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3,673 results for “, Practices”
Comparison and practical review of segmentation approaches for label-free microscopy
<p>This dataset contains microscopic images of PNT1A cell line captured by multiple microcopic without use of any labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation. </p> <p>See Vicar et al. Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison. BMC Bioinformatics (2019) 20:360. DOI <a href="https://doi.org/10.1186/s12859-019-2880-8">10.1186/s12859-019-2880-8</a></p> <p>Code using this dataset is available at <a href="https://github.com/tomasvicar/Cell-segmentation-methods-comparison">https://github.com/tomasvicar/Cell-segmentation-methods-comparison</a></p> <p><strong>Materials and methods </strong></p> <p>Cells were cultured in RPMI-1640 medium supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml) with 10% fetal bovine serum. Prior microscopy acquisition, cells were maintained at 37 cenigrade in a humidified incubator with 5% CO2. Intentionally, high passage number of cells was used (>30) in order to describe distinct morphological heterogeneity of cells (rounded and spindle-shaped, relatively small to large polyploid cells). For acquisition purposes, cells were cultivated in Flow chambers µ-Slide I Luer Family (Ibidi, Martinsried, Germany).</p> <p>Quantitative phase imaging (QPI) microscopy was performed on Tescan Q-PHASE (Tescan, Brno, Czech republic), with objective Nikon CFI Plan Fluor 10x/0.30 captured by Ximea MR4021MC (Ximea, Münster, Germany). Imaging is based on the original concept of coherence-controlled holographic microscope \cite{Kolman:10,Slaby:13}, images are shown in grayscale with units of pg/µm2.</p> <p>DIC microscopy was performed on microscope Nikon A1R (Nikon, Tokyo, Japan), with objective Nikon CFI Plan Apo VC 20x/0.75 captured by CCD camera Jenoptik ProgRes MF (Jenoptik, Jena, Germany). </p> <p>HMC microscopy was performed on microscope Olympus IX71 (Olympus, Tokyo, Japan), with objective Olympus CplanFL N 10x/0.3 RC1 captured by CCD camera Hamamatsu Photonics ORCA-R2 (Hamamatsu Photonics K.K., Hamamatsu, Japan).</p> <p>PC microscopy was performed on a Nikon Eclipse TS100-F microscope, with a Nikon CFI Achro ADL 10x/0.25 objective captured by CCD camera Jenoptik ProgRes MF.</p> <p><strong>Folder structure and file and filename description</strong><br> <br> <em>folder "source data+groundtruth"</em><br> - includes raw microscopic data <br> (uncompressed 16-bit for DIC, HMC and PC, 32-bit for QPI)<br> - includes manualy annotated groundtruth (zip file - imageJ ROI file, 1bit png mask)</p> <p>e.g. <br> DIC_01_raw.tif<br> DIC_01_groundtruth_imagejROI.zip<br> DIC_01_groundtruth_mask.png</p> <p><br> <em>folder "reconstructions"</em></p> <p>includes reconstructed images using reconstructions with highest dice coefficient achieved. </p> <p>for DIC and HMC: rDIC-Koos, rDIC-Yin, and rWeka<br> for PC: rPC-Top-Hat, rDIC-Yin, and rWeka<br> for QPI: rWeka</p> <p>note that for rWeka images numbered 01 for DIC, HMC and PC and 01-03 for QPI were used for learning.</p> <p><strong>Abbreviations</strong><br> DIC, differential image contrast<br> HMC, Hoffman modulation contrast<br> PC, phase contrast<br> QPI, quantitative phase imaging<br> rDIC-Koos, DIC/HMC image reconstruction according to Koos et al, Sci Rep. 2016;6:30420<br> rDIC-Yin, DIC/HMC image reconstruction according to Yin et al, Inf Process Med Imaging. 2011;22:384-97.<br> rPC-Yin, PC image reconstruction according to Yin et al, Med Im Anal. 2012; 16(5):1047<br> rPC-Top-Hat, Top-Hat filter according to Dewan et al, IEEE Transactions on Biomedical Circuits and<br> Systems.2014;8(5):716-728<br> rWeka, probability map using Trainable Weka segmentation according to Arganda-Carreras et al. Bioinformatics. 2017</p>
Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions
<p>This data set corresponds to the paper: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions [1] (Experiment: Comprehensive reporting).</p> <p>The key research questions corresponding to this data set were:</p> <p>RQ1: What is the role of challenges for the field of biomedical image analysis (e.g. How many challenges conducted to date? In which fields? For which algorithm categories? Based on which modalities?)</p> <p>RQ2: What is common practice related to challenge design (e.g. choice of metric(s) and ranking methods, number of training/test images, annotation practice etc.)? Are there common standards?</p> <p>RQ3: Does common practice related to challenge reporting allow for reproducibility and adequate interpretation of results?</p> <p>To address these research questions, we aimed to capture all biomedical image analysis challenges that have been conducted up to 2016. To acquire the data, we analyzed the websites hosting/representing biomedical image analysis challenges, namely grand-challenge.org, dreamchallenges.org and kaggle.com as well as websites of main conferences in the field of biomedical image analysis, namely Medical Image Computing and Computer Assisted Intervention (MICCAI), International Symposium on Biomedical Imaging (ISBI), International Society for Optics and Photonics (SPIE) Medical Imaging, Cross Language Evaluation Forum (CLEF), International Conference on Pattern Recognition (ICPR), The American Association of Physicists in Medicine (AAPM), the Single Molecule Localization Microscopy Symposium (SMLMS) and the BioImage Informatics Conference (BII). This yielded a list of 150 challenges with 549 tasks.