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3,673 results for “, Practices”
Mapping Ottoman Damascus Through News Reports: A Practical Approach,
<p>This release corresponds to the publication of the book chapter "Mapping Ottoman Damascus Through News Reports: A Practical Approach," in <em>Digital Humanities and Islamic & Middle East Studies</em>, ed. Elias Muhanna (Boston, Berlin: De Gruyter, 2015), pp. 175-198.</p>
OpenUP survey on researchers' current perceptions and practices in peer review, impact measurement and dissemination of research results
<p>OpenUP project (http://openup-h2020.eu/) conducted a survey to capture current perceptions and practices in peer review, dissemination of research results and impact measurement among European researchers. The survey was coducted between 20 January and 23 February 2017. It consisted of four sections. The first section asked a series of questions on the respondents’ scientific discipline, career stage, gender and other characteristics. The following sections asked a series of questions on peer review practices, dissemination of research results and impact measurement/use of altmetrics. The questionnaire was collaboratively prepared by the OpenUP consortium. </p> <p>The survey was implemented via surveygizmo tool (https://www.surveygizmo.com/). Invitations to participate were sent to a random sample of researchers from arXiv, Pubmed and RePEc. The OpenUP team mined researchers’ contact details from these platforms. The OpenUP project team made efforts to further boost the repondent sample for certain underrepresented areas through the DARIAH website, THESIS network, EURODOC, AIMS portal, the Parthenos community and other channels. The survey targeted researchers from the EU-28, Switzerland and Norway. The goal was to get around 1,000 responses. In total, there were 976 completed response and completion rate was 72.4%. </p> <p>The attached documents include the questionnaire and the dataset. In the dataset (cvs file) the top row contains numbered questions that correspond to the numberring in the questionnaire (word file). The data was exported as an excel file, anonymised by creating respondent IDs and IP data were deleted. The file was then converted to CSV.</p> <p> </p>
Sketches and Diagrams in Practice — Supplementary Material
<p>Sketches and diagrams play an important role in the daily work of software developers. In our paper "Sketches and Diagrams in Practice" we present the results of our <strong>research on the usage of sketches and diagrams in software engineering practice</strong>. We focused especially on their relation to the core elements of a software project, the source code artifacts. Furthermore, we wanted to assess how helpful sketches are for understanding the related source code. We intended to find out if, how, and why sketches and diagrams are archived and are thereby available for future use. Software is created with and for a wide range of stakeholders. Since sketches are often a means for communicating between these stakeholders, we were not only interested in sketches and diagrams created by software developers, but by all software practitioners, including testers, architects, project managers, as well as researchers and consultants. In a <strong>survey with 394 software ‘practitioners’</strong>, we mainly asked questions about the the last sketch or diagram that they had created. Contrary to our expectations and previous work, the majority of sketches and diagrams contained at least some UML elements. However, most of them were informal. The most common purposes for creating sketches and diagrams were designing, explaining, and understanding, but analyzing requirements was also named often. More than half of the sketches and diagrams were created on analog media like paper or whiteboards and have been revised after creation. Most of them were used for more than a week and were archived. About half of the sketches were rated as helpful to understand the related source code artifact(s) in the future. Our study complements a number of existing studies on the use of sketches and diagrams in software development, which analyzed the above aspects only in parts and often focused on an academic environment, a single company, open source projects, or were limited to a small group of participants.</p> <p>The questionnaire for the online survey was online from August 28, 2013 until December 31, 2013. Further information on our research design and research questions can be found in the referenced paper. This dataset contains the questionnaire, the data we collected during this survey, and a basic R script that can be used as a starting point for validating our results and further exploring the data. This data set has been reviewed and accepted by the Artifact Evaluation Committee of FSE 2014. Since we assured our participants that their data is handled confidentially, only the quantitative data is directly available here. If you are also interested in the qualitative data from our survey, don’t hesitate to contact the authors.</p>
The ecological and economic benefits of sustainable agricultural practices
<p><strong>Code and data for Mata et al.'s <em>The ecological and economic benefits of sustainable agricultural practices: Evidence from on-farm trials in broad-acre crops. </em></strong></p> <p><strong>Abstract</strong></p> <p>1. A transition to more sustainable agricultural practices is essential to mitigate the negative environmental impacts of conventional farming and to ensure long-term food security. However, widespread adoption requires robust evidence demonstrating their efficacy and economic viability.</p> <p>2. We co-designed a two-year field trial with farmers and agronomy advisors in Australia to evaluate the ecological and economic outcomes of sustainable agricultural practices for managing the redlegged earth mite, a major pest of Australian crops and pastures. We compared 'Novel' treatments – representing long-term farmer-implemented sustainable practices based on biological control – with 'Conventional' treatments, and 'Plus' treatments designed as counterfactuals to disentangle the effects of specific pest control and plant nutrient components.</p> <p>3. Redlegged earth mite densities remained below economic thresholds across all treatments and years, demonstrating effective pest control in both conventional and sustainable systems. Notably, the Novel treatment supported higher densities of beneficial arthropods, suggesting increased biological control potential.</p> <p>4. Yield and gross profit margins were generally similar across treatments, indicating that sustainable agriculture practices can maintain profitability while fostering biodiversity.</p> <p>Practical implication. Our study provides evidence that biological control and biofertiliser supplementation can be effectively used to manage agricultural pests. It also demonstrates the value of close collaboration with farmers and agronomy advisors in conducting ecological field research with real-world applications.</p>
Data for "How do ecologists estimate occupancy in practice?" by Goldstein et al.
