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641 results for “Guideline”
FUNDAMENTAL GUIDELINES FOR EXPLAINING CONCEPT
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reporting guideline of ARRIVE version 2.0 of paper
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Experiment Quality Evaluation Guidelines - Qualitative Study
<p>Experiment Quality Evaluation Guidelines - Qualitative Study</p>
In depth interview guideline for Assessment of Patient Safety Culture and Associated Factors among Healthcare Professionals in Public Hospitals of Bahir Dar City, Northwest Ethiopia
<p>The data contains in-depth interview guidline for <span><span>Assessment of Patient Safety Culture and Associated Factors among Healthcare Professionals in Public Hospitals of Bahir Dar City, Northwest Ethiopia: A Mixed-Methods Study</span></span></p> <p><span> </span></p>
Instruks Dataset - A dataset of Clinical Guidelines in Denmark
<p>Dataset of Clinical Guidelines used by Danish municipalities, collected in 2023 under permission of Region Syddanmark, Midtjylland, Nordjylland, Sjælland and Hovedstaden.</p> <p> </p> <p>The zip file ClinicalGuidelines.raw contains the following folders:</p> <ul> <li> <p>HTML_RegionHovedstaden: Clinical Guidelines used in the capital region (Hovedstaden)</p> </li> <li> <p>HTML_RegionMidt: Clinical Guidelines used in the region of Midtjylland,</p> </li> <li> <p>HTML_RegionNord: Clinical Guidelines used in the region of Nordjylland</p> </li> <li> <p>HTML_RegionSjælland: Clinical Guidelines used in the region of Sjælland,</p> </li> <li> <p>HTML_RegionSyd: Clinical Guidelines used in the Syddanmark region.</p> <p>The dataset has been collected with the consent of each of the areas. The dataset contains HTML files (that have been sanitized in follow-up versions of the datasets).</p> <p> </p> </li> </ul> <p>The dataset is the accompanying material for the paper "Automating Pathway Extraction from Clinical Guidelines: A Conceptual Model, Datasets and Initial Experiments" by Daniel Grathwol, Han van der Aa, and Hugo A. López. To appear in the 30th International Conference on Cooperative Information Systems. </p>
Sampling in Cloud Benchmarking: A Critical Review and Methodological Guidelines
<p>This replication package contains the data and code to replicate our critical review on sampling in cloud benchmarking.</p> <p> </p> <p><strong>Paper</strong></p> <p>Akbari, Saman, and Manfred Hauswirth. "Sampling in Cloud Benchmarking: A Critical Review and Methodological Guidelines." <em>2024 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)</em>. IEEE, 2024. DOI: <a href="https://doi.org/10.1109/CloudCom62794.2024.00034" target="_blank" rel="noopener">10.1109/CloudCom62794.2024.00034</a>.</p> <pre>@inproceedings{akbari2024sampling, title={Sampling in Cloud Benchmarking: A Critical Review and Methodological Guidelines}, author={Akbari, Saman and Hauswirth, Manfred}, booktitle={2024 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)}, pages={160--167}, year={2024}, organization={IEEE} }</pre> <p> </p> <p><strong>Abstract</strong></p> <blockquote> <p>Cloud benchmarks suffer from performance fluctuations caused by resource contention, network latency, hardware heterogeneity, and other factors along with decisions taken in the benchmark design. In particular, the sampling strategy of benchmark designers can significantly influence benchmark results. Despite this well-known fact, no systematic approach has been devised so far to make sampling results comparable and guide benchmark designers in choosing their sampling strategy for use within benchmarks. To identify systematic problems, we critically review sampling in recent cloud computing research. Our analysis identifies concerning trends: (i) a high prevalence of non-probability sampling, (ii) over-reliance on a single benchmark, and (iii) restricted access to samples. To address these issues and increase transparency in sampling, we propose methodological guidelines for researchers and reviewers. We hope that our work contributes to improving the generalizability, reproducibility, and reliability of research results.</p> </blockquote>
Guidelines for conducting ethical AI research in neurology: Supplementary materials
<p>Pre-emptive recognition of the ethical implications of study design and algorithm choices in artificial intelligence (AI) research is an important but challenging process. AI applications have begun to transition from a promising future to clinical reality in neurology. As the clinical management of neurology is often concerned with discrete, often unpredictable, and highly consequential events linked to multimodal data streams over long timescales, forthcoming advances in AI have great potential to transform care for patients. However, critical ethical questions have been raised with implementation of the first AI applications in clinical practice. Clearly, AI will have far-reaching potential to promote, but also to endanger, ethical clinical practice. This article employs an anticipatory ethics approach to scrutinize how researchers in neurology can methodically identify ethical ramifications of design choices early in the research and development process, with a goal of pre-empting unintended consequences that may violate principles of ethical clinical care. First, we discuss the use of a systematic framework for researchers to identify ethical ramifications of various study design and algorithm choices. Second, using epilepsy as a paradigmatic example, anticipatory clinical scenarios that illustrate unintended ethical consequences are discussed, and failure points in each scenario evaluated. Third, we provide practical recommendations for understanding and addressing ethical ramifications early in methods development stages. Awareness of the ethical implications of study design and algorithm choices that may unintentionally enter AI is crucial to ensuring that incorporation of AI into neurology care leads to patient benefit rather than harm.</p>
Figure 2 from: Nilsson R, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch C, Nylander J, Bergsten J, Porter T, Jumpponen A, Vaishampayan P, Ovaskainen O, Hallenberg N, Bengtsson-Palme J, Eriksson K, Larsson K, Larsson E, Kõljalg U (2012) Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys 4: 37-63. https://doi.org/10.3897/mycokeys.4.3606
Figure 2 - A reverse complementary sequence (bottom) aligned to its nine best BLAST matches, all of which were nearly identical to the query sequence based on BLAST scores, and all of which were given in the correct orientation by their respective authors.