</p> <p>Next, a tool for instantiating the challenge parameter list introduced in [1] was used by some of the authors (engineers and medical student) to formalize all challenges that met our inclusion criteria as follows: (1) Initially, each challenge was independently formalized by two different observers. (2) The formalization results were automatically compared. In ambiguous cases, when the observers could not agree on the instantiation of a parameter - a third observer was consulted, and a decision was made. When refinements to the parameter list were made, the process was repeated for missing values. Based on the formalized challenge data set, a descriptive statistical analysis was performed to characterize common practice related to challenge design and reporting.</p> <p>[1] Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A. P., Carass, A., Feldmann, C., Frangi, A. F., Full, P. M., van Ginneken, B., Hanbury, A., Honauer, K., Kozubek, M., Landman, B. A., März, K., Maier, O., Maier-Hein, K., Menze, B. H., Müller, H., Neher, P. F., Niessen, W., Rajpoot, N., Sharp, G. C., Sirinukunwattana, K., Speidel, S., Stock, C., Stoyanov, D., Aziz Taha, A., van der Sommen, F., Wang, C.-W., Weber, M.-A., Zheng, G., Jannin, P., Kopp-Schneider, A.: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions. arXiv preprint arXiv:1806.02051 (2018).</p>
Quantitative assessment of research data management practice - University of Bordeaux
<p>This survey was run at the University of Bordeaux in January 2019 using the questionnaire "Quantitative assessment of research data management practice" :</p> <p>Teperek, M., Krause, J., Lambeng, N., Blumer, E., van Dijck, J., Eggermont, R., … der Velden, Y. T. (2019). Quantitative assessment of research data management practice. Retrieved from : <a href="https://osf.io/mz3fx/">https://osf.io/mz3fx/</a></p> <p>The questionnaire included all the primary and secondary common questions, institution-specific questions regarding services and file sharing (EPFL questions), institution-specific questions for profile information.</p> <p>Data from the 425 responses collected are published here.</p> <p>Details regarding data collection and curation are included in the README file.</p> <p> </p>
Participatory activities good practices in the field of cultural heritage (REACH project)
<p>The REACH repository of good practices comprises over a hundred and twenty records of European and extra European participatory activities in the field of cultural heritage, with an emphasis on small-scale, localised examples, but including also larger collaborative projects and global or distributed online initiatives. Located in over twenty different countries, the activities showcased here cover a wide variety of topics and themes, from urban, rural and institutional heritage to indigenous and minority heritage; from preservation, and management to use and re-use of cultural heritage. This easy-to-use collection of good practices offers professionals, practitioners, researchers and citizens useful information about activities which could be transferred, adapted or replicated in new contexts.</p>
Post-trial access practice in Malaria, Tuberculosis, and NTDs Clinical Trial studies in Sub-Saharan African countries, quantitative study
<p>This is the data set used <span>to evaluate post trial access plan and implementation practice on TB, Malaria and NTD clinical trial studies conducted in the sub-Saharan African countries. </span></p>
Behavioral and fMRI Data: Nurturing the reading brain: Home literacy practices are associated with children's neural response to printed words through vocabulary skills
<p>This is the behavioral and fMRI dataset described in "Nurturing the reading brain: Home literacy practices are associated with children’s neural response to printed words through vocabulary skills". </p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu">GDPR</a>), we cannot provide raw MRI data. Therefore, the fMRI data consists of individual pre-processed volumes, normalized into the MNI template (see paper for details about the preprocessing pipeline). Anonymized behavioral data and first level analyses are also provided for each participant (SPM.mat file as well as beta, con, spmT, RPV and ResMS files). Note that the dataset also include runs and GLM results for a third task (Dots) that was not analyzed in the paper. Finally, the <a href="https://www.psychopy.org">PsychoPy</a> implementation of the tasks is also provided. If you have any questions, please send an email to jerome.prado [at] univ-lyon1.fr. </p> <p><strong>IMPORTANT:</strong></p> <p>In accordance with EU privacy regulations, we ask that you sign and return a Data Use Agreement (DUA) before downloading the data. You can download the DUA <a href="https://zenodo.org/record/4965716/files/DUA.pdf?download=1">here</a>. Please, sign it and send it to jerome.prado [at] univ-lyon1.fr.</p>
Base rates of food safety practices in European households: Summary data from the SafeConsume Household Survey