<p> Data for review of occupancy estimtaion methods by Goldstein et al.</p> <p> </p> <p>Please see the accompanying manuscript for full methodology. We will link to it when the manuscript is published.</p> <p> </p> <p>This upload contains three datasets: "binary_scores_phase1.csv", "binary_scores_phase2.csv" and "modsel_results.csv". Each is a .csv file containing data from a survey of occupancy estimation practices. Each row represents one peer-reviewed paper, while each column represents a characteristic of the paper. Most columns are TRUE/FALSE, indicating whether or not the paper satisfied the relevant criterion.</p> <p> </p> <p>One additional raw resource is provided. The .zip file "all_papers_2022-04-07.zip" contains 6 .xls files giving the full set of papers returned by the original Web of Science search. These are unmodified from the initial search.</p> <p> </p> <p>All datasets contain the column:</p> <p>ID - A unique ID for each paper; it most cases, a DOI. When Web of Science returned an invalid DOI, the ID is set to the paper title instead.</p> <p> </p> <p>Note that all papers in Phase 2 are also in Phase 1, and all model selection papers are in both Phase 1 and Phase 2. The ID column can be used to join datasets.</p> <p> </p> <p>Across both datasets, if all options in a category are FALSE or if a field is NA, that may mean that the review team was unable to determine what choices the authors made.</p> <p> </p> <p>binary_scores_phase1.csv gives the results of Phase 2 of the review. It contains the following columns:</p> <p> </p> <p>coll_newdata - Did the authors analyze newly collected data?</p> <p>coll_existing - Did the authors analyze existing, previously published data?</p> <p>coll_longterm - Did the authors analyze data produced by a long-term monitoring program?</p> <p>coll_particip - Did the authors analyze participatory science data?</p> <p>eco_frshwtr - Was the study system a freshwater ecosystem?</p> <p>eco_marine - Was the study system a marine ecosystem?</p> <p>eco_terra - Was the study system a terrestrial ecosystem?</p> <p>region_USA - Were the data collected in the USA?</p> <p>region_NoAm - Were the data collected in North America?</p> <p>region_CenAm - Were the data collected in Central America?</p> <p>region_SoAm - Were the data collected in South America?</p> <p>region_Africa - Were the data collected in Africa?</p> <p>region_Eur - Were the data collected in Europe?</p> <p>region_Asia - Were the data collected in Asia?</p> <p>region_Oceania - Were the data collected in Oceania?</p> <p>framework_MLE - Did the authors estimate models in a maximum likelihood framework?</p> <p>framework_ML - Did the authors estimate models in a machine learning framework?</p> <p>framework_Bayes - Did the authors estimate models in a Bayesian framework?</p> <p>gof_AUC - Did the authors use area-under-the-curve to evaluate their models?</p> <p>gof_CV - Did the authors use cross validation to evaluate their models?</p> <p>gof_PPC - Did the authors use poserior predictive checks to evaluate their models?</p> <p>gof_parboot - Did the authors use parametric bootstrapping to evaluate their models?</p> <p>gof_bayespv - Did the authors use Bayesian p-values to evaluate their models?</p> <p>gof_any - Did the authors conduct any model checking?</p> <p>taxon_mammal - Were some or all of the study species mammals?</p> <p>taxon_bird - Were some or all of the study species birds?</p> <p>taxon_herp - Were some or all of the study species herptiles (reptiles and amphibians)?</p> <p>taxon_fish - Were some or all of the study species fish?</p> <p>taxon_arthro - Were some or all of the study species arthropods?</p> <p>taxon_othinv - Were some or all of the study species non-arthropod invertebrates?</p> <p>soft_unmarked - Did the authors estimate models using the software unmarked?</p> <p>soft_PRESENCE - Did the authors estimate models using PRESENCE-family software?</p> <p>soft_MARK - Did the authors estimate models using MARK-family software?</p> <p>soft_JAGS - Did the authors estimate models using JAGS-family software?</p> <p>soft_lme4 - Did the authors estimate models using the software lme4?</p> <p>soft_MaxEnt - Did the authors estimate models using the software MaxEnt?</p> <p>soft_baseR - Did the authors estimate models using custom models written in base-R?</p> <p>soft_NR - Did the authors fail to clearly report what software they used to estimate models?</p> <p>soft_other - Did the authors estimate models using some other software?</p> <p>nspecies - How many species did the authors study?</p> <p>nspec_1 - Did the authors analyze data on exactly 1 species?</p> <p>nspec_2 - Did the authors analyze data on exactly 2 species?</p> <p>nspec_3_5 - Did the authors analyze data on 3-5 species?</p> <p>nspec_6_10 - Did the authors analyze data on 6-10 species?</p> <p>nspec_11_20 - Did the authors analyze data on 11-20 species?</p> <p>nspec_20plus - Did the authors analyze data on more than 20 species?</p> <p>compare_avg - Did the authors conduct model averaging?</p> <p>compare_sel - Did the authors conduct model selection?</p> <p>compare_other - Did the authors compare multiple models without averaging or selecting between them?</p> <p>modsel_AICc - Did the authors use AICc in model selection or averaging?</p> <p>modsel_AIC - Did the authors use AIC in model selection or averaging?</p> <p>modsel_other - Did the authors use another information criterion in model selection or averaging?</p> <p>dattype_DND - Were some or all of the data collected as detection-nondetection data?</p> <p>dattype_count - Were some or all of the data collected as count data?</p> <p>dattype_PO - Were some or all of the data collected as presence-only data?</p> <p>dattype_other - Were some or all of the data collected in some other form?