compressed.xz: data file for <sup>14</sup>N<sub>2</sub><sup>16</sup>O hot list on Ames-B1b and dms 2023-G10K (component #3), input file required by n2olist.f90<br>n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.dat.xz: independent line list (component #3)<br><br></li> <li>n2o.iso1-12.levels.ABG-IMRHT.dat.xz: energy levels in ABG-IMRHT list, with empirical corrections included, input file required by n2olist.f90</li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5.corrected.xz: Latest Ames-296K line list with empirical energy level corrections (component #4), input file for n2olist.f90<br>n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5.corrected.xz: same as above, in HITRAN format, including line-broadening parameters<br><br></li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5corrected.100pct-abundance.xz: Latest Ames-296K line list with empirical energy level corrections, including the full set of IR transitions for <strong>12 stable isotopologues, assuming 100% abundannce </strong>for every isotopologue, and 1E-31 cm/molecule intensity cut-off at 296 K.</li> <li>n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz: same as above, in HITRAN format, including line-broadening parameters<br><br></li> <li>ames.n2o.intensity.xz: line count and intensity sum (original, selected, iso #1 and iso #2-12) in each 0.01 cm<sup>-1</sup> bins at 296 K, 1000 K, 1500 K, 2000 K, and 3000 K, for reference and statistics, optional input file to n2olist.f90<br><br></li> <li>ames.n2o.sint.reductions.xz: A check-point file during Ames-2000K file generation, for reference only. including # of lines (total & selected), intensity sum (total, iso #1, iso #2-12), and intensity retention ratio in each 0.01 cm<sup>-1</sup> bins for total, iso #1, and iso #2-12.<br><br></li> </ul> <p><strong>4. List of 12 isotopologues</strong>, abundances adopted, and number of lines in ABG-IMRHT and Ames-2000K line list, <br>[updated 2025-06-17 v2.0: the #lines in ABG-IMRHT are updated to S(296K) cut-off at 1E-31 cm/molecule, instead of 5E-31]</p> <table> <tbody> <tr> <td>#</td> <td>ISO</td> <td>abundance</td> <td># lines in ABG-IMRHT</td> <td># lines in Ames-2000K</td> </tr> <tr> <td>1</td> <td>446</td> <td>0.990333</td> <td>1388017</td> <td>3014643103</td> </tr> <tr> <td>2</td> <td>456</td> <td>3.64093E-3</td> <td>375541</td> <td>97932924</td> </tr> <tr> <td>3</td> <td>546</td> <td>3.64093E-3</td> <td>411353</td> <td>118432059</td> </tr> <tr> <td>4</td> <td>448</td> <td>1.98582E-3</td> <td>377552</td> <td>88169721</td> </tr> <tr> <td>5</td> <td>447</td> <td>3.69280E-4</td> <td>238921</td> <td>40212389</td> </tr> <tr> <td>6</td> <td>556</td> <td>1.33858E-5</td> <td>93933</td> <td>7534736</td> </tr> <tr> <td>7</td> <td>548</td> <td>7.30080E-6</td> <td>93674</td> <td>5583963</td> </tr> <tr> <td>8</td> <td>458</td> <td>7.30080E-6</td> <td>86446</td> <td>4995485</td> </tr> <tr> <td>9</td> <td>547</td> <td>1.35765E-6</td> <td>55360</td> <td>2588347</td> </tr> <tr> <td>10</td> <td>457</td> <td>1.35765E-6</td> <td>50754</td> <td>2275332</td> </tr> <tr> <td>11</td> <td>558</td> <td>2.68412E-8</td> <td>15814</td> <td>500190</td> </tr> <tr> <td>12</td> <td>557</td> <td>4.99134E-9</td> <td>8537</td> <td>274623</td> </tr> <tr> <td> </td> <td>Total</td> <td>1.00000069</td> <td>3195902</td> <td>3383142872</td> </tr> </tbody> </table> <p>5. This project is funded by NASA Grant 18-APRA18-0013 through NASA/SETI Institute Co-operative Agreement 80NSSC20K1358. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</p>
Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report
<p>This is a comprehensive data repository of the <em>data management survey</em> carried out in Autumn of 2023 through a collaboration between the <a href="https://opensciencestudies.eu/">PHIL_OS</a> project and the <a href="https://agres.elgo.gr/">Research Directorate of the Hellenic Agricultural Organization ELGO-DIMITRA</a>.</p> <p>Please cite as: </p> <blockquote> <p>Tsiroukis F., Leonelli S. and ELGO-DIMITRA (2024) <em>Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report.</em> PHIL_OS Report. DOI: 10.5281/zenodo.14003418</p> </blockquote>
STAR4BBS D1.3 Report impact and contribution SCS and Labels_Appendix II dataset
<p>This dataset contains the full coding sheet for the systematic mapping that formed STAR4BBS deliverable D1.3 (Appendix II). The systematic mapping exercise reviewed literature on the impact of and contribution to GHG emissions reductions of existing sustainability systems and certification schemes (SCS) and B2B labels used within the bioeconomy. A coding sheet in the context of a systematic map is a structured tool used to extract and record specific data from studies being reviewed, ensuring consistency and accuracy in data collection. It forms the basis of the analysis and is included for transparency.</p>
Data for SARS-CoV-2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)
<p>This version contains a single file, with time series data for the most recent <a href="https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/surveillance-reports/sarscov2-reinfection-trends-in-south-africa-monthly-report/">monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa</a>:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> </ul> <p>Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data.</p> <p> </p> <p>Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. <a href="https://www.science.org/doi/10.1126/science.abn4947">Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa</a>. DOI: 0.1126/science.abn4947</p> <p>For code and more details see: <a href="https://github.com/jrcpulliam/reinfections/releases/tag/v3.0">https://github.com/jrcpulliam/reinfections/releases/tag/v3.0</a> or <a href="https://zenodo.org/record/6108448">10.5281/zenodo.6108448</a></p> <p>The version of this data set associated with the publication (available via the links above) included the following files:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> <li><code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or >0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>)</li> <li><code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript)</li> <li><code>sim_90_null.RDS</code> - simulation results (as used in the manuscript)</li> <li><code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)</li> </ul>
