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587 results for “guidance”
Data for "Soil CO2 efflux errors are lognormally distributed - Implications and guidance."
<p>Soil CO2 flux data at site ES-LMa of four automatic chambers in the control-openLand-subplot for the period from 2015-11-10 to 2016-11-10.</p> <p>These data were used for the publication:</p> <p>Wutzler, et al. (2020) "Soil CO2 efflux errors are lognormally distributed - Implications and guidance." Geoscientific Instrumentation, Methods, and Data Systems</p> <p>Variables, units and description are found in the ReadmeDataDescription.csv file</p> <p> </p>
Dataset from: "Reward expectation facilitates context learning and attentional guidance in visual search"
<p>Dataset for Bergmann N, Koch D, Schubö A (2019). Reward expectation facilitates context learning and attentional guidance in visual search, <em>Journal of Vision</em>, 19(3). <a href="https://doi.org/10.1167/19.3.10">https://doi.org/10.1167/19.3.10</a></p>
Dataset: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication
<p>The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below.</p> <ul> <li><code>year</code> = year of publication (YYYY)</li> <li><code>date</code> = date (YYYY-MM-DD) of publication</li> <li><code>title</code> = full paper title from journal website</li> <li><code>authors</code> = list of authors comma-separated</li> <li><code>n_authors</code> = number of authors (integer between 1 and 27)</li> <li><code>col_code</code> = color-issue classification (see below)</li> <li><code>volume</code> = Journal volume</li> <li><code>start_page</code> = first page of paper (consecutive)</li> <li><code>end_page</code> = last page of paper (consecutive)</li> <li><code>base_url</code> = base url to access the PDF of the paper with <code>/volume/start_page/year/</code></li> <li><code>filename</code> = specific file name of the paper PDF (e.g. <code>hess-9-111-2005.pdf</code>)</li> </ul> <p>Color classification is stored in the <code>col_code</code> variable with:</p> <ul> <li><code>0</code> = chromatic and issue-free,</li> <li><code>1</code> = red-green issues,</li> <li><code>2</code>= rainbow issues and</li> <li><code>bw</code>= black and white paper.</li> </ul> <p> </p> <p>See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology</p> <p>Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.</p> <p> </p> <p> </p>
Data for a publication "LIPSS pattern induced by polymer surface instability for myoblast cell guidance"
<p>The data set for contains data used in the article "LIPSS pattern induced by polymer surface instability for myoblast cell guidance".</p> <p><strong>Versions:</strong></p> <p><em><strong>V2: </strong>The current version contains reorganized file folders and is supplemented by the data found in the publication "LIPSS pattern induced by polymer surface instability for myoblast cell guidance". The dataset newly contains contact angle, cytocompatibility tests, FTIR analyses or Zeta potential results.</em></p> <p><strong>Article abstract</strong></p> <p>The presented study highlights the efficiency of employing a KrF excimer laser to create diverse types of periodic nanostructures (LIPSS – laser induced periodic surface structures) on polyether ether ketone (PEEK) and polyethylene naphthalate (PEN) substrates. LIPSS structures are very important both in tissue engineering and find also strong application in the field of sensor construction, and SERS analysis. By exposing the polymer films below their ablation threshold to laser fluence ranging from 4 to 16 mJ·cm<sup>-2</sup> at 6,000 pulses, we studied both single-phase exposure at beam incidence angles of 0° and 45°, and two-phase exposure. Atomic force microscopy analysis revealed that the laser-treated samples contained distinctive periodic patterns such as waves, globules, and pod-like structures each exhibiting unique surface roughness. Moreover, using analytical methods like EDS and XPS shed light on the changes in the atomic composition, specifically focusing on the C and O elements, as a result of laser exposure. Notably, in almost all cases, we observed an increase in oxygen percentage on the sample surfaces. This increase not only led to a decrease in the contact angle with water but also lowered the zeta potential value, thus showing that the modified samples have enhanced hydrophilicity of the surface and altered electrostatic properties. Last but not least, the samples were