</p> <p>survey_visual - Were some or all of the data collected using in-person visual surveys?</p> <p>survey_audio - Were some or all of the data collected using in-person audio surveys?</p> <p>survey_camtrap - Were some or all of the data collected using camera trap surveys?</p> <p>survey_capture - Were some or all of the data collected using animal capture surveys?</p> <p>survey_sign - Were some or all of the data collected using sign surveys?</p> <p>survey_passaudio - Were some or all of the data collected using passive acoustic surveys?</p> <p>survey_DNA - Were some or all of the data collected using eDNA surveys?</p> <p>survey_other - Were some or all of the data collected using some other survey protocol?</p> <p>modtype_SSOM - Did the authors analyze data with an SSOM?</p> <p>modtype_DynOcc - Did the authors analyze data with a dynamic occupancy model?</p> <p>modtype_GLM - Did the authors analyze data with a GLM?</p> <p>modtype_cooccur - Did the authors analyze data with a multispecies co-occurrence model?</p> <p>modtype_community - Did the authors analyze data with a multispecies community model?</p> <p>modtype_MaxEnt - Did the authors analyze data with a MaxEnt model?</p> <p>modtype_other - Did the authors analyze data with another model?</p> <p>affil_acad - Did any of the authors have an academic affiliation?</p> <p>affil_govt - Did any of the authors have a government affiliation?</p> <p>affil_other - Did any of the authors have a private or NGO affiliation?</p> <p> </p> <p> </p> <p>binary_scores_phase2.csv gives the results of Phase 2 of the review. It contains the following columns:</p> <p> </p> <p>code_avail - Did we determine that the authors published their model fitting code?</p> <p>data_avail - Did we determine that the authors published their data?</p> <p>nsite - At how many sites were data collected?</p> <p>nsite_lt20 - Were data collected at 20 or fewer sites?</p> <p>nsite_21_50 - Were data collected at 20-50 sites?</p> <p>nsite_51_100 - Were data collected at 51-100 sites?</p> <p>nsite_101p - Were data collected at more than 100 sites?</p> <p>time_pds - Over how many primary time periods (e.g. sampling seasons) were data collected?</p> <p>time_pds_1 - Were data collected during a single time period?</p> <p>time_pds_2_3 - Were data collected during 2-3 time periods?</p> <p>time_pds_4p - Were data collected during 4 or more time periods?</p> <p>nrepl - Roughly how many replicate surveys were collected per site?</p> <p>nrepl_1 - Was only one replicate survey conducted per site?</p> <p>nrepl_2_3 - Were 2-3 replicate surveys conducted per site?</p> <p>nrepl_4_5 - Were 4-5 replicate surveys conducted per site?</p> <p>nrepl_6p - Were 6 or more replicate surveys conducted per site?</p> <p>window - What sampling window was used to discretize continuous-time sampling? (continuous-time studies only; otherwise NA)</p> <p>det_window_lt_day - Was a sampling unit of less than one day used to discretize sampling?</p> <p>det_window_1day - Was a sampling unit of one day used to discretize sampling?</p> <p>det_window_2_6day - Was a sampling unit of 2-6 days used to discretize sampling?</p> <p>det_window_7_14day - Was a sampling unit of l7-14 days used to discretize sampling?</p> <p>det_window_15p_day - Was a sampling unit of 15 days or more used to discretize sampling?</p> <p>homerange_is_bigger - Did the authors describe their survey area per site as bigger than the target species' home range?</p> <p>homerange_is_smaller -Did the authors describe their survey area per site as smaller than the target species' home range?</p> <p>informative_priors - Did the authors use informative priors in a Bayesian analysis?</p> <p>time_in_mod_as_covar - Did the authors include primary time periods in the model as a covariate?</p> <p>time_in_mod_separate_model - Did the authors use separate models to analyze data collected during different primary time periods?</p> <p>time_in_mod_other - Did the authors include primary time periods in the model in some other way?</p> <p>ncovar_det - How many covariates were included in the best model's detection submodel?</p> <p>ncovar_det_zero - Did the detection submodel include 0 covariates?</p> <p>ncovar_det_1_3 - Did the detection submodel include 1-3 covariates?</p> <p>ncovar_det_4p - Did the detection submodel include 4 or more covariates?</p> <p>ncovar_occ - How many covariates were included in the best model's occupancy submodel?</p> <p>ncovar_occ_zero - Did the occupancy submodel include 0 covariates?</p> <p>ncovar_occ_1_3 - Did the occupancy submodel include 1-3 covariates?</p> <p>ncovar_occ_4p - Did the occupancy submodel include 4 or more covariates?</p> <p>covars_both_mods - Were any variables considered in both submodels?</p> <p>model_ranefs - Did the authors include any random effects?</p> <p>model_expl_spatial - Did the model have an explicit spatial component?</p> <p>motivating_q_range - Was range estimation a main goal motivating the study?</p> <p>motivating_q_drivers - Was identifying drivers of occupancy a main goal motivating the study?</p> <p>motivating_q_trends - Was identifying trends in occupancy a main goal motivating the study?</p> <p>motivating_q_predict - Was predicting occupancy under new conditions a main goal motivating the study?</p> <p>motivating_q_theoretical - Was advancing ecological theory a main goal motivating the study?</p> <p>motivating_q_field_method - Was evaluation of a field method a main goal motivating the study?</p> <p>motivating_q_model_method - Was evaluation of a modeling method a main goal motivating the study?</p> <p>context_conservation - Did the authors contextualize their study as relevant to conservation?</p> <p>context_management - Did the authors contextualize their study as relevant to wildlife management?