Wearable data and self reported fatigue scores from a remote observational study in Sjogren's disease, SLE and healthy participants
<p>Fatigue is a subjective, complex, and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. To date, there is no consensus about reliable quantitative assessments of fatigue.</p> <p>We collected observational data for a period of one month from 296 participants (healthy volunteers, Sjogren’s Syndrome, and Systemic Lupus Erythematosus patients) in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity, and sleep daily features, and app-based daily and weekly questions (e.g., pain, mood, general physical activity, and fatigue). When matching both sensor data and PROs, and excluding missing data, the dataset contains data from 183 subjects and 3950 recording days.</p> <p>The analysis of the association of digital data to self-reported fatigue was published at <em><strong>Rao C., et. al. (2023), Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease, Frontiers in Digital Health</strong></em>.</p> <p>Demographics, digital parameters, and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Details on the data files can be found under README.txt.</p>
MCR LTER: Coral Reef: Growth-predation risk trade-offs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats; Data for Ladd et al., 2025, Scientific Reports.
This dataset is in support of the manuscript: Growth-predation risk tradeoffs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats. These data were collected to 1) document how Acropora pulchra is distributed around the island of Moorea, and 2) to better understand the ecological processes that shape that distribution. Data include 1) results from surveys around the island of Moorea documenting the presence and size distribution of Acropora pulchra thickets, 2) results from an experiment measuring the growth and survivorship of Acropora pulchra fragments in the presence and absence of fish predators at nearshore fringing reef sites and adjacent sites in the mid lagoon (n = 20 sites in total), and 3) ancillary data on nitrogen content and dN15 in the tissue of the macroalgae Turbinaria ornata, sediment accumulation, and corallivore biomass at the experimental sites. All data were collected in 2016 and 2017.
Input files for Dispa-SET for the JRC report "Power System Flexibility in a variable climate"
<p><strong>Input files for Dispa-SET for the JRC report "Power System Flexibility in a variable climate"</strong></p> <p>Here you can find the input files needed to reproduce the results of the <a href="https://doi.org/10.2760/75312">report</a>:</p> <pre><code>De Felice, M., Busch, S., Kanellopoulos, K., Kavvadias, K. and Hidalgo Gonzalez, I., Power system flexibility in a variable climate, EUR 30184 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-18183-5 (online), doi:10.2760/75312 (online), JRC120338. </code></pre> <p>The results in the report are generated with the Dispa-SET power system model, available and explained at <a href="https://www.dispaset.eu/">www.dispaset.eu</a>.</p> <p>A description of the data sources with the references can be found into the report.</p> <p><strong>How to use this dataset</strong></p> <p>This dataset can be used as input data for the Dispa-SET model. We refer to the <a href="https://doi.org/10.2760/75312">report</a> and the <a href="https://www.dispaset.eu">official model documentation</a> for information about the data and the model.</p> <p><strong>Description of the dataset</strong></p> <p>The file <code>EnVarClim.yml</code> is a template of the YAML configuration file used by Dispa-SET. To run a specific climate year the <code>XXXX</code> present in some input files must be replaced with the year.</p> <p><strong>Availability factors</strong></p> <p>In the folder <code>AvailabilityFactors</code> there are the availability factors (from 0 to 1) for the power plants and the renewable generation. There is a subfolder for each simulated zone and inside a file for each climate year: from <code>emh_and_cc_availability_1990.csv</code> to <code>emh_and_cc_availability_2015.csv</code>.</p> <p><strong>Cross-border transmission</strong></p> <p>In the folder <code>DayAheadNTC</code> there is the file <code>merged_constant_NTC.csv</code> containing the capacity (in MW).</p> <p><strong>NOTE</strong>: due to an error in the pre-processing code there are some additional lines for the Western Balkans countries ending with a <code>1</code> (e.g. <code>GR -> MK1</code>). Those lines are ignored by the model because are not associated to any simulated zone.</p> <p><strong>Cross-border historical flows</strong></p> <p>In the file <code>CC_L_flows.csv</code> under the folder <code>Flows</code> are contained the hourly flows between the simulated zones and their neighbours (RU, TR, UA).