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APPENDIX D of the 2022 EU Report on Pesticide Residues
<p>Detailed information on the different control programmes, full list of samples exceeding the MRLs anonymised by sample code, analytical scope from the official laboratories reporting to EFSA data on pesticide residues, the HBGV and PF used in the risk assessment.</p> <p><br>Detailed information on deterministic and probabilistic risk assessment results.</p>
Fractesus_VTT_73W_RR2_report
<p>Fractesus project. Fracture test mini-CT. Master Curve output. Framatome. </p>
Figure 7 in First report of Hestiasula castetsi (Bolivar, 1897) from Kerala, India with description of unique male specimens (Mantodea: Hymenopodidae: Acromantinae)
Figure 7. Apex of abdomen.
Figure 5 in First report of Hestiasula castetsi (Bolivar, 1897) from Kerala, India with description of unique male specimens (Mantodea: Hymenopodidae: Acromantinae)
Figure 5. Inner view of left fore leg.
Figure 10 in First report of Hestiasula castetsi (Bolivar, 1897) from Kerala, India with description of unique male specimens (Mantodea: Hymenopodidae: Acromantinae)
Figure 10. Genital, in situ photo.
Laboratory data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - North Macedonia
<p>This dataset contains ASF laboratory analytical results in domestic pigs and wild boar.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2023_MK - Food and Veterinary Agency (FVA)</li> <li>ASF2022_MK - Food and Veterinary Agency (FVA)*</li> <li>ASF2022_MK - Food and Veterinary Agency (FVA)</li> </ul> <p> </p> <p> </p> <p>*This version of the ASF laboratory data has been republished with the subunit identification code (sampUnitIds.subUnitId) column empty due to data protection reasons</p>
Daten der FDM-Bedarfserhebung an acht Hochschulen in Brandenburg (IN-FDM-BB Report R 1.2.1)
<p><strong>Data and reproducible code for the rdm survey at eight universities (of applied sciences) in Brandenburg</strong></p> <p>The accompanying README file contains information about the folders and files, and instructions to reproduce the code and figures. The file <span>DMP_FDM_Bedarfserhebung_IN-FDM-BB_2024.pdf contains the data management plan.</span></p> <p><strong>Daten und reproduzierbarer Code zur FDM-Bedarfserhebung an acht brandenburgischen Hochschulen </strong></p> <p>Die README-Datei enthält die relevanten Informationen zu allen Ordnern und Dateien in Datenpublikation_FDM_Bedarfserhebung_IN-FDM-BB_2024.zip. Die Datei <span>DMP_FDM_Bedarfserhebung_IN-FDM-BB_2024.pdf beinhaltet den zugehörigen Datenmanagementplan.</span></p> <p><strong>GENERAL INFORMATION</strong></p> <p><em>Title of Dataset: </em> </p> <p>Dataset from a survey on the handling of research data at eight universities (of applied sciences) in Brandenburg</p> <p>***</p> <p>Datensatz der Bedarfserhebung zum Umgang mit Forschungsdaten an acht Hochschulen in Brandenburg</p> <p> </p> <p><em>Date of data collection:</em><strong> </strong></p> <p>April to May 2023</p> <p>***</p> <p>April bis Mai 2023</p> <p> </p> <p><em>Collection instrument: </em></p> <p>The online tool TH Survey based on Quamp (Sociolutions): software to collect, manage and analyse empirical data</p> <p>***</p> <p>Das Online-Tool TH Survey basierend auf der Software Quamp (Sociolutions) für die Erhebung, die Verwaltung und die Auswertung empirischer Daten</p> <p><em> </em></p> <p><em>Funding:</em> </p> <p>The Federal Ministry of Education and Research (BMBF) funds the universities of applied sciences in the project IN-FDM-BB; the Ministry of Science, Research and Culture (MWFK) funds the universities that are part of the project IN-FDM-BB.</p> <p>***</p> <p>Das Bundesministerium für Bildung und Forschung (BMBF) fördert die am Projekt IN-FDM-BB beteiligten Hochschulen für Angewandte Wissenschaften); das brandenburgische Ministerium für Wissenschaft, Forschung und Kultur (MWFK) fördert die am Projekt IN-FDM-BB beteiligten Universitäten. Weitere Informationen zum Projekt unter https://fdm-bb.de/ueber-das-projekt-2/.