<p>This data set contains estimates of the base rates of 550 food safety-relevant food handling practices in European households. The data are representative for the population of private households in the ten European countries in which the SafeConsume Household Survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK).</p> <p><em>Sampling design</em></p> <p>In each of the ten EU and EEA countries where the survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK), the population under study was defined as the private households in the country. Sampling was based on a stratified random design, with the NUTS2 statistical regions of Europe and the education level of the target respondent as stratum variables. The target sample size was 1000 households per country, with selection probability within each country proportional to stratum size.</p> <p><em>Fieldwork</em></p> <p>The fieldwork was conducted between December 2018 and April 2019 in ten EU and EEA countries (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, United Kingdom). The target respondent in each household was the person with main or shared responsibility for food shopping in the household. The fieldwork was sub-contracted to a professional research provider (Dynata, formerly Research Now SSI). Complete responses were obtained from altogether 9996 households.</p> <p><em>Weights</em></p> <p>In addition to the SafeConsume Household Survey data, population data from Eurostat (2019) were used to calculate weights. These were calculated with NUTS2 region as the stratification variable and assigned an influence to each observation in each stratum that was proportional to how many households in the population stratum a household in the sample stratum represented. The weights were used in the estimation of all base rates included in the data set.</p> <p><em>Transformations</em></p> <p>All survey variables were normalised to the [0,1] range before the analysis. Responses to food frequency questions were transformed into the proportion of all meals consumed during a year where the meal contained the respective food item. Responses to questions with 11-point Juster probability scales as the response format were transformed into numerical probabilities. Responses to questions with time (hours, days, weeks) or temperature (C) as response formats were discretised using supervised binning. The thresholds best separating between the bins were chosen on the basis of five-fold cross-validated decision trees. The binned versions of these variables, and all other input variables with multiple categorical response options (either with a check-all-that-apply or forced-choice response format) were transformed into sets of binary features, with a value 1 assigned if the respective response option had been checked, 0 otherwise.</p> <p><em>Treatment of missing values</em></p> <p>In many cases, a missing value on a feature logically implies that the respective data point should have a value of zero. If, for example, a participant in the SafeConsume Household Survey had indicated that a particular food was not consumed in their household, the participant was not presented with any other questions related to that food, which automatically results in missing values on all features representing the responses to the skipped questions. However, zero consumption would also imply a zero probability that the respective food is consumed undercooked. In such cases, missing values were replaced with a value of 0.</p>
Stories on Open Educational Practices in German Higher Education
<p><strong>Stories on Open Educational Practices in German Higher Education by Sigrid Fahrer, </strong><a href="#_oewao57q7xt8"><strong>Tamara Heck</strong></a><strong>, </strong><a href="#_fronuh2cauex"><strong>Ronny Röwert</strong></a><strong>, </strong><a href="#_oshco7u6xx6k"><strong>Naomi Truan</strong></a></p> <p><em>citation suggestion: </em>Fahrer, S., Heck, T., Röwert, R., Truan, N. (2022). Stories on Open Educational Practices in German Higher Education. Data set on autoethnographic reflections. <a href="https://doi.org/10.5281/zenodo.7326390">https://doi.org/10.5281/zenodo.7326390</a></p> <p>The stories are part of the autoethnographic reflections of the four practitioners. They are based on the following research papers:</p> <ul> <li>Cronin, C. (2017). Openness and Praxis: Exploring the Use of Open Educational Practices in Higher Education. The International Review of Research in Open and Distributed Learning, 18(5). <a href="https://doi.org/10.19173/irrodl.v18i5.3096">https://doi.org/10.19173/irrodl.v18i5.3096</a></li> <li>Hegarty, B. (2015). Attributes of Open Pedagogy: A Model for Using Open Educational Resources. Educational Technology, 55(4), 3–13. <a href="https://upload.wikimedia.org/wikipedia/commons/c/ca/Ed_Tech_Hegarty_2015_article_attributes_of_open_pedagogy.pdf">https://upload.wikimedia.org/wikipedia/commons/c/ca/Ed_Tech_Hegarty_2015_article_attributes_of_open_pedagogy.pdf</a></li> <li>Mayrberger, K. (2020). Open Educational Practices (OEP) in Higher Education. In M. A. Peters (Ed.), Springer eBook Collection. Encyclopedia of Educational Philosophy and Theory (pp. 1–7). Springer. <a href="https://doi.org/10.1007/978-981-287-532-7_710-1">https://doi.org/10.1007/978-981-287-532-7_710-1</a>.</li> <li>Wiley, D., & Hilton III, J. L. (2018). Defining OER-Enabled Pedagogy. The International Review of Research in Open and Distributed Learning, 19(4). <a href="https://doi.org/10.19173/irrodl.v19i4.3601">https://doi.org/10.19173/irrodl.v19i4.3601</a></li> </ul>
Der königlich sächsische Hausorden der Rautenkrone. Genese, Verfasstheit und Verleihungspraxis eines Hausordens des 19. Jahrhunderts (The Royal Saxon House Order of the Rue Crown. Origin, constitution and award practice of a house order of the 19th century.)