</p> <p><strong>Fuel prices</strong></p> <p>In the folder <code>FuelPrices</code> are contained a set of files containing the hourly prices for the fuels (biomass, coal, lignite, gas, oil) and CO2 emissions. It is worth noting that in spite of their hourly resolution the time-series are constant through the year.</p> <p><strong>Hourly load</strong></p> <p>In the folder <code>Load_RealTime</code> there are hourly load time-series for each zone considering a different climate year. For the Western Balkans countries we use the same time-series for each climate year.</p> <p><strong>Outage factors</strong></p> <p>The files <code>CC_L_outages.csv</code> in the folder <code>OutageFactors</code> contain the outage factor (from 1, full outage, to 0) for the various generation units. Whenever a simulation zone is missing the model assumes the absence of outages.</p> <p><strong>Power plants data</strong></p> <p>In the folder <code>PowerPlants</code> there is a file named <code>CC_L_plants.mip.csv</code> for each simulated zone. The CSV files contain the data <a href="http://www.dispaset.eu/en/latest/data.html#power-plant-data">needed by Dispa-SET</a>.</p> <p><strong>Water storage levels</strong></p> <p>The folder <code>ReservoirLevel</code> contains the storage level (values from 0 to 1 relative to the size of the storage) for all the simulated zones. The levels have been computed for each climate year using a different inflow using the <a href="http://www.dispaset.eu/en/latest/mid_term.html">mid-term scheduler</a> recently implemented in Dispa-SET. For the Western Balkans countries we use the same time-series for each climate year.</p> <p><strong>Hydro-power inflows</strong></p> <p>In the folder <code>ScaledInflows</code> are contained the inflows used for the hydro-power generation. The values in the CSV files describes how much energy is available for hydro-power generation compared to the installed capacity.</p> <p><strong>Linked resources</strong></p> <ul> <li>Model output files:<strong> </strong>https://zenodo.org/record/3778133</li> <li>Source code for the figures: https://github.com/energy-modelling-toolkit/figures-JRC-report-power-system-and-climate-variability</li> </ul>
MiRoR15-P2-Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research
<p>Survey questionnaire, anonymised survey data, and codebook related to: Superchi C, Hren D, Blanco D, Rius R, Recchioni A, Boutron I, González JA. Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research. BMJ Open 2020;0:e035604. doi:10.1136/bmjopen-2019-035604</p>
IPBES Data Management Tutorials - Session 3.4: Data management report details: File formats
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session, <em>Data management report details: File formats</em>, focuses on specific recommended file formats for text, tabular data, images, sound, and geospatial data. </p>
IPBES Data Management Tutorials - Session 3.7: Data management report details: Long-term storage details
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session, <em>Data management report details: Long-term storage details</em>, explores the reasons why IPBES recommends Zenodo as a long-term repository. </p>
Continuous multi-sensor wearable data and daily subject-reported fatigue of heathy adults
<p>Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are limited. We collected a total of 28 healthy adult subjects and 973 recording days. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters at 1-minute temporal resolution, and daily questionnaires (patient-reported outcome scores, PROs) on fatigue. When matching both sensor data and PROs, the datasets contains data from 27 subjects and 405 recording days.</p> <p>Analysis of these multimodal digital data to inform, quantify, and augment subjectively captured non-pathological fatigue measures were published at <em>Luo H., et. al. (2020), Assessment of Fatigue Using Wearable Sensors: A Pilot Study. Digit Biomark</em>.</p> <p>Demographics, sensor parameters and other information on this dataset can be found in the aforementioned manuscript and related supplementary material.</p> <p>Files included are</p> <ul> <li><em>fatiguePROs.csv</em>: daily PROs for all subjects</li> <li>subjectID_*.csv: sensor time series for each subject</li> </ul>
IPBES Data Management Tutorials - Session 3.6: Data management report details: Data sharing and access considerations
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management c</em>hapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session <em>Data sharing and access considerations </em>covers details on licenses, exceptions to data sharing, and intellectual property considerations. </p>
INFORMATE Project - CHORUS Report Summaries - 20231106
<p>These data provide a summary of the All, Author Affiliation, and Dataset Reports generated by the <a href="https://dashboard.chorusaccess.org/">CHORUS Dashboard</a> for three agencies: the U.S. National Science Foundation, U.S. Geological Survey, and the U.S. Agency for International Development. The reports summarized here was collected on November 6-7, 2023 as part of the INFORMATE Project funded by NSF.</p><p>The columns are:</p><p>Column Definition</p><p>agency The funding agency [NSF, USGS, or USAID]</p><p>date. The date of data retrieval (YYYYMMDD)</p><p>report. The report [all, authors, datasets]</p><p>Property Name of the column in the input file</p><p>count Number of values (rows) of the property</p><p>unique Number of unique values of the property</p><p>top Most common value of the property</p><p>freq Number of occurrences (frequency) of the most common value</p><p>Count % The percentage of rows that include the property</p>
Dataset - Terminology of e-Oral Health: Consensus Report of the IADR's e-Oral Health Network Terminology Task Force.