assessed for biocompatibility; we studied the interaction of the prepared replicates with mouse myoblasts (C2C12). The impact of globular/dot structures on the cell growth in comparison to pristine or linear LIPSS-patterned surfaces was determined. The linear pattern (LIPSS) induced the myoblast cell alignment along the pattern direction, while dot/globular pattern even enhanced the cytocompatibility compared to LIPSS samples. Through this comprehensive analysis, the research underscores the multifaceted implications of employing KrF excimer laser-induced nanostructures, ranging from surface morphology alterations to biocompatibility enhancements, thus, opening new avenues for advanced material engineering.</p>
Repository of NbS guidance material
<p><strong>NbS Guidance Repository </strong></p> <p>The NbS Guidance Repository is a collection of guides, handbooks, manuals, toolboxes, and other guidance materials. These resources are specifically designed to facilitate knowledge brokering and capacity-building on Nature-based Solutions (NbS) for a diverse group of non-academic actors. The guidance materials contain information on specific actions supporting the governance, planning, implementation, management, and monitoring of NbS or related concepts that work with nature, such as Green Infrastructure, Urban Forestry, and Ecosystem-based Adaptation. Additionally, they address challenges related to planning and implementing NbS, including issues with climate change adaptation, biodiversity enhancement, and environmental justice.</p> <p>The repository is established as part of the European Union Horizon 2020 project, CONEXUS, to catalogue the current status of guidance materials. Many European-funded research and innovation projects similarly transferred their gained knowledge and perspectives on NbS through guidance materials. Different platforms, like OPPLA, bring these materials together, yet the focus is Europe-centric. However, as several projects have involved cases outside Europe, such as CONEXUS’ cooperation with Latin American cities, this repository specifically included guidance materials from or relevant to Latin America. Thus, the collected guidance material focuses on urban contexts in Latin America and Europe, but materials with a global emphasis are also included. The materials are available in digital format, as PDFs (portable digital documents) or websites, to ensure easy accessibility.</p> <p>The guidance materials were collected from different avenues, such as published literature like the review of European NbS projects by <a href="https://research-and-innovation.ec.europa.eu/knowledge-publications-tools-and-data/publications/all-publications/nature-based-solutions-state-art-eu-funded-projects_en" target="_blank" rel="noopener">Wild et al. (2020)</a> and resource databases like NetworkNature. To supplement the collected guidance materials with more examples relevant to Latin America, CONEXUS partners and practitioners from the region who were engaged were asked to mention the NbS guidance materials they know about. Additional materials were collected through an internet search using the Google search engine in Spanish and Portuguese and through snowballing. As the focus is on materials targeted at non-academic users, scientific databases were not consulted.</p>
Testing Database Engines via Query Plan Guidance
<p>This artifact is the supplementary material for the paper "Testing Database Engines via Query Plan Guidance" that is published in ICSE'23.</p> <p>In the paper, we propose a new concept called Query Plan Guidance (QPG) for testing Database Management Systems (DBMSs). QPG tackles the test case generation problem by guiding test case generation towards exploring a variety of unique query plans. We found 50+ unique, previously-unknown bugs in SQLite, TiDB, and CockroachDB.</p> <p>The prototype of QPG is a Java project, and we provide Dockerfiles to reproduce our three key empirical results.</p>
Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis"
