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opencc-by-4.0Apr 2023View details →

Expert review of iNaturalist identifications of Australian millipedes in GBIF

This tab-separated file ("millipede_review.txt") is derived from the "verbatim.txt" file in a Darwin Core archive downloaded 2023-03-24 from GBIF (DOI: <a href="https://doi.org/10.15468/dl.jtpncb">https://doi.org/10.15468/dl.jtpncb</a>). The 2029 records in the download are "Research Grade" iNaturalist observations of millipedes observed in Australia. Encoding is UTF-8. The dataset has the following Darwin Core fields from "verbatim.txt" and my added fields (fieldnames in capitals): gbifID = GBIF occurrence record code; record accessible as https://www.gbif.org/occurrence/[gbifID] catalogNumber = iNaturalist observation number; record accessible as https://www.inaturalist.org/observations/[catalogNumber] scientificName = the taxon identification made on the iNaturalist platform and accepted by GBIF IDCHECK = my assessment of the identification (see below) COMMENT = my explanation for doubting an identification (see below) recordedBy = the name used for copyright assignment by the original observer on iNaturalist USERNAME = the iNaturalist username of the original observer RGID1 = the iNaturalist username of the first person to suggest the final, accepted identification RGID2 = the iNaturalist username of the second person to suggest the final, accepted identification RGID3 = the iNaturalist username of the third person to suggest the final, accepted identification RGID4 = the iNaturalist username of the fourth person to suggest the final, accepted identification RGID5 = the iNaturalist username of the fifth person to suggest the final, accepted identification I reviewed the images and image sets on iNaturalist that are referred to in these records between 2023-03-24 and 2023-04-02. I classed the iNaturalist identifications in the IDCHECK field as follows: correct - The image clearly shows the diagnostic characters of the taxon likely -  The ID is probably correct, but I can't be sure because diagnostic characters aren't clearly visible in the image possible -  The ID might be correct, but the image isn't good enough to distinguish the identified taxon from another, similar taxon unlikely -  The ID is probably incorrect because the image appears to show a different taxon, although diagnostic characters aren't clearly visible incorrect -  The image clearly shows the diagnostic characters of a different taxon

opencc-by-4.0Apr 2023View details →

Journal metrics as predictors of Research Excellence Framework 2021 results: Comparison of impact factor quartiles and Finnish expert-ratings - dataset

This dataset accompanies the conference submission 'Journal metrics as predictors of Research Excellence Framework 2021 results: Comparison of impact factor quartiles and Finnish expert-ratings'. It contains the data in CSV format, one file per unit of analysis (Units of Assessment, Higher Education Institutions, and Subject Areas). The format of the UoA file is as follows (the other two files are analogous): <ul> <li> institution_name: name of higher education institution (e.g. university) </li> <li> unit_of_assessment_name: UoA name in REF (https://www.ref.ac.uk/panels/units-of-assessment/) </li> <li> main_panel: main panel in REF </li> <li> multiple_submission_letter: blank unless submitted to multiple panels.Exceptionally HEIs may have requested permission to make <a href="https://ref.ac.uk/publications-and-reports/invitation-to-make-requests-for-multiple-submissions-exception-from-submission-for-small-units-and-for-impact-case-studies-requiring-security-clearance/">two submissions from the same UoA to different panels</a>. </li> <li> multiple_submission_name: blank unless submitted to multiple panels </li> <li> non_english: number of articles in language other than English </li> <li> jufo_score_uoa: JUFO score (see paper for calculation details) of UoA </li> <li> jif_score_uoa: JIF score (see paper for calculation details) of UoA </li> <li> ref_score_uoa: REF score (see paper for calculation details) of UoA </li> </ul>

opencc-by-4.0May 2023View details →

A large expert-curated cryo-EM image dataset for machine learning protein particle picking

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.

opencc-by-4.0Apr 2023View details →

Vollständige Transkripte der Expert*inneninterviews zur Masterarbeit "Digitale Badges als Kompetenznachweis – Erwartungen aus Sicht von HR-Verantwortlichen in Österreich"

Vollständige Transkripte der Expert*inneninterviews zur Masterarbeit "Digitale Badges als Kompetenznachweis – Erwartungen aus Sicht von HR-Verantwortlichen in Österreich"

opencc-by-4.0Jul 2023View details →

Supplementary material of the study "Help! I need somebody. A Mapping Study about Expert Identification in Software Development"