</p> <p>context_natural_hist - Did the authors contextualize their study as relevant to studying natural history of target species?</p> <p>context_methodology - Did the authors contextualize their study as advancing methodology?</p> <p>detdensity - Did the authors mention the possibility that detection and animal density were confounded?</p> <p>interp_top_mod_as_biol - Did the authors interpret model selection results as evidence for a biological process?</p> <p>nmod_reported_best - Did the authors report only the best model from a model selection workflow?</p> <p>nmod_reported_some - Did the authors report multiple model results from a model selection workflow?</p> <p>nmod_reported_all - Did the authors report all model results from a model selection workflow?</p> <p>priors_reported - Did the authors report their priors in a Bayesian workflow?</p> <p>violation_nonindependence - Do the authors acknowledge violating the assumption of independent data?</p> <p>violation_movement - Do the authors acknowledge violating the assumption of no animal movement?</p> <p>violation_demography - Do the authors acknowledge violating the assumption of no demographic change?</p> <p>violation_det_heterogeneity - Do the authors acknowledge violating the assumption of no unmodeled heterogeneity in detection?</p> <p>violation_yes_other - Do the authors acknowledge violating another assumption?</p> <p>violation_explicit_no - Do the authors state that all assumptions were met?</p> <p>hypotheses_all - Do the authors provide hypotheses for the effect of all covariates?</p> <p>hypotheses_some - Do the authors provide hypotheses for the effect of some covariates?</p> <p>interpret_det_literal - Do the authors interpret detection literally?</p> <p>interpret_det_biol - Do the authors interpret detection as confounded with biology?</p> <p>interpret_vars_significant - Do the authors interpret some variables as significant based on p-values?</p> <p>interpret_vars_credible - Do the authors interpret some variables as meaningful based on Bayesian credible intervals?</p> <p> </p> <p> </p> <p>modsel_results.csv describes the model selection choices made by 64 Phase 2 papers that conducted model selection. In addition to ID, it contains two columns:</p> <p> </p> <p>Submodel approach - Did the authors use a separate-by-submodel approach to model selection, did they only conduct model selection on one submodel, or did they perform variable selection on both submodels simultaneously?</p> <p>Candidate set approach - Did the authors conduct model selection among a set of a priori candidate models, or did they select between arbitrary models based on combinations of all covariates?</p> <p> </p> <p> </p> <p> </p>
Database of best practice for pondscape NbS for CC adaptation and mitigation
<p>We built an inventory (database) of Nature-based Solutions (NbS) actions (creation, restoration, and management) in ponds and pondscapes (ponds at the landscape scale) in a diversity of social-ecological settings to assess the best practices. We formulated an online questionnaire that was shared with pond stakeholders. The questionnaire asked general (e.g., number of ponds, area of the pondscape, etc.) and specific (e.g., costs of the action, stakeholders involved, etc.) information on the NbS action implemented, and on 11 associated Nature's Contributions to People (NCPs). Among the NCPs we included, for instance, habitat creation for biodiversity, regulation of climate, learning or physical and physiological experiences. The database contains information gathered through the questionnaire, research papers and relevant web pages and platforms.</p> <p>We used three different approaches to obtain information on NbS actions implemented in ponds/pondscapes and the associated NCPs mainly focusing on Europe and Uruguay: 1) the development of a user-friendly online questionnaire on NbS implemented in ponds/pondscapes and associated NCPs, which was shared in the form of a survey through the platform Survey Monkey with PONDERFUL members and pond Stakeholders; 2) the search of information in research papers; and 3) the search of information on web pages such as <a href="https://oppla.eu/" target="_blank" rel="noopener">https://oppla.eu</a>, <a href="https://renature-project.eu/" target="_blank" rel="noopener">https://renature-project.eu</a>, <a href="https://climate-adapt.eea.europa.eu/" target="_blank" rel="noopener">https://climate-adapt.eea.europa.eu</a>, <a href="https://una.city/" target="_blank" rel="noopener">https://una.city</a>. We requested permissions from the respondents to make the data available.</p>
A Comprehensive Self-Consolidating Concrete Dataset for Advanced Construction Practices
<ul> <li><span>Size: over 2500 Self-consolidating concrete mixtures from 176 published papers.</span></li> <li><span>Material type: Self-consolidating concrete (SCC).</span></li> <li><span>Features:</span> <ul> <li><span>Identification features (5 features): References, number of the mixture, the authors, year of publication, & the mixture code.</span></li> <li><span>Powders type, content, & density (76 features): Cement, various supplementary cementitious materials, & other mineral additions.</span></li> <li><span>Paste properties (8 features): The total amount of powder used, the water content, the calculated volume of the paste, the water-to-cement ratio, the water-to-binder ratio, the water-to-powder ratio, the volume of water to the volume of powder ratio, & the volume of water to the volume of cement ratio.</span></li> <li><span>Aggregate properties (7 features): Content and density of fine and coarse aggregates, the total aggregate, the maximum size of the aggregate, & the fine-to-total-aggregate ratio.</span></li> <li><span>Admixture properties (3 features): Quantity of admixture used, its proportion relative to the cement & the total binder content.