<p>This resources provides pre-processed input data, model checkpoints and model outputs for experiments on the OpenI dataset in below study. </p> <blockquote> <p>Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2023. <a href="https://arxiv.org/abs/2307.12803">Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis</a>. In Proceedings of the 16th International Natural Language Generation Conference (INLG), Prague, Czech Republic. Association for Computational Linguistics.</p> </blockquote> <p>For more information please refer to the accompanying paper and code repository (<a href="https://github.com/jantrienes/inlg2023-radsum">https://github.com/jantrienes/inlg2023-radsum</a>).</p> <p><strong>The data is structured as follows:</strong></p> <ul> <li><code>data/preprocessed/</code> includes the dataset(s) for each model</li> <li><code>output/</code> includes one folder for each experiment/model run. The first part of each output path indicates the dataset that was used at inference.</li> <li>For a mapping between model IDs and results in the paper, see below table. All models were also trained <em>with the background section as input. </em>These are available in directories with the <code>-bg-</code> qualifier. </li> </ul> <table> <thead> <tr> <th>Model name in paper</th> <th>Output directory</th> </tr> </thead> <tbody> <tr> <td><em>Results from Table 2</em></td> </tr> <tr> <td>OracleExt</td> <td>openi-unguided/oracle</td> </tr> <tr> <td>BertExt (Liu and Lapata, 2019)</td> <td>openi-unguided/bertext-default</td> </tr> <tr> <td>BertAbs (Liu and Lapata, 2019)</td> <td>openi-unguided/bertabs-default</td> </tr> <tr> <td>GSum (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>GSum w/ LR-Approx</td> <td>openi-bertext-default-clip-lrapprox/gsum-default</td> </tr> <tr> <td>GSum w/ BERT-Approx</td> <td>openi-bertext-default-clip-bertapprox/gsum-default</td> </tr> <tr> <td>GSum w/ Thresholding</td> <td>openi-bertext-default-clip-threshold/gsum-default</td> </tr> <tr> <td>WGSum (Hu et al., 2021)</td> <td>openi-wgsum/wgsum-default</td> </tr> <tr> <td>WGSum+CL (Hu et al., 2022)</td> <td>openi-wgsum-cl/wgsum-cl-default</td> </tr> <tr> <td><em>Results from Table 3</em></td> </tr> <tr> <td>Fixed (k=1)</td> <td>openi-unguided/bertext-default-clip-k1</td> </tr> <tr> <td>LR-Approx</td> <td>openi-unguided/bertext-default-clip-lrapprox</td> </tr> <tr> <td>BERT-Approx</td> <td>openi-unguided/bertext-default-clip-bertapprox</td> </tr> <tr> <td>Thresholding</td> <td>openi-unguided/bertext-default-clip-threshold</td> </tr> <tr> <td>k = |OracleExt|</td> <td>openi-unguided/bertext-default-clip-oracle</td> </tr> <tr> <td><em>Results from Table 4</em></td> </tr> <tr> <td>Fixed (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>Oracle Length</td> <td>openi-bertext-default-clip-oracle/gsum-default</td> </tr> <tr> <td>Oracle Length + Content</td> <td>openi-oracle/gsum-default</td> </tr> <tr> <td><em>Results from Table 5</em></td> </tr> <tr> <td>BertExt w/ k=[1,5]</td> <td>openi-unguided/bertext-default-clip-k{1,2,3,4,5}</td> </tr> <tr> <td>GSum w/ k=[1,5]</td> <td>openi-bertext-default-clip-k{1,2,3,4,5}/gsum-default</td> </tr> </tbody> </table>
Experimental evaluation of louver guidance efficiency for green sturgeon (Acipenser medirostris), Primary Datasets, 2016-2017
Throughout the world, louver-bypass systems are a common method for fish protection at water diversion sites. This study used controlled laboratory experiments to quantify louver efficiency for juvenile green sturgeon under a range of conditions. Green sturgeon juveniles used in the study were spawned from the University of California, Davis (hereafter UC Davis) captive broodstock program. Experimental trials were conducted within an indoor flume at the J. Amorocho Hydraulics Laboratory (JAHL) at UC Davis. The flume had a zero degree bed slop, and was equipped with a louver placed at a 15-degree angle to the streamwise flow. Louver slats had 25-mm clear spacing. The louver terminated at a bypass channel 0.3 m wide, and was operated to maintain water velocity in the bypass that was 1.2 times greater than the velocity in the flume. Full factorial experimental treatments were designed to address the influence of sturgeon size, water velocity, diel period, and water temperature on louver performance and behavior. Sturgeon were tested within three predefined size classes (range: 6 – 34 cm TL). At each size class, fish were tested under combinations of water velocity, water temperature, and photophase. During each experimental trial, 60 (+/-2) juvenile sturgeon were released from an acclimation chamber to enter the test area at the bottom of the flume. Fish were allowed to freely navigate throughout the test area until they were transported through the louver slats or bypass channel, at which point they were removed and the time was recorded. Trials ended when all fish were collected at a downstream endpoint, or after 90 minutes of exposure time, and remaining fish were removed from the flume. Trials were completed across two study years within seven months after hatch, following the same protocols with a few minor adjustments in year two. During daytime trials in both study years the louver face was monitored with video recordings to observe contact between fish and the