Figure 6 from: Nilsson R, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch C, Nylander J, Bergsten J, Porter T, Jumpponen A, Vaishampayan P, Ovaskainen O, Hallenberg N, Bengtsson-Palme J, Eriksson K, Larsson K, Larsson E, Kõljalg U (2012) Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys 4: 37-63. https://doi.org/10.3897/mycokeys.4.3606
Figure 6 - An assembly chimera. The black dashed lines indicate breaks in the BLAST alignment and should always be taken to mean that manual examination is needed.
Figure 1 from: Nilsson R, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch C, Nylander J, Bergsten J, Porter T, Jumpponen A, Vaishampayan P, Ovaskainen O, Hallenberg N, Bengtsson-Palme J, Eriksson K, Larsson K, Larsson E, Kõljalg U (2012) Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys 4: 37-63. https://doi.org/10.3897/mycokeys.4.3606
Figure 1 - An ITS alignment featuring five random species each of the fungal phyla Ascomycota, Basidiomycota, Glomeromycota, Chytridiomycota, and Zygomycota s.l. The left half of the screen represents the ITS1 and the right half the 5.8S. Whereas the ITS1 alignment appears more or less chaotic, the 5.8S stands out as a very conserved element throughout these five phyla. The 5.8S starts at position 803 (indicated by the black cursor in the uppermost sequence). Seaview (Gouy et al. 2010) was used to display the alignment.
Figure 5 from: Nilsson R, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch C, Nylander J, Bergsten J, Porter T, Jumpponen A, Vaishampayan P, Ovaskainen O, Hallenberg N, Bengtsson-Palme J, Eriksson K, Larsson K, Larsson E, Kõljalg U (2012) Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys 4: 37-63. https://doi.org/10.3897/mycokeys.4.3606
Figure 5 - a Graphical overview of the BLAST results of a regular sequence b BLAST results of a chimeric sequence where the ITS1 comes from another species, such that the ITS1 is not involved in the alignment featuring the 5.8S+ITS2 (hence the lack of a match for the first ca. 180 bp.). Obviously, a severely compromised sequence that is already in INSD will always find a perfect match through BLAST in INSD: itself. In that case, the presence of a 100% similar reference sequence cannot be used as a testimony to the authenticity of the query sequence.
Figure 8 from: Nilsson R, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch C, Nylander J, Bergsten J, Porter T, Jumpponen A, Vaishampayan P, Ovaskainen O, Hallenberg N, Bengtsson-Palme J, Eriksson K, Larsson K, Larsson E, Kõljalg U (2012) Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys 4: 37-63. https://doi.org/10.3897/mycokeys.4.3606
Figure 8 - Untrimmed sequences tend to look like this when run through BLAST. Note how the first ca. 20 bp., and the last ca. 30 bp., of the query sequence (represented by the red bar with scale marks every 100 bp.) do not align to any of the BLAST hits. The use of different but closely situated primers may give a similar pattern, however, pointing at the need to also look at the BLAST alignments for start and end positions of the reference sequences.
Figure 4 from: Nilsson R, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch C, Nylander J, Bergsten J, Porter T, Jumpponen A, Vaishampayan P, Ovaskainen O, Hallenberg N, Bengtsson-Palme J, Eriksson K, Larsson K, Larsson E, Kõljalg U (2012) Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys 4: 37-63. https://doi.org/10.3897/mycokeys.4.3606
Figure 4 - A multiple alignment where the topmost sequence is chimeric and the remaining sequences represent its best BLAST matches. The alignment is fine in ITS1 and 5.8S (a; the 5.8S starts at position 479), but the alignment in ITS2 (b; position 637 and on) falls far short of scientific rigour. Alignments like these bespeak chimeric unions.