Supplementary Material Context: Software development is a knowledge-intensive activity, and its success in an organization relies deeply on knowledge sharing. Knowledge management challenges are often increased in agile environments, which involve a lot of tacit knowledge, commonly acquired through experiences and hard to be made explicit. Therefore, knowledge sharing among practitioners is crucial. However, identifying suitable experts to share specific knowledge is not trivial. It involves not only discovering the individuals with the desired knowledge but also considering other factors that may improve the expert responsiveness, such as social connections and availability. Objective: Considering the important role experts play in knowledge sharing, we decided to investigate approaches that help identify experts that can share knowledge in software development. Our goal is to provide a panorama of the existing approaches and shine a light on research opportunities. Method: We carried out a systematic literature mapping and analyzed 17 publications. Results: The results show that most approaches have relied on code repositories as a source of evidence for identifying experts and, consequently, focus on supporting developers and aiding in the codification activity. Additionally, expert identification has been mostly automated, and factors beyond possessing the desired knowledge have often been disregarded. Conclusion: Although there are several expert identification approaches, there has been a lack of concern with factors that influence reaching the most suitable expert for a specific situation (e.g., considering the characteristics of the person seeking knowledge). Moreover, there is a need for deeper reflection on how to better explore different artifacts as sources of expert evidence and how to combine them to improve expert identification. This package contains supplementary material of the study performed to investigate approaches that help identify experts that can share knowledge in software development. It contains: <ul> <li>A spreadsheet containing raw data (research protocol, considered and selected publications, and research questions answers).</li> </ul>

opencc-by-4.0Dec 2022View details →

Expert survey data- Low trophic aquaculture products

The expert data was collected through two different events. The first event was a tasting event organized in France in October 2022, where the participants consisted primarily of chefs and cooking students who possess practical knowledge of preparing food. During this event, the experts had the opportunity to taste the LTA products, providing valuable insights. The second event took place during the AquaVitae annual meeting in Brazil in April 2023. Although the experts in Brazil did not have the chance to taste the products, they exhibited extensive familiarity with these species due to their years of experience working with them.

opencc-by-4.0Sep 2023View details →

SE-PEF: a Resource for Personalized Expert Finding

The problem of personalization in Information Retrieval has been under study for a long time. A well know issue related to this task is the lack of publicly available datasets that can support a comparative evaluation of personalised search systems. To contribute in this respect, this paper introduces SE-PEF (StackExchange - Personalized Expert Finding), a resource useful for designing and evaluating personalized models related to the task of Expert Finding (EF). The contributed dataset  includes more than  250k queries and 565k answers from 3,306 experts, which are annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. The results of the preliminary experiments conducted show the appropriateness of SE-PEF to evaluate and to train effective EF models. If you use this dataset, please also cite the following: ``` @inproceedings{   10.1145/3624918.3625335,   author = {Kasela, Pranav and Pasi, Gabriella and Perego, Raffaele},   title = {SE-PEF: a Resource for Personalized Expert Finding},   year = {2023},   isbn = {9798400704086},   publisher = {Association for Computing Machinery},   address = {New York, NY, USA},   url = {https://doi.org/10.1145/3624918.3625335},   doi = {10.1145/3624918.3625335},   booktitle = {Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region},   pages = {288–309},   numpages = {22},   series = {SIGIR-AP '23} } ```

An Expert System to Reduce Depression in Primary Care

ClinicalTrials.gov study NCT00497874. IPD Sharing: Not stated. Countries: 1. Publications: 1.

ACCU-CHEK® Aviva Expert Study: Does Use of a Bolus Advisor Improve Glycemic Control in Patients Not Achieving Optimal Control Using Multiple Daily Injections (MDI)?

ClinicalTrials.gov study NCT01460446. IPD Sharing: Not stated. Countries: 2. Publications: 1.

Surgery for Cancer With Option of Palliative Care Expert

ClinicalTrials.gov study NCT03436290. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.

EXPERT CTO: Evaluation of the XIENCE PRIME™ LL and XIENCE Nano™ Everolimus Eluting Coronary Stent Coronary Stents, Performance, and Technique in Chronic Total Occlusions

ClinicalTrials.gov study NCT01435031. IPD Sharing: Not stated. Countries: 1. Publications: 1.