</p> <p><br><strong>SHARING/ACCESS INFORMATION</strong></p> <p><em>Licenses/restrictions placed on the data: </em>These data are available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a> </p> <p><em>Links to publications that cite or use the data: </em></p> <p>Daniela Mertzen. „IN-FDM-BB Report: R 1.2.1 Aktuelle Kenntnisse und Bedarfe im Bereich Forschungsdatenmanagement an acht brandenburgischen Hochschulen“. FDM-BB, 16. Mai 2024. <a href="https://doi.org/10.5281/zenodo.11044845">https://doi.org/10.5281/zenodo.11044845</a>.</p> <p>Claudia Haase, Daniela Mertzen, Myriam Musolff, Michael Panitz, Carina Schiller, Stefanie Schreiber, Claus Spiecker, und Ian Wolff. „IN-FDM-BB Werkstattbericht: W 1.2.2 Auswertung Bedarfserhebung mit daraus folgenden Aktivitäten“. FDM-BB, 20. Dezember 2023. <a href="https://doi.org/10.5281/zenodo.10408198">https://doi.org/10.5281/zenodo.10408198</a>.</p> <p>Michael Panitz. „IN-FDM-BB Werkstattbericht: W 1.2.1 Konzept der Bedarfserhebung“. Werkstattbericht. Zenodo, 30. März 2023. <a href="https://doi.org/10.5281/zenodo.7870896">https://doi.org/10.5281/zenodo.7870896</a>.</p> <p>***</p> <p>Lizenz: Diese Daten sind unter einer <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a> Lizenz verfügbar.</p> <p>Links zu den Publikationen, die diese Daten verwenden:</p> <p>Daniela Mertzen. „IN-FDM-BB Report: R 1.2.1 Aktuelle Kenntnisse und Bedarfe im Bereich Forschungsdatenmanagement an acht brandenburgischen Hochschulen“. FDM-BB, 16. Mai 2024. <a href="https://doi.org/10.5281/zenodo.11044845">https://doi.org/10.5281/zenodo.11044845</a>.</p> <p>Claudia Haase, Daniela Mertzen, Myriam Musolff, Michael Panitz, Carina Schiller, Stefanie Schreiber, Claus Spiecker, und Ian Wolff. „IN-FDM-BB Werkstattbericht: W 1.2.2 Auswertung Bedarfserhebung mit daraus folgenden Aktivitäten“. FDM-BB, 20. Dezember 2023. <a href="https://doi.org/10.5281/zenodo.10408198">https://doi.org/10.5281/zenodo.10408198</a>.</p> <p>Michael Panitz. „IN-FDM-BB Werkstattbericht: W 1.2.1 Konzept der Bedarfserhebung“. Werkstattbericht. Zenodo, 30. März 2023. <a href="https://doi.org/10.5281/zenodo.7870896">https://doi.org/10.5281/zenodo.7870896</a>.</p> <p><br><strong>DATA & FILE OVERVIEW</strong></p> <p>For an overview, please see the README file (English version: README-EN.md, German version: README-DE.md).</p> <p>***</p> <p>Eine Übersicht ist in der README-Datei zu finden (deutsche Fassung: README-DE.md, Englische Fassung: README-EN.md).</p> <p><br><strong>METHODOLOGICAL INFORMATION</strong></p> <p><em>Description of methods used for collection/generation of data: </em></p> <p>To collect these data, a quantitative data collection method was employed: An online survey on research data management among researchers and research support staff at eight publicly-funded research-focused universities (of applied sciences) was carried out in April and May of 2023. The survey was implemented in the online tool „TH Survey“ at the Technical University of Applied Sciences Wildau wich is based on Quamp (Sociolutions), a software to collect, manage and analyse empirical data. The survey consisted of a total of 32 questions on the handling of research data and their requirements for support in this domain.</p> <p>The raw data (in XLSX format) were processed in R (programming language) and the datasets for analysis were converted to a non-proprietary CSV format.</p> <p>A total of 550 participants took part in the survey. The dataset was split into one dataset for participants who are active researchers (N = 486) and one dataset for participants who are not active researchers (N = 64). The latter group only saw 12 instead of all 32 questions. The datasets for each university (applied sciences) are subsets of the dataset of active researchers (N = 486). </p> <p>***</p> <p>Zur Erhebung der hier präsentierten Daten wurde eine quantitative Datenerhebungsmethode gewählt. Eine Online-Bedarfsumfrage zum Thema Forschungsdatenmanagement unter den Forschenden der acht staatlichen forschenden Hochschulen wurde im April und Mai 2023 durchgeführt. Die Umfrage wurde in dem Online-Tool „TH Survey“ an der Technischen Hochschule Wildau implementiert; dieses Tool basiert auf Quamp (Sociolutions), einer Software zur Erhebung, zur Verwaltung und zur Auswertung empirischer Daten. Die Umfrage umfasst 32 Fragen zum Umgang mit Forschungsdaten sowie zu Unterstützungsbedarfe der Forschenden in diesem Bereich.