Figure 3 from: Nilsson R, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch C, Nylander J, Bergsten J, Porter T, Jumpponen A, Vaishampayan P, Ovaskainen O, Hallenberg N, Bengtsson-Palme J, Eriksson K, Larsson K, Larsson E, Kõljalg U (2012) Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys 4: 37-63. https://doi.org/10.3897/mycokeys.4.3606
Figure 3 - "Strand=Plus/Minus" indicates that the query and reference sequence come in opposing read directions. Another hint comes from the observation that the alignment starts at the first base (1) in the query sequence and progresses upwards to base 60 in the first alignment line; however, for the reference sequence, the alignment starts at base 635 and progresses downwards to base 578.
Figure 7 from: Nilsson R, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch C, Nylander J, Bergsten J, Porter T, Jumpponen A, Vaishampayan P, Ovaskainen O, Hallenberg N, Bengtsson-Palme J, Eriksson K, Larsson K, Larsson E, Kõljalg U (2012) Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys 4: 37-63. https://doi.org/10.3897/mycokeys.4.3606
Figure 7 - An assembly chimera. An extraneous sequence segment was assembled into a position where it should not have been, such as in the middle of the 5.8S. The white area in the reference sequences indicates the absence of sequence data for this particular part of the query sequence. Manual examination is always needed in cases like this.
Figure 6 from: Faulwetter S, Pafilis E, Fanini L, Bailly N, Agosti D, Arvanitidis C, Boicenco L, Capatano T, Claus S, Dekeyzer S, Georgiev T, Legaki A, Mavraki D, Oulas A, Papastefanou G, Penev L, Sautter G, Schigel D, Senderov V, Teaca A, Tsompanou M (2016) EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases. Research Ideas and Outcomes 2: e9774. https://doi.org/10.3897/rio.2.e9774
Figure 6 - Calman 1906 is available in BLR as https://zenodo.org/record/14941. The taxonomic treatment of Leucon longirostris G.O. Sars (shown above) extracted from this expedition document is also avaible in BLR: https://zenodo.org/record/14942. Both links have unique DOIs assigned to them and thus are also retrievable as https://doi.org/10.5281/zenodo.14941, and https://doi.org/10.5281/zenodo.14942, accordingly.
Figure 2 from: Faulwetter S, Pafilis E, Fanini L, Bailly N, Agosti D, Arvanitidis C, Boicenco L, Capatano T, Claus S, Dekeyzer S, Georgiev T, Legaki A, Mavraki D, Oulas A, Papastefanou G, Penev L, Sautter G, Schigel D, Senderov V, Teaca A, Tsompanou M (2016) EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases. Research Ideas and Outcomes 2: e9774. https://doi.org/10.3897/rio.2.e9774
Figure 2 - Stations without coordinates (red box) are commonly listed, as well as non-SI units, here: depth as fathoms (based on a slide by Aglaia Legaki, Gabriella Papastefanou and Marilena Tsompanou).
Figure 9 from: Faulwetter S, Pafilis E, Fanini L, Bailly N, Agosti D, Arvanitidis C, Boicenco L, Capatano T, Claus S, Dekeyzer S, Georgiev T, Legaki A, Mavraki D, Oulas A, Papastefanou G, Penev L, Sautter G, Schigel D, Senderov V, Teaca A, Tsompanou M (2016) EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases. Research Ideas and Outcomes 2: e9774. https://doi.org/10.3897/rio.2.e9774
Figure 9 - Top: to retrieve a scanned BHL book document from BHL click on the "Download Contents" icon on the top-right and select to browse the corresponding web page on the Internet Archive ("View at Internet Archive"). Bottom: The link to the jpeg2000 (JP2) image is found on the bottom right. Sources: top: http://biodiversitylibrary.org/page/9663476; bottom: https://archive.org/details/mittheilungenaus17staz.
Figure 8 from: Faulwetter S, Pafilis E, Fanini L, Bailly N, Agosti D, Arvanitidis C, Boicenco L, Capatano T, Claus S, Dekeyzer S, Georgiev T, Legaki A, Mavraki D, Oulas A, Papastefanou G, Penev L, Sautter G, Schigel D, Senderov V, Teaca A, Tsompanou M (2016) EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases. Research Ideas and Outcomes 2: e9774. https://doi.org/10.3897/rio.2.e9774
Figure 8 - Plazi workflow: from the publication through different levels of data processing to final availability of structured data.
Figure 10 from: Faulwetter S, Pafilis E, Fanini L, Bailly N, Agosti D, Arvanitidis C, Boicenco L, Capatano T, Claus S, Dekeyzer S, Georgiev T, Legaki A, Mavraki D, Oulas A, Papastefanou G, Penev L, Sautter G, Schigel D, Senderov V, Teaca A, Tsompanou M (2016) EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases. Research Ideas and Outcomes 2: e9774. https://doi.org/10.3897/rio.2.e9774
Figure 10 - Open Data: an emerging landscape of data and other academic publications (based on a slide by Dmitry Schigel).
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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.