<p>This data set was produced as part of a <a href="https://www.academia.edu/86314498/Der_königlich_sächsische_Hausorden_der_Rautenkrone_Genese_Verfasstheit_und_Verleihungspraxis_eines_Hausordens_des_19_Jahrhunderts">bachelor's thesis on the Royal Saxon House Order of the Rue Crown</a> (<em>Orden der Rautenkrone</em>) at the University of Greifswald. The thesis examines the award practices of the Grand Masters of the Order and attempts to draw conclusions about social circumstances. </p> <p>For the work, a data set was created that includes all knights of the Order of the Rue Crown in the period from 1807 to 1918. The names of the beloved were expanded to include a standardised name (GND) and their life data, GND/Wikidata identifier and main geographical affiliation as well as rank and profession. </p> <p>The data here are provided as Numbers and Excel files. Furthermore, the individual tables have been exported into CSV format (Note: in Excel, the CSV files may be displayed incorrectly despite UTF-8 encoding - especially with special characters and umlauts)</p> <p>The dates are not yet completely accurate. For example, in the case of the standardised names, since the persons concerned may have received the corresponding status (king, etc.) only later after the award. The data sets are in constant development. If you have additional information about an entry or have discovered an error, please feel free to contact me. </p>
Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> These data represent estimated mean value, standard error, and units for a range of metrics by land cover class in the Sacramento-San Joaquin Delta. Metrics are grouped into three major categories: Agricultural Livelihoods (including metrics for gross production value, number of agricultural jobs, and annual wages per employee), Water Quality (in terms of the application rates for pesticides identified as critical pesticides, groundwater contaminants, and those posing a high or moderate risk to aquatic organisms), and Climate Change Resilience (qualitative scores representing relative tolerance for heat, drought, and flood).</p> <p><strong>DESCRIPTION</strong><br> These data were developed to facilitate projecting the net impacts of land cover change scenarios on multiple metrics of interest to the Sacramento-San Joaquin Delta, including potential benefits and trade-offs. They were used in initial analyses of scenarios representing habitat restoration and perennial crop expansion, and they are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (In review) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta.</em> R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits </li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7504874.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7504874)</p> <p><strong>PROGRESS</strong><br> Complete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision.</p> <p><strong>UPDATE FREQUENCY</strong><br> As Needed</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from the Quarterly Census of Employment and Wages 2014-2020 (EDD 2022), annual County Agricultural Commissioners Reports 2014-2020 (CDFA 2022), Pesticide Use Report Data 2014-2018 (CDPR 2022), and qualitative assessments of climate change resilience (Peterson et al. 2020, DSC 2021).</p> <p><strong>Literature Cited:</strong></p> <ul> <li>CDFA. 2022. County Ag Commissioners’ Data Listing. California Department of Food & Agriculture. Available from: https://www.nass.usda.gov/Statistics_by_State/California/Publications/AgComm/index.php</li> <li>CDPR. 2022. Pesticide Use Report Data. California Department of Pesticide Regulation. Available from: https://www.cdpr.ca.gov/docs/pur/purmain.htm</li> <li>DSC. 2021. Delta Adapts: Creating a Climate Resilient Future. Public Review Draft. Delta Stewardship Council. Available from https://deltacouncil.ca.gov/delta-plan/climate-change</li> <li>EDD. 2022. Quarterly Census of Employment and Wages (QCEW). California Employment Development Department. Available from: https://data.edd.ca.gov/Industry-Information-/Quarterly-Census-of-Employment-and-Wages-QCEW-/fisq-v939</li> <li>Peterson C, Marvinney E, Dybala K. 2020. Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA. Migratory Bird Conservation Partnership, Sacramento, CA. Dryad Dataset doi:10.25338/B8061X</li> </ul> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>METRIC_CATEGORY: </strong>Broad grouping assigned to each METRIC; one of Agricultural Livelihoods, Water Quality, or Climate Change Resilience</li> <li><strong>METRIC: </strong>Specific metric being estimated; one of Agricultural Jobs, Annual Wages, Gross Production Value, Drought, Flood, Heat, Critical Pesticides, Groundwater Contaminant, or Risk to Aquatic Organisms</li> <li><strong>UNIT: </strong>The units in which the <strong>METRIC </strong>is estimated</li> <li><strong>CODE_NAME:</strong> The land cover class or subclass for which the <strong>METRIC </strong>is estimated</li> <li><strong>LABEL: </strong>A more user-friendly version of <strong>CODE_NAME</strong>, useful for creating figures and