</span></li> </ul> </li> <li>Properties: <ul> <li>Fresh properties (13 features): Including filling ability properties, i.e., slump flow spread, V-funnel flow time, & the T50 time; Passing ability properties, i.e., J-Ring flow spread, L-box H1/H2 ratio, & U-box flow; Segregation resistance i.e., sieve segregation index, column segregation index, dynamic segregation index, segregation factor, & sieve GTM stability test. Additionally, the percentage of air content is also documented.</li> <li><span>Rheological properties (3 features): yield stress & plastic viscosity values alongside with the used rheometer. The instruments employed in these measurements include the ICAR Rheometer, R/S Plus Rheometer, ConTec5 Viscometer, ConTec4SCC, Concrete Shear Box, & TR-CRI Concrete Rheometer.</span></li> </ul> </li> <li><span>Application: Essential in choosing Self-Compacting Concrete (SCC) mixtures for different uses, considering the importance of both fresh & rheological properties. Intended to support the creation of sustainable & eco-friendly building materials.</span></li> </ul>
Mapping of STEAM practices - anonymised dataset
<p>This dataset is the result of the initial dissemination of a survey submitted to STEAM practitioners, aiming at gaining insight on their on-going STEAM practices, which have informed the project's mapping of STEAM practices (Deliverable 4.2). </p>
Bibliographic Dataset for the Systematic Literature Review on Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice
<p>This database contains all the bibliographic information found after applying the Search Strategy used for the Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice: Systematic Literature Review. The following electronic databases were searched:</p> <ul> <li>Scopus.</li> </ul> <p>A total of 907 records were found. The search was conducted on 16/02/2024.</p> <p>The information is presented in .ris, .bib, and .csv format.</p>
General practice characteristics associated with life expectancy of practice populations: a cross-sectional study
<p>The dataset was used to investgate features of general practice associated with life expectancy of general practice populations in England for the period 2015-2019.</p>
Survey data on financial literacy, financial inclusion, informal financial business practices, and intentions towards formalization of female small vendors in Lima, Peru
<p><span>This dataset encapsulates a comprehensive survey aimed at understanding informal business practices and financial literacy among small business vendors in Peru. The dataset comprises three key components: the survey questionnaire, raw survey data, and a detailed codebook. Researchers interested in the dynamics of financial practices in emerging markets may find this dataset particularly valuable, as it allows for the exploration of factors influencing financial decisions in small enterprises, with potential modifications suggested for adapting the survey to different national or cultural contexts. This dataset not only contributes to empirical research in financial behavior but also supports gender-specific studies by allowing the variable 'sex' to be adapted to 'gender' with multiple response options. </span></p> <p><span>The data and supplementary material is divided in tree files:</span></p> <p><span>The survey, presented in "Survey IFE.docx," includes questions across various domains such as informal business practices, financial literacy, financial inclusion, intentions towards financial formalization, and the formality of business ventures, along with demographic variables like age, sex, business age, and number of employees. </span></p> <p><span>The raw data, stored in "Dataset.csv," records responses from 118 participants, mapped against 31 indicators. </span></p> <p><span>The "Codebook.doc" provides exhaustive details about the survey variables, coding of responses, and the methodology employed, facilitating the replication of the study and application of the dataset in varied research contexts.</span></p>
Database of Participatory Practices and Social Innovations in Wind Energy Developments
<p>Inês Campos was responsible for designing the database, collecting data, and analyzing data. Flávio Oliveira also collaborated in the design of the database and data collection. </p>
Data from: Validating a practical methodology for thatch - mat - soil distinction in turfgrass soils
<p><span>We described a <a name="_Hlk163223211"></a>practical method for the distinction of thatch, mat and soil layers in turfgrass soils, being a combination of visually and manually observable characteristics. For two widely different turfgrass species, we analyzed total organic matter (TOM) and dry bulk density (ρd) in thin slices of 6 mm (between 0 and 10 cm soil depth), resulting in clear patterns for both soil properties with increasing depth. Statistical analysis of TOM patterns resulted in similar boundary depths between calculated and observed layers, validating our practical method for the distinction of thatch, mat and soil layers as a reliable method. </span><span>Furthermore, we characterized thatch, mat and soil layer by different TOM fractions. <span>TOM was fractionalized into three distinctive and functional pools of organic matter: (1) visible organic matter (VOM), consisting of mainly non-decomposed plant structures, (2) decomposed organic matter (DOM), consisting of mainly decomposed plant structures with its associated microbial biomass, and (3) soil organic matter (SOM), being the background value or recalcitrant native organic matter in a soil and its local microbial biomass.