Antimicrobial Stewardship & Patient Safety Improvements: Introducing the NIVAS Line Flushing Guidance
<p>Recent published literature highlights that as much as 35% [1] of medication may remain in the infusion line as residual volume. The line is not commonly flushed outside of paediatric and oncology settings and therefore the total prescribed dose is not administered to patients, and this residual medication is discarded, raising the issue of underdosing. [2]</p><p> </p><p>At Salisbury NHS Foundation Trust, the Medical Device Management Services team is actively working towards improving patient safety and recovery through antimicrobial stewardship and compliance with new NIVAS guidelines [3]. We were keen to understand the implications of current intravenous administration practice at our Trust. We were particularly interested in assessing the prevalence and extent to which patients are underdosed, and the cost implications of discarded medication.</p>
PRETEST AND POSTEST OF EXPERT SYSTEM FOR VOCATIONAL GUIDANCE
<p>Database on the process of vocational orientation in the I.E.P San Pedro - Quinocay in the province of Yauyos.</p>
Image repository for "Towards advancing Translators' Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 context"
<p>The files on this trusted repository are provided by the authors of the manuscript with the title “Towards advancing Translators’ Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 context” that was received by the MDPI journal Sustainability (ISSN 2071-1050) on 29 January 2024, got the manuscript ID sustainability-2872279, and is intended to become part of the special issue “Sustainable Materials, Manufacturing and Design” accessible under the link <a href="https://www.mdpi.com/journal/sustainability/special_issues/Sus_materials_manufacturing_design">https://www.mdpi.com/journal/sustainability/special_issues/Sus_materials_manufacturing_design</a>.</p> <p>The authors Paul-Ludwig Michael Noeske, Alexandra Simperler, Welchy Leite Cavalcanti, Vinicius Carrillo Beber, Brendon Weager, Tasmin Alliott, Peter Schiffels, and Gerhard Goldbeck aim at facilitating common access to the files representing high-resolution microscopy images (corresponding to the light microscopy (LM) and scanning electron microscopy (SEM) images shown in Figure 9 and Figure 12 in the manuscript or complementing them) given in .jpg and .tif format, respectively. Moreover, this repository comprises a .csv file containing the data points underlying the values presented in Table A1 of this manuscript and their description. The authors indicate here that following the sixth step of the translation process in materials modelling the translator may provide these data in this presentation that is adapted to the process-centric perspective required by representatives of an enterprise manufacturing prepregs and to their background knowledge disclosed to the translator beforehand.“</p>
Supporting publication for 'Prevalence sample-based guidance for reporting 2021 data'
<p>The record is aimed at helping the reporting countries to submit their sample-based level data to the EFSA Data Collection Framework. We include here two excel files and one XML file, and we give below specific information on their use.</p> <p>The two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes, and offer examples on how prevalence data can be reported using SSD2 and how data are aggregated afterwards. The XML file is the same example as in the Excel file with similar title but in the XML format that allows for it be uploaded in the Data Collection Framework.</p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Dataset]
<p>Dataset used for the paper submitted to RO-MAN 2022</p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p>
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 4. Areas (polygons) in Western Podillya (Ukraine), where there is a predicted probability for the accommodation 9, 8 or 7 reptile species (gradient from dark gray — 9 species to light — 7 species). Districts numbered as in fig. 3.