</p> <p>Die Rohdaten (im XLSX-Format) wurden mithilfe der Programmiersprache R verarbeitet und die Datensätze zur Auswertung wurden in ein nicht-proprietäres CSV-Format übertragen.</p> <p>Insgesamt 550 Teilnehmende füllten den Online-Fragebogen aus. Dieser Datensatz wurde geteilt; ein Datensatz beinhaltet nur aktiv forschende Teilnehmende (N = 486), ein weiterer Datensatz enthält die Antworten der nicht aktiv forschenden Teilnehmenden (N = 64). Letztere Gruppe sah nur 12 der insgesamt 32 Fragen. Die Datensätze für jede Hochschule sind Subsets des Datensatzes der aktiv Forschenden (N = 486).</p> <p> </p> <p><em>Methods for processing the data: </em></p> <p>Results were prepared for analysis with the programming language R. The datasets can be found in the „data“ folder.</p> <p>Along with the data, the fully reproducible analysis code (RMD (R-Markdown) files) that generates the figures in the accompanying papers can be found below.</p> <p>***</p> <p>Die Rohdaten wurden mit der Programmiersprache R zur Auswertung vorbereitet. Die Datensätze (im CSV-Format) finden sich im Unterorder „data“. Neben den Daten wird der reproduzierbare Code (RMD (R Markdown Dateien) zur Auswertung hier publiziert, welcher die im zugehörigen Papier abgebildeten Visualisierung generiert.</p> <p><em> </em></p> <p><em>Instrument- or software-specific information needed to interpret the data: </em></p> <p>The codebook required to interpret the values in the published datasets.</p> <p>***</p> <p>Das Codebuch wird benötigt, um die Werte in den publizierten Datensätzen zu interpretieren.</p>
Data and analysis for "Association of self-reported musculoskeletal pain with school furniture suitability and daily activities among primary school and university students"
<p>Datasets and R analysis code and report for the article "Association of self-reported musculoskeletal pain with school furniture suitability and daily activities among primary school and university students".</p>
Fig. 2 in First report on the occurrence of two marine ornamental fishes (Acanthuriformes) from Northern part of the Bay of Bengal, India
Fig. 2 — Zanclus cornutus (Linnaeus, 1758)
Fig. 1 in First report on the occurrence of two marine ornamental fishes (Acanthuriformes) from Northern part of the Bay of Bengal, India
Fig. 1 — Acanthurus xanthopterus Valenciennes, 1835
Histochemical β-glucuronidase (GUS) assay of miR171a/miR171a* reporters
<p>CORRECTION</p> <p>Manavella PA, Koenig D, Rubio-Somoza I, Burbano HA, Becker C, Weigel D (2013) <span>Tissue-specific silencing of Arabidopsis SU(VAR)3-9 HOMOLOG8 by miR171a. </span><span>Plant Physiol. 161(2):805-12. doi: 10.1104/pp.112.207068<br><br></span></p> <p><span>A duplication occurred during preparation of Figure 6, with an image of the ProMIR171A::GUS x MIMI171A (Row 1, Column 6) control cross duplicated in the panel that was supposed to show another control cross, ProSUVH8::GUSmiR171a* x MIMI171A* (Row 6, Column 6). The erroneously duplicated image meant to show, as a control, the reversion of the silencing phenotype in nascent secondary root tips by a mimicry construct designed to titrate the endogenous miR171a*. The same suppression of miR171a*-triggered silencing of ProSUVH8::GUSmiR171a*<span> </span>by MIM171A* was also shown for flowers, seedlings, and epidermis in the same figure (Row 6, Columns 2, 4, and 8). This data set includes all the original photographs displayed in figure 6.</span></p>
Enhancing Automated Tools: Reporting Improvements and Bugs with Bug Builder