</span></span></p> <p><span><span>We distinguished thatch, mat and soil layer based on visual and manual observable characteristics of the layers and a protocol as described in Evers et al. (2024) https://doi.org/10.1002/its2.148. This study was conducted on a well-established turfgrass demonstration field with monoculture plots of turfgrass varieties (turfgrass seed company DLF; Moerstraten, the Netherlands; 51°32'27'' N 4°20'54" E) 3.5 years after sowing, reflecting the result of organic matter accumulation over these initial years of turfgrass establishment. The climate regime was marine with cool to medium summer temperatures and mild winters (Cfb/Cfa according to the Köppen-Geiger climate classification system (Peel, et al., 2007)). The field was built on a sandy soil (Hortic Anthrasol as described in the FAO/UNESCO soil map of the world (2006)). Sampling of the soil took place in June 2016 before the field was sown. We tested our methodology with two turfgrass species, slender creeping red fescue (<em>Festuca rubra trichophylla </em>(Frt), variety Beudin of DLF) <span>as an example of a spreading turfgrass</span><em> </em><span>and perennial ryegrass (<em>Lolium perenne </em></span>(Lp)<em>,</em> variety Duparc of DLF) as an example of a bunch-type grass, as these two species were expected to differ widely in thatch and mat depth. The individual plot size for each variety was approximately 1 m<sup>2</sup> (0.8 x 1.2 m). Plots of each species were sampled at the end of February 2020. A subplot of 0.25 m<sup>2</sup> (0.5 m x 0.5 m) in the center of one plot per species was selected, to avoid contamination with other varieties (at least 90% pure monoculture), and it was marked with a metal frame. From the 25 cells of 5 x 5 cm in this frame, nine evenly dispersed cells were chosen to take a set of soil samples of 10 cm depth, using a core sampler with 2.8 cm diameter, for thatch-mat and mat-deeper soil boundary observation and TOM and <em>ρ</em><sub>d</sub> analyses. A<span>nother set of nine samples was taken next to the previous cells for VOM analyses. </span>Every fresh 10 cm soil core was first photographed (Canon Powershot S5 camera, 24 megapixels; Canon Europe, Amstelveen, the Netherlands), judged on thatch-mat and mat-deeper soil boundaries following the method described in supplementary information, and then sliced into 14 subsamples of 6 mm each plus a 16 mm subsample at the bottom, starting to measure just below the green canopy with an accurate ruler (Sola HK ¼ W12, EU-accuracy class 3), for analyzing TOM and <em>ρ</em><sub>d</sub>.in every subsample. TOM and <em>ρ</em><sub>d</sub> were analyzed after samples were dried at 105°C for 24 h. VOM was analyzed after a<span>ll sediment per slice was carefully washed off with tap water in a fine sieve (approximately 600 µm (27 mesh)), after which the remaining (dead and living) plant biomass, mainly roots and rhizomes, was dried at 65 °C for at least 48 h. SOM was determined separately in the bulk soil of the study site and is 2.6% of the dry matter. DOM was calculated via subtraction of VOM and SOM from TOM</span></span></span></p> <p><span>Based on the analyzed TOM content of the 14 slices per nine replicates, the boundaries of distinctive layers were calculated. For this, we rescaled the TOM results per replicate via the normalized function <em>F </em>(χ) = (χ-χ<sub>min</sub>)/(χ<sub>max</sub>-χ<sub>min</sub>) into values between 0 and 1 to overcome scale differences between replicates but keeping distributions the same. Per turfgrass species, the best fitted line, i.e., the smallest <em>rse</em>, and its 95%-confidence interval through all 135 points was iteratively calculated by non-linear least square regression with the nls function of R (version 3.5.2; 2018-12-20). For the fitting of the statistical models, either a logistic function (<em>F </em>(χ) = α/(1+e<sup>-(βχ+γ)</sup>) or a bell-shaped Gaussian function (<em>G </em>(χ) = αe<sup>-((χ-β)^2/2γ^2)</sup>) was used, based on the best fit for the respective turfgrass species. The characteristics of these mathematical functions were used to explain the TOM-dynamics in the soil. To this end, the turning points of the curves were calculated by finding where the first derivative of both functions equals zero, i.e., solving <em>F’</em> (χ) = 0 and <em>G’ </em>(χ) = 0 respectively. The inflection points of each curve were determined by analyzing the second derivative, i.e., identifying the points where the second derivative <em>F’’</em> (χ) or <em>G’’</em> (χ) = 0, indicating changes in concavity. The inflection points and turning points indicated changes in TOM content in the turfgrass soil profile as likely boundaries between distinctive soil layers. </span><span>Differences in parameters between distinctive soil layers and turfgrass species were determined based on the calculated means of parameters per nine replicates of soil slices Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Normally distributed means were compared with one-way ANOVA for a 3-layered soil system, followed by either Tukey post hoc tests in case of equality of variance, or by the Games-Howell post hoc test in case of no equality of variance, or with a one sample t-test for a 2-layered soil system. </span></p>
State of open science practices in France _ Dataset