tables</li> <li><strong>SCORE_MEAN:</strong> The mean value of each METRIC estimated for each land cover class or subclass</li> <li><strong>SCORE_SE: </strong>The standard error of the mean</li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong></p> <ul> <li><strong>FTE: </strong>full-time equivalents; refers to converting monthly agricultural jobs data to annual estimates by dividing by 12</li> <li><strong>ha:</strong> hectares</li> <li><strong>kg: </strong>kilograms</li> <li><strong>USD: </strong>U.S. dollars</li> <li><strong>yr: </strong>year</li> </ul> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> agriculture, livelihoods, economy, water quality, pesticides, climate change, resilience, multiple-benefit conservation</li> <li><strong>Place:</strong> Sacramento-San Joaquin River Delta, Central Valley, California<br> </li> </ul>
Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> Spatial data representing climate, proximity to streams, and probability of flooding in the Sacramento-San Joaquin Delta.</p> <p><strong>DESCRIPTION</strong><br> These data were compiled as predictors of the distribution of riparian landbird species and groups of waterbird species, to facilitate projecting the probability of species or group presence across a given landscape. They were used to identify Priority Bird Conservation Areas and in analyses of the impacts of scenarios representing habitat restoration and perennial crop expansion on suitable habitat. These data are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (<em>In review</em>) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta</li> <li>Dybala KE, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T (<em>In review</em>) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta</em>. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits.</li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7672193.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7672193)</p> <p><strong>PROGRESS</strong><br> Complete</p> <p><strong>UPDATE FREQUENCY</strong><br> None planned</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from WorldClim (representing 1970-2000), National Hydrography Dataset (published 2020), and Point Blue's Water Tracker (representing 2013-2019).</p> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>bio_1: </strong>annual mean temperature (C), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>bio_12:</strong> total annual precipitation (mm), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>streamdist: </strong>square root of the distance to the nearest stream (m) (National Hydrography Dataset; USGS 2020)</li> <li><strong>pwater_fall:</strong> mean probability of open surface water during the fall, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> <li><strong>pwater_win:</strong> mean probability of open surface water during the winter, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> </ul> <p><strong>Literature Cited</strong></p> <ul> <li>Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 37:4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a> </li> <li>Reiter ME, Elliott NK, Barbaree B, Moody D. 2018. An automated open surface water tracking system for California’s Central Valley. Report to the U.S. Fish and Wildlife Service. Petaluma, California: Point Blue Conservation Science. Available from: <a href="https://data.pointblue.org/apps/autowater/ ">https://data.pointblue.org/apps/autowater/ </a></li> <li>[USGS] United States Geological Survey. 2020. National Hydrography Dataset Best Resolution (NHD) for Hydrologic Units (HU) 4 - 1802, 1803, 1804, 1805. Reston (VA): U.S. Geological Survey. Available from: <a href="https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products ">https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products </a></li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong><br> N/A</p> <p><strong>COORDINATE REFERENCE SYSTEM</strong><br> WGS 84 / UTM zone 10N (EPSG:32610)</p> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> climate, temperature, precipitation, hydrology, streams, water, flood, remote sensing </li> <li><strong>Place: </strong>Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>
Quality evaluation criteria, best practices, and assessment systems for Institutional Publishing Service Providers (IPSPs): dataset
<p>The dataset contains tabular information on the elements of best practice in scholarly publishing found in a set of documents (high-level recommendations and principles, indexation criteria and specific assessment guidelines used on the national and institutional levels). The set of documents subject to analysis (58 items) were identified by the DIAMAS project team members (bibliographic metadata are provided in IPSP-best-practice-documents.xml and IPSP-best-practice-documents.ris).</p> <p>The dataset was compiled by the DIAMAS project team using an analysis matrix that included the general information about the documents (title, issuing entity, scope and purpose, etc.) and the the seven core components of scholarly publishing identified in the Diamond Open Access Action Plan (2022) and revised by the DIAMAS project team.