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 3. Areas (downward diagonal filled polygons) in Western Podillya (Ukraine), where the average predicted habitat suitability for reptile species exceeds 0.5 (Districts: 1 — Terebovlianskyi, 2 —Husiatynskyi, 3 — Buchatskyi, 4 — Chortkivskyi, 5 — Chemerovetskyi, 6 — Horodenkivskyi, 7 — Zalishchytskyi, 8 — Borshchivskyi, 9 — Kamianets-Podilskyi, 10 — Zastavnivskyi, 11 — Khotynskyi).
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 4. Two upper categories ("Moderate" and "High") collapsed to identify areas of predicted presence (dark gray shading) for B. variegata in the study area.
Fig. 1 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 1. Response of Bombina variegata to Bio 11: x-axis — mean temperature of coldest quarter (°C x 10); y- axis— logistic output (probability of presence).
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 3. Response of Triturus cristatus to the Human Footprint: x-axis — Human Footprint; y-axis — logistic output (probability of presence).
Fig. 2 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 2. Response of Pelobates fuscus to Bio 3: x-axis — isothermality; y-axis — logistic output (probability of presence).
OCT porcine kidney dataset for percutaneous nephrostomy guidance
<h2><strong>Code</strong> [<a href="https://github.com/thepanlab/FOCT_kidney" target="_blank" rel="noopener">GitHub</a>] | <strong>Publication</strong> [<a href="https://doi.org/10.1364/BOE.421299" target="_blank" rel="noopener">Biomedical Optics Express'21</a>]</h2> <h3>Abstract</h3> <p>Percutaneous renal access is the critical initial step in many medical settings. In order to obtain the best surgical outcome with minimum patient morbidity, an improved method for access to the renal calyx is needed. In our study, we built a forward-view optical coherence tomography (OCT) endoscopic system for percutaneous nephrostomy (PCN) guidance. Porcine kidneys were imaged in our experiment to demonstrate the feasibility of the imaging system. Three tissue types of porcine kidneys (renal cortex, medulla, and calyx) can be clearly distinguished due to the morphological and tissue differences from the OCT endoscopic images. To further improve the guidance efficacy and reduce the learning burden of the clinical doctors, a deep-learning-based computer aided diagnosis platform was developed to automatically classify the OCT images by the renal tissue types. Convolutional neural networks (CNN) were developed with labeled OCT images based on the ResNet34, MobileNetv2 and ResNet50 architectures. Nested cross-validation and testing was used to benchmark the classification performance with uncertainty quantification over 10 kidneys, which demonstrated robust performance over substantial biological variability among kidneys. ResNet50-based CNN models achieved an average classification accuracy of 82.6%±3.0%. The classification precisions were 79%±4% for cortex, 85%±6% for medulla, and 91%±5% for calyx and the classification recalls were 68%±11% for cortex, 91%±4% for medulla, and 89%±3% for calyx. Interpretation of the CNN predictions showed the discriminative characteristics in the OCT images of the three renal tissue types. The results validated the technical feasibility of using this novel imaging platform to automatically recognize the images of renal tissue structures ahead of the PCN needle in PCN surgery.</p> <h3>Description</h3> <p>The dataset contains OCT images of 10 porcine kidneys from three tissues: cortex, medulla, and pelvis calyx. There is 1000 images per tissues/per kidney. More information about the dataset can be found in the paper: <a href="https://doi.org/10.1364/BOE.421299">https://doi.org/10.1364/BOE.421299</a></p> <p>The repository that processed this dataset can be found at <a href="https://github.com/thepanlab/FOCT_kidney">https://github.com/thepanlab/FOCT_kidney</a></p> <p> </p>
ScienceDex guides
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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.