<p>L’enquête State of Open Science Practices in France (SOSP-FR) a été conduite entre juin 2020 et septembre 2020. Elle a pour but d’interroger les pratiques des outils numériques et autour des données de la recherche dans les communautés scientifiques françaises. Le questionnaire se compose de 38 questions réparties en 9 thématiques. Les questions portent sur des pratiques déjà établies et des pratiques ou usages émergents comme l’<em>open peer review </em>ou les articles de données dits <em>data papers</em>. Le nombre de répondants est de 1 089, permettant d’interroger une répartition disciplinaire, genrée et statutaire assez représentative de l’état de l’emploi dans l’enseignement supérieur et de recherche en France.</p> <p>Les données ont été recueillies en 2020 via le logiciel Sphinx, mis à disposition par la TGIR Huma-Num.</p> <p>Le fichier SOSP_metadonnées_variables liste les variables avec les questions et les modalités associées. Il précise le traitement des données.</p> <p>Cette recherche a été financée par le Comité pour la science ouverte: <a href="https://www.ouvrirlascience.fr/sosp_-state-of-open-science-practices-in-france/">https://www.ouvrirlascience.fr/sosp_-state-of-open-science-practices-in-france/</a></p> <p>The State of Open Science Practices in France (SOSP-FR) survey was carried out between June 2020 and September 2020. It aims to question the practices of digital tools and about research data in French scientific communities. The questionnaire consists of 38 questions divided into 9 themes. The questions concern practices already fixed and those emerging uses such as open peer review or data papers. The number of respondents was 1089, making possible a fairly representative disciplinary, gender and status analyse of the state of employment in higher education and research in France.</p> <p>The data were collected in 2020 using the Sphinx software, provided by the TGIR Huma-Num.</p> <p>The SOSP_metadonnées_variables.csv file lists the variables with the associated questions and modalities.</p> <p>This research was funded by the Open Science Committee: <a href="http://The data were collected in 2020 using the Sphinx software, provided by the TGIR Huma-Num. The readme.csv file lists the variables with the associated questions and modalities. This research was funded by the Open Science Committee: https://www.ouvrirlascience.fr/sosp_-state-of-open-science-practices-in-france/">https://www.ouvrirlascience.fr/sosp_-state-of-open-science-practices-in-france/</a></p>
Survey on Open Science Practices in Functional Neuroimaging. Dataset and Materials
<p>Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. A PubMed search with the search terms ("fMRI" OR "functional magnetic resonance imaging" OR "functional Magnetic Resonance Imaging") was done to collect email addresses from corresponding authors of scientific articles published between 2010/01/01 and 2020/08/28. An email was sent to 14,690 addresses on 2020/01/12 with an invitation to participate, including a personalized link to the survey. If the recipients did not click the link or did not complete the survey after 14 days, they received a single reminder email. The questionnaire was composed of five building blocks. The Blocks 1-3 focused on three areas of open science practices: data structure, preregistration and data sharing. The fourth block asked about technical expertise with software and the fifth part assessed sociodemographic data.</p>
Community Established Best Practice Recommendations for Tephra Studies-from Collection through Analysis
<p>Tephra is a unique volcanic product with an unparalleled role in understanding past eruptions, long-term behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, extremely high-resolution time-stratigraphic markers across a range of sedimentary settings and are used in a range of disciplines (e.g., volcanology, climate science, archaeology, ecology, and impact assessment). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that often inhibits data integration across geographic regions and across disciplines.</p> <p>Here we present comprehensive recommendations for tephra data gathering and reporting that were developed by the tephra science community to serve as guidelines for future investigators and to ensure that sufficient data are gathered for transparency and interoperability. Recommendations include standardized field and laboratory data collection along with reporting and correlation guidance. These are organized as tabulated lists of key metadata with their definition and purpose. They are system independent and usable for template, tool, and database development. This new standardized framework promotes consistent tephra documentation and archiving, fosters interdisciplinary communication, and improves effectiveness of data sharing among diverse communities of researchers. Wider adoption will help to expand the applicability and usability of tephra data and facilitate scientific collaboration and data reuse.</p> <p>For additional details, see the accompanying manuscript:</p> <p>Wallace, K.*, Bursik, M. Kuehn, S., Kurbatov, A., Abbott, P., Bonadonna, C., Cashman, K., Davies, S., Jensen, B., Lane, C., Plunkett, G., Smith, V. Tomlinson, E., Thordarsson, T., and Walker, D. Community established best practice recommendations for tephra studies—from collection through analysis. <em>Sci Data</em> <strong>9, </strong>447 (2022). <a href="https://doi.org/10.1038/s41597-022-01515-y">https://doi.org/10.1038/s41597-022-01515-y</a></p> <p>*corresponding author: Kristi Wallace, <a href="mailto:kwallace@usgs.gov">kwallace@usgs.gov</a></p> <p>Open access article is available online here <a href="https://doi.org/10.1038/s41597-022-01515-y">https://doi.org/10.1038/s41597-022-01515-y</a> or as a PDF here <a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41597-022-01515-y.pdf&data=05%7C01%7Ckwallace%40usgs.gov%7C673f9f39fd3e4122dd9b08da6f3b9667%7C0693b5ba4b184d7b9341f32f400a5494%7C0%7C0%7C637944598967375940%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=%2BKVfwK2FbUKAoJf2gMerCmBMEQE1rvMDkS6xIk3DGKY%3D&reserved=0">https://www.nature.com/articles/s41597-022-01515-y.pdf</a>.</p>
Cultivation practices in soybean production at a glance