</p> <p>More information about the data collection methodology can be found in the report D3.1 IPSP Best Practices Quality evaluation criteria, best practices, and assessment systems for Institutional Publishing Service Providers (IPSPs) (<a href="https://doi.org/10.5281/zenodo.7859172">https://doi.org/10.5281/zenodo.7859172</a>), which is based on this dataset.</p> <p> </p> <p><strong>****Dataset contents****</strong></p> <p>IPSPs_best-practices-overview.csv</p> <p>IPSPs_best-practices-overview.ods</p> <p>IPSP-best-practice-documents.xml</p> <p>IPSP-best-practice-documents.ris</p> <p>README.txt</p> <p> </p> <p><strong>****Column headers and field types***</strong></p> <p>Title (original) (text)</p> <p>Title (English) (text)</p> <p>Publication date (date, DD/MM/YY)</p> <p>Last accessed (date, DD/MM/YY)</p> <p>URL (text-web address)</p> <p>Scope (text, controlled)</p> <p>Type of document (text, controlled)</p> <p>Original language (text)</p> <p>Other languages (text)</p> <p>Entity issuing the document (text)</p> <p>Entity responsible for the assessment (text)</p> <p>Scope of the assessment (text, controlled)</p> <p>Scope of assessment: region or country (text)</p> <p>Disciplines’ coverage (text)</p> <p>Periodicity of the assessment (text)</p> <p>Reassessment frequency? If yes: periodicity (text)</p> <p>Benefits linked to the assessment (text)</p> <p>(1) Funding (text)</p> <p>(2) Ownership and governance (text)</p> <p>(3) Open science practices (text)</p> <p>(4) Editorial quality, editorial management and research integrity (text)</p> <p>(5) Technical service efficiency (text)</p> <p>(6) Visibility (including indexation), communication, marketing and impact (text)</p> <p>(7) Diversity, Equity and Inclusion (text)</p>
A2.2a Digital repositories data citation practices. Supplementary material
<p>Data to complement the quantitative analysis of data citation practices in digital repositories based on metadata records from the re3data.org repositories registry.</p> <p>Data was retrieved using re3data.org API on 23-02-2023 and 06-03-2023 and processed using the OpenRefine software.</p> <p>Part of "A FAIR-enabling citation model for Cultural Heritage Objects" project activities.</p>
Information Filtering in Electronic Networks of Practice: An fMRI Investigation of Expectation [Dis]confirmation
Open the record for dataset details and reuse information.
FAIRsFAIR Policy and Practice Survey 2019 data for D3.1_D3.2_D6.1
<p>As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The FAIRsFAIR project runs from March 2019-February 2022. Landscaping activities have been a core activity during the initial stages of the FAIRsFAIR project and there has been close cooperation with colleagues in Work Package 3 carrying out the FAIR data policy and practice analyses; Work Package 2 on assessing FAIR requirements for interoperability and persistence; Work Package 6 on providing an overview of research communities’ needs for competence centres; and Work Package 7 on mapping RDM policies and support as well as FAIR education offerings in European HEIs. In particular, efforts were made to avoid duplication of effort across the three open consultation and survey instruments developed to assess the current landscape and to define a consistent approach to presenting our findings.</p> <p>As part of the landscape analysis, FAIRsFAIR ran an open consultation on Policy and Practice which was made available via EU Survey from August 2 - September 27, 2019. The consultation was also made accessible from the FAIRsFAIR website. A total of 106 responses were submitted by research support staff from across Europe and beyond. The open consultation included both open-ended and closed questions which sought to identify the different levels of maturity with regards to FAIR practices among disciplines, the range of policies that influence the way that researchers work, and the sources of support currently available to researchers. In developing the questions, FAIRsFAIR worked collaboratively with several related initiatives including the EOSC 5B projects, the Group of European Data Experts in RDA (GEDE), and the EOSC FAIR Working Group and Landscape Working Group to avoid duplication of effort in our information collection. FAIRsFAIR will continue to cooperate with these and other initiatives over the life of the project. The open consultation targeted members of the research support community to gain insights on their views and experiences in relation to implementing the FAIR principles.</p> <p>The open consultation questions were grouped under five broad themes:</p> <p>1) Practice</p> <p>2) Policy</p> <p>3) Repositories</p> <p>4) Skills</p> <p>5) Competence centres</p> <p>The data resulting from the open consultation has been used to inform three key FAIRsFAIR deliverables:</p> <ul> <li>D3.1 FAIR Data Policy Landscape Analysis</li> <li>D3.2 FAIR Data Practice Analysis</li> <li>D6.1 Overview of Needs for Competence Centre</li> </ul> <p> </p> <p> </p>
Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions
