Search

This report summarizes the group discussions and priorities on microplastics data management that emerged from a virtual workshop hosted by the University of Waterloo���s Water Institute, the Federated Research Data Repository (FRDR), and the Gordon Foundation., The workshop is funded by Compute Ontario. Additional support was provided by the NSERC/ECCC Alliance Grants - Plastics science for a cleaner future program, the Global Water Futures Program, University of Waterloo Library, Federated Research Data Repository and the Gordon Foundation

The acceleration of climate change and its impact highlight the need for long-term reliable climate data at high spatiotemporal resolution to answer key science questions in cold regions hydrology. Prior to the digital age, climate records were archived on paper. For example, from the 1950s to the 1990s, solar radiation data from recording stations worldwide were published in booklets by the former Union of Soviet Socialist Republics (USSR) Hydrometeorological Service. As a result, the data are not easily accessible by most researchers. The overarching aim of this research is to develop techniques to convert paper-based climate records into a machine-readable format to support environmental research in cold regions. This study compares the performance of a proprietary optical character recognition (OCR) service with an open-source OCR tool for digitizing hydrometeorological data. We built a digitization pipeline combining different image preprocessing techniques, semantic segmentation, and an open-source OCR engine for extracting data and metadata recorded in the scanned documents. Each page contains blocks of text with station names and tables containing the climate data. The process begins with image preprocessing to reduce noise and to improve quality before the page content is segmented to detect tables and finally run through an OCR engine for text extraction. We outline the digitization process and report on initial results, including different segmentation approaches, preprocessing image algorithms, and OCR techniques to ensure accurate extraction and organization of relevant metadata from thousands of scanned climate records. We evaluated the performance of Tesseract OCR and ABBYY FineReader on text extraction. We find that although ABBY FineReader has better accuracy on the sample data, our custom extraction pipeline using Tesseract is efficient and scalable because it is flexible and allows for more customization.

Le présent documentaidera les chercheurs canadiens à choisir un dépôt quidonnera un accès immédiat et à long terme à leurs données sur la COVID‑19.

Cette directive a pour but d’aider les chercheurs à minimiser les risques de divulgation quand ils partagent des données recueillies auprès de participants humains.

Le guide repose sur les recommandations duCOVID-19 Working Groupde laResearch Data Alliance(RDA):Recommendations and Guidelines for Data Sharing.Ces recommandations ont été conçues pour aider les chercheurs à se doter de bonnes pratiques pour le partage de données dans leur discipline età maximiser l’impact de leur travail.Ce guide s'est inspiré en partiepar lestravaux précédentsdeTayler etRipp (2020)FAQ: Partage et dépôt de données en appui à une intervention rapide face à la COVID-19.

Cette guide aide les chercheurs à préparer leur documentation pour les dépôts.

Cet arbre décisionnel a été conçu pour aider les chercheurs canadiens à repérer les situations où les données de participants doivent être dépersonnalisées ou rendues anonymes avant d’être consignées dans un dépôt. Il repose considérablement sur l’Énoncé politique des trois conseils: Éthique de la recherche avec des êtres humains — EPTC 2 (2018). Cet énoncé canadien porte sur le consentement et l’utilisation secondaire des données dans la recherche.

The guidance below is intended to help Canadian researchers minimize disclosure risk when sharing data collected from human participants. If you use any of the following techniques to anonymize your data, please include this information in yourdocumentation and README file.For transparency, it should be clear how the dataset was modified to protect study participants.&nbsp

The speed with which COVID-19 research is being produced and shared only increases the need for good documentation and description. It may be necessary to make available the results of preliminary trials or unprocessed datasets, which may be difficult for fellow researchers to interpret without contextual information. This guide is a reminder of how to frame research so that it remains usable and understandable by others.

During a pandemic, rapid access to the latest data informs public health response and helps save lives. Preserving these data for future use can also help to clarify the long-term economic, political, and cultural impacts of public health and policy decisions, and may inform the response to future pandemics. Use this document to help select a repository that will provide immediate and long-term access to COVID-19 data.

"Can I Share My Data?" is designed to bea quick andeasy-to-usedecision tree toalert Canadian researchersto situations where research dataderived from human participants either may not be shared publicly or may require some modification before sharing. It relies heavily on the CanadianTri-Council Policy Statement: Ethical Conduct for Research Involving Humans - TCPS 2 (2018)which addresses consent and secondary use of information for research purposes.

Chemists in academic institutions utilize a variety of resources and strategies to remain current and to track scholarly information, patents, and news. To explore how chemists in academic institut...