<p>This video contains information about cultivation practices in soybean production. It was made within the scope of the Legumes Translated Horizon 2020 project. It is available in Serbian with subtitles in English, German, Hungarian, Italian, Romanian, Russian and Serbian.</p>
Good Practices and Success Cases for Short Food Supply Chains connecting consumers and producers
<p>The agroBRIDGES Good Practices (GPs) is a compilation of 51 Good Practices prepared by TEAGASC in collaboration with other agroBRIDGES project partners, where the Good Practices, success cases and opportunities related to Short Food Supply Chains (SFSCs) across Europe are highlighted. The Good Practices are identified and collated from SFSCs in Denmark, France, Greece, Italy, Latvia, Lithuania, the Netherlands, Poland, Turkey, Finland, Ireland, Spain and other international representations across Europe.</p> <p>The dataset contains a spreadsheet containing the 51 Good Practices collected. The included cases are presented in alphabetical order and numbered from 1 -51:</p> <p>1. "Bios Coop" Social Consumer Cooperative </p> <p>2. AGFORISE (AGroFOod clusters platform with common long-term Research and Innovation) </p> <p>3. Agriloops </p> <p>4. Airfield Estate </p> <p>5. Benedicto Turgus (Benedict’s Market) </p> <p>6. Bienvenue à la ferme </p> <p>7. Bioalverde </p> <p>8. Burgerboerderijen </p> <p>9. CASETTA ROSSA Solidarity Purchase Group / Gruppo di Acquisto Solidale (GAS) Casetta Rossa </p> <p>10. Castlegregory Market </p> <p>11. Cloughjordan Community Farm </p> <p>12. Conservas artesanales contigo </p> <p>13. Digital Agriculture Market </p> <p>14. Drumanilra </p> <p>15. Evidence of Public Procurement of local food in the EU </p> <p>16. Fru Møllers Mølleri </p> <p>17. Gastroteca </p> <p>18. Kindergarten "Duzgynelis" </p> <p>19. Københavns Fødevarefællesskab (KBHFF)/Copenhagen Food Co-operative (CPH) </p> <p>20. La Colmena que dice sí </p> <p>21. La OSA Coop </p> <p>22. La OSA Coop </p> <p>23. La Ruche qui dit Oui/Boeren en Buren/L'Alveare Che Dice SÍ </p> <p>24. Local food program </p> <p>25. M**bun </p> <p>26. Manna Organic Store </p> <p>27. Más Que Lechugas </p> <p>28. Mobilus Turgelis </p> <p>29. Naturecode </p> <p>30. Né d'une seule ferme </p> <p>31. Podkarpacki E-bazarek </p> <p>32. Podkarpackie Smaki (Subcarpathian Flavors Cluster) </p> <p>33. Pora Na Pola </p> <p>34. American Farm School of Thessaloniki </p> <p>35. Public Food Services </p> <p>36. QualityLowInputFood (QLIF) </p> <p>37. Rechtstreek </p> <p>38. REKO food collective </p> <p>39. School milk and fruit </p> <p>40. Self picking berries </p> <p>41. Streeckgenoten</p> <p>42. SVAIGI - online shop </p> <p>43. Telaraki.com </p> <p>44. The Agricultural Marketing Platform </p> <p>45. The Cold Chain of Raw Milk </p> <p>46. The Eco-Cabinet (Økoskabet) </p> <p>47. The Little Cheese Shop </p> <p>48. Tiešā pirkšana - Direct shopping group </p> <p>49. TomaTomate </p> <p>50. Traditional United Europe Food (TRUEFOOD) </p> <p>51. Via Amerina and Forre Bio-District</p>
ROSEWOOD4.0 Best practices & innovations: CSV file
<p>ROSEWOOD4.0 harnesses digital solutions and knowledge transfer along the forest value chain to reinforce the sustainability of forest resilience and wood mobilisation in Europe. This CSV file includes the complete information of 279 Factsheets of <em>Best practices and Innovations</em> (BP&I) in forest management, wood supply and forest-based industries exploiting relevant digital technologies and industry 4.0 solutions. All these BP&I were jointly identified and validated by the project partners.</p> <p>The BP&I factsheets are published in a <em>Knowledge Platform for Regional Forest Innovation</em>, which is an open, multilingual repository (currently 13 European languages) created by the consortium to enable the widest possible dissemination of results. Spreading this knowledge in Europe will help practitioners and professionals to gain a better understanding of how the digital transformation in forestry can improve sustainable forest management and ecosystem resilience and thus benefit a more competitive forest-based sector in rural regions.</p> <p>The platform is accessible at: https://www.forestinnovationhubs.rosewood-network.eu</p> <p> </p>
Dataset of Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy
<p>The dataset includes search results used in article “Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy” biorXiv, 2022.01.17.476562, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. <a href="https://doi.org/10.1101/2022.01.17.476562">https://doi.org/10.1101/2022.01.17.476562</a></p> <p> </p> <p>Search Results Description: Please use Figure 1 from paper to trace the data made available and experimental group.</p> <p>The excel “raw data 2022-06-24” file contains the following data</p> <ol> <li>Body weight of each rabbit on a weekly basis</li> <li>Analysis of breeding parameters at mid-pregnancy</li> <li>Histological areas of each mammry tissue measured</li> <li>Optical density values obtained for biochemical leptin concentration determination</li> <li>Optical density values obtained for biochemical triglyceride concentration determination</li> <li>Optical density values obtained for biochemical glucose concentration determination</li> <li>Optical density values obtained for biochemical cholesterol concentration determination</li> <li>RT-qPCR results (Ct) from QuantStudio export for milk protein analysis</li> <li>RT-qPCR results (Ct) from QuantStudio export for lipid metabolism analysisen</li> </ol> <p>“Statistical analysis.doc” contained the description of the statistics used in Excel and the description of the linear mixed model analysis , the reference of the script available by the CRAN project is also include.The R scipt file for using the linear mixed model in R added with the "data-croissance-analysis" file.</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.