<p>This dataset corresponds to yield, price and fuel consumption from organic rainfed almond orchards in SE Spain under different diversification and tillage practices. The objective is to carry out an integrated environmental (focused on the CO<sub>2</sub> emissions) and economic assessment of farm operations under different diversification and tillage practices through a cradle-to-farm gate life cycle assessment (LCA) based on these data.</p> <p>These data correspond to the open-access article " Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions" published in Scientia Horticulturae. (https://doi.org/10.1016/j.scienta.2019.108978), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003].</p>
Practices and policies of preprint platforms for life and biomedical sciences
<p>Given the increase in the use and profile of preprint servers – and alternative publishing hybrid platforms such as F1000 Research – in the life sciences, it is increasingly important to identify how many such servers and hybrids exist, to describe their scope in terms of the scientific disciplines they cover, and to compare and contrast their characteristics and policies.</p> <p>We surveyed forty-four (44) platforms that host preprints relevant to life and biomedical sciences and that were active online and accepting submissions on 25 June 2019. Information on preprint platform policies, features and practices was collected through online research by the authors and by surveying preprint platform representatives directly. </p> <p>Full data sheets include an additional 5 platforms hosted on OSF Preprints (rows 49-53) to fulfil the wider scope for the ASAPbio project, not in disciplinary scope (biology and medical sciences) for the manuscript with Jamie Kirkham.</p> <p><strong>Tables 1-5: </strong>Data (44 platforms, manuscript) are separated into five main tables of information and a list of preprint platform websites for reference.</p> <p>Table 1: Scope and ownership of each server<br> Table 2: Content-specific characteristics and information relating to submission, journal transfer options, and external discoverability<br> Table 3: Screening, moderation, and permanence of content<br> Table 4: Usage metrics and other features<br> Table 5: Metadata<br> Preprint platform websites</p> <p>Data for each platform are listed as ‘Verified’ in the tables if these tables (V1.0 or V2.0) were seen and approved by a platform representative between January 13 and January 27, 2020.</p> <p><strong>Original online survey:</strong> a blank copy of the original survey form used by online researchers (the authors) and supplied pre-filled (or empty, in some cases) to preprint platform representatives for verification (or completion, in some cases). </p> <p><strong>Final data:</strong> survey data is presented in .txt and .xlsx, as follows:</p> <ul> <li>Row 1: Heading (where field is included in manuscript tables, the heading presented here replaces any heading used in original survey. All columns are presented in the order the information was requested on the original survey form, with some supplementary columns added and columns removed (detailed below).</li> <li>Row 2: Schema or description of field</li> <li>Row 3: Whether and where included in manuscript tables. For supporting information for table data (e.g. source information, URLs), the table location for supported data is indicated in brackets, e.g. (Table 2) and supporting information is not included in tables. Data included in manuscript tables is presented in its final form, which in some cases is simplified from the original survey data. This simplified version of the data was presented to platform representatives for additional verification (v1.0/v2.0 verification). Data not included in manuscript tables is presented here as verified by platform representatives and/or found online. Some columns from the original survey have been removed due to the information not being informative or useful: specifically, Print ISSN (not reported for any platform); End date (no platforms have an end date; although two platforms stopped accepting submissions after survey completed; Personal contact information for platform representative(s) has been removed).</li> <li>Rows 4 onwards: data for each preprint platform (44 included in manuscript (rows 4-47), plus 5 additional OSF platforms (rows 48-52)</li> <li>Columns 3-6 (D-G) report online research and verification information and Column 13 (M) reports an additional data field (number of articles) – these are supplementary to the original survey columns</li> <li>Verification status: Released V1/V2 data applies to data included in manuscript tables (as indicated in row 3); Online survey data applies to data used for manuscript tables and also to original survey data included here but not included in manuscript tables (‘Not included’ in row 3)</li> <li>Note that data fields are presented as individual columns in these sheets, while some entries in Tables 1-5 combine several data fields.</li> </ul> <p>These data were collected in collaboration and as part of:<br> i. An ASAPbio project, led by Dr Naomi Penfold, to develop an online directory of preprint platforms<br> ii. A research project led by Prof Jamie Kirkham<br> These data are supplementary outputs for both projects.</p> <p>Data v1.0 were presented during the ASAPbio January 2020 workshop – see Penfold, Naomi C, & Polka, Jessica. (2020, January). ASAPbio Preprint Platform Directory: 2019 data (presentation) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.3626770.<br> <br> <strong>Version 3.0 updates (December 14, 2020): added new files with updated information about servers from the ASAPbio preprint directory (https://asapbio.org/preprint-servers), provided by Jessica Polka (now included as author).</strong></p>