<p>README<br>====================<br>This repository contains the data and documentation for a research project. It includes the dataset,<br>which is provided in CSV format and the original PDF with the survey answers.</p> <p>Research Information<br>====================<br>Terminology of e-Oral Health: Consensus Report of the IADR’s e-Oral Health Network Terminology<br>Task Force. Authors reported multiple definitions of e-oral health and related terms, and used several definitions<br>interchangeably, like mhealth, teledentistry, teleoral medicine and telehealth. The International<br>Association of Dental Research e-Oral Health Network (e-OHN) aimed to establish a consensus on<br>terminology related to digital technologies used in oral healthcare.</p> <p>This dataset contains data from a survey about digital oral health. The survey asked participants to provide their definition of various terms related to digital oral health, as well as their agreement with the provided definitions. The dataset also includes three figures that the participants were asked to review.</p> <p>The purpose of this dataset is to collect data on the public's understanding of digital oral health terms and to identify areas where there may be confusion or misinterpretation. The data from this dataset could be used to develop educational materials or to improve the way that digital oral health information is communicated to the public.</p> <p>Additional notes<br>====================<br>The data is not currently cleaned or preprocessed.</p> <p>Dataset<br>====================<br>The dataset file, named "dataset.csv," is in this repository. It contains the raw anonymized data<br>collected from the participants in a structured format. Each row represents a respondent, and the<br>columns correspond to different variables.</p> <p>Codebook<br>====================<br>The codebook file, named "codebook.pdf," is also included in this repository. It provides a<br>comprehensive description of the variables present in the dataset. The codebook outlines each<br>variable's meaning, type, and possible values, allowing users to understand and analyze the data<br>effectively.</p> <p>Metadata<br>====================<br>No metadata is provided</p> <p>Files<br>====================<br>01_readme.txt this readme file<br>02_codebook.pdf The codebook of the dataset<br>03_dataset.csv The dataset in csv format<br>04_e-OHN Delphi (2023-02-03).pdf The output from the survey</p> <p>Usage<br>====================<br>To work with the dataset, you can download the "dataset.csv" file and import it into your preferred<br>software or programming language for analysis. The codebook provides valuable information about<br>the variables, allowing you to understand the data structure and make informed decisions during your<br>analysis.<br>Please note that while every effort has been made to ensure the accuracy and quality of the data, it is<br>important to review the codebook and understand the context of the research before concluding the<br>dataset.</p> <p>License<br>====================<br>The data and documentation in this repository are provided under the CC BY-SA.<br>This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. If you remix, adapt, or build upon the material, you must license the modified material under identical terms. CC BY-SA includes the following elements:</p> <p> BY: credit must be given to the creator.<br> SA: Adaptations must be shared under the same terms.<br> <br>Please refer to the license file for further details on how the data can be used and shared.</p> <p>Contact Information<br>====================<br>For any questions, clarifications, or inquiries related to the dataset or research project, please contact<br>Assoc Prof Dr Sergio Uribe, sergio.uribe@rsu.lv</p>
Experimental data for "Measurement Report: Influence of particle density on secondary ice production by graupel and ice pellet collisions"
<p>This dataset includes measurement data on secondary ice production due to bare graupel - bare graupel, and ice pellet - ice pellet collisions carried out in the Mainz Cold Room (M-CR) of the Johannes Gutenberg University of Mainz. </p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Croatia
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2022_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2021_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2020_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2019_HR: Ministry of Agriculture (MPS)</li> </ul>
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.