<p><strong>Qualitative Research</strong></p> <p><strong>Part I - Manual Process</strong></p> <p>Task: Open a Issue for some automated test tool</p> <p><em>Geneva Emotion Wheel </em></p> <p>List of emotions (Anger, Interest, Amusement, Pride, Joy, Pleasure, Contentment, Love, Admiration, Relief, Compassion, Sadness, Guilt, Regret, Shame, Disappointment, Fear, Disgust, Contempt, Hate)</p> <p>What are the causes of these feelings?</p> <p><strong>Part II - Bug Builder</strong></p> <p>Task: Open a Issue for some automated test tool</p> <p><em>Geneva Emotion Wheel </em></p> <p>List of emotions (Anger, Interest, Amusement, Pride, Joy, Pleasure, Contentment, Love, Admiration, Relief, Compassion, Sadness, Guilt, Regret, Shame, Disappointment, Fear, Disgust, Contempt, Hate)</p> <p>What are the causes of these feelings?</p> <p><strong>Part III - Semi-Structured Experience Interview</strong></p> <p>1. How do you feel about opening a task to the automation team using the manual process?</p> <p>2. What situations do you face when opening a bug manually to the automation team?</p> <p>3. What were your first impressions of Bug Builder?</p> <p>4. Do you feel that Bug Builder meets your needs for reporting automation bugs? Why?</p> <p>5. Which Bug Builder features did you find most useful?</p> <p>6. Which Bug Builder features did you find least useful or unnecessary?</p> <p>7. How intuitive do you find the Bug Builder interface?</p> <p>8. Has Bug Builder helped increase the efficiency of opening bugs? How?</p> <p>9. Have you noticed any improvements in the speed of opening a bug since implementing Bug Builder?</p> <p>10. Have you encountered any technical difficulties when using Bug Builder? If so, what?</p> <p>11. What would you do differently if you had to implement Bug Builder again?</p> <p>12. Have you received sufficient training to use Bug Builder effectively?</p> <p>13. How effective was the team support provided during the Bug Builder deployment?</p> <p>14. What additional support would you like to see?</p> <p>15. Are you satisfied with the Builder? Why?</p> <p>16. Would you recommend the Bug Builder to other testing teams? Why?</p> <p>17. Are there any other tools or features that you think could complement or replace the Bug Builder?</p> <p>18. Is there anything else you would like to share about your experience with the tool?</p> <p>19. Are there any specific improvements you would like to see in the Bug Builder?</p>
Fig 8 in First report of the high altitude cladoceran species Streblocerus serricaudatus (Fischer, 1849) S.LAT from the Western Ghats of India, Tamil Nadu
Fig 8: Antennule with 3 spines at the tip.
Fig 1 in First report of the high altitude cladoceran species Streblocerus serricaudatus (Fischer, 1849) S.LAT from the Western Ghats of India, Tamil Nadu
Fig 1: Posterior part of the body of Streblocerrus serricaudatus showing protuberances.
Fig 5 in First report of the high altitude cladoceran species Streblocerus serricaudatus (Fischer, 1849) S.LAT from the Western Ghats of India, Tamil Nadu
Fig 5: Carapace. Fig 6: Bilobed Post abdomen.
Fig 2 in First report of the high altitude cladoceran species Streblocerus serricaudatus (Fischer, 1849) S.LAT from the Western Ghats of India, Tamil Nadu
Fig 2: Kanyakumari district map showing Pechiparai dam
Figure 1 in First report of morphological and molecular characterization of Moroccan populations of Globodera pallida
Figure 1: Sampled areas and regions infested with GlOBOdeRa pallida in Morocco.
Fig 2&3 in First report of the high altitude cladoceran species Streblocerus serricaudatus (Fischer, 1849) S.LAT from the Western Ghats of India, Tamil Nadu
Fig 2&3: External structure of Streblocerrus serricaudatus.
Fig 6 in First report of the high altitude cladoceran species Streblocerus serricaudatus (Fischer, 1849) S.LAT from the Western Ghats of India, Tamil Nadu
Fig 6: Bilobed Post abdomen.
Fig 5 in First report of the high altitude cladoceran species Streblocerus serricaudatus (Fischer, 1849) S.LAT from the Western Ghats of India, Tamil Nadu
Fig 5: Carapace.
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.