Dataset: Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions
<p># Dataset for Paper "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions" - Rev 1#</p> <p>This is the dataset for the paper titled "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions". All mapping study data has the prefix *MAP*, while all survey data the prefix *SUR*. It has been updated for a major revision at Springer Empirical Software Engineering (Rev 1).</p> <p>In case of questions, feel free to contact the author, Grischa Liebel, ORCID: https://orcid.org/0000-0002-3884-815X, current affiliation and email: Reykjavik University, Iceland, grischal@ru.is</p> <p>## Systematic Mapping Study ##<br> The mapping study data is mainly contained in the *MAPmappingStudy.xlsx* file. Different tabs are used for the two phases: exclusion by title and abstract (tab *title_abs*), and exclusion by fulltext (tab *fulltextScreening*). The final set of papers is obtained by filtering the *fulltextScreening* tab by included papers (Column V).</p> <p>The tab *fulltextScreening* contains a number of columns named \*range (e.g., *noStudentsRange*). These columns contain the unified/categorised values for the verbatim values listed in the column with the same name without *range*. For instance, *noStudentsRange* contains the range of students in the primary study, while *noStudents* contains the actual number obtained from the studies.</p> <p>The file *MAPvenues.txt* contains the included venues in the mapping study.</p> <p>Finally, the raw search results are provided as BIB/RIS files with the prefix *MAPRAW*.</p> <p>## Survey ##<br> The survey folder contains the survey pages (named *surveyPageN.pdf*), as well as the raw data in *surveyDataAnon.xlsx*. Note that free-text answers have been aggregated, anonymised, and sorted alphabetically in individual tabs. Similarly, countries that only occur once have been changed to Do Not Disclose answers, and all answers have been sorted randomly. All questions are listed by their acronym. The corresponding questions, as well as possible answers, are described in the *QuestionKey* tab.</p>
Database - A Calculus of Tracking: Theory and Practice
<p>A manually curated sample (Top 100 Alexa domains only) of a OpenWPM database obtained from Princeton Web Census (https://webtransparency.cs.princeton.edu/webcensus/). The sample is used to instantiate the model for the paper "<a href="https://petsymposium.org/2021/files/papers/issue2/popets-2021-0027.pdf">A Calculus of Tracking: Theory and Practice</a>" to appear in PETS 2021.</p> <p>Accepted manuscript: https://petsymposium.org/2021/files/papers/issue2/popets-2021-0027.pdf</p> <p>GitHub page: https://github.com/giorgioditizio/calculus_of_tracking</p> <p> </p>
Twitter Poll: Should #OpenScience practices be part of tenure & #REF2020 criteria
<p>The Twitter Poll "Should #OpenScience practices be part of tenure & #REF2020 criteria?" was run online in support of Dutch EU Presidency Open Science Meeting, 4-5 April 2016.</p> <p>The poll run from 1-8 April 2016, attracting 122 voters, 11,336 impressions and 469 engagements (4.1% conversion rate).</p> <p>The 122 Twitter voters are in alignement with one of the key Call for Action recommendations on "New assessment, reward and evaluation systems New systems that really deal with the core of knowledge creation and account for the impact of scientific research on science and society at large, including the economy, and incentivise citizen science".</p> <p><em><strong>Event website</strong></em>: http://english.eu2016.nl/documents/reports/2016/04/04/amsterdam-call-for-action-on-open-science</p> <p><em><strong>CODE to EMBED TWITTER POLL:</strong></em></p> <p><blockquote class="twitter-tweet" data-cards="hidden" data-lang="en"><p lang="en" dir="ltr">Should <a href="https://twitter.com/hashtag/OpenScience?src=hash">#OpenScience</a> practices be part of tenure &amp; <a href="https://twitter.com/hashtag/REF2020?src=hash">#REF2020</a> criteria? <a href="https://twitter.com/EU2016NL">@EU2016NL</a> <a href="https://twitter.com/openscience">@openscience</a> <a href="https://twitter.com/hashtag/phdchat?src=hash">#phdchat</a> <a href="https://twitter.com/PhDForum">@PhDForum</a></p>&mdash; Foster Open Science (@fosterscience) <a href="https://twitter.com/fosterscience/status/715777247899205636">April 1, 2016</a></blockquote><br /> <script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script></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.