How to Manage Data to Enhance Their Potential for Synthesis, Preservation, Sharing, and Reuse—A Great Lakes Case Study
Bibliographic record
Abstract
ABSTRACT Proper data management (applying coordinated standards and structures to data collection, maintenance, retrieval, and documentation) is essential for complex projects to ensure data accuracy and accessibility. In this article, we used a recent project evaluating changes in Lake Whitefish (Coregonus clupeaformis) growth, condition, and recruitment in the Great Lakes as a case study to illustrate how thoughtful data management approaches can enhance and improve research. Data management best practices described include dedicating personnel to data curation, setting data standards, building a relational database, managing data updates, checking for and trapping errors, extracting data, documenting data sets, and coordinating with project collaborators. The data management actions taken ultimately resulted in a rich body of scientific publication and a robust database available for future studies. Investing in data management allowed this project to serve as a model for taking the first steps toward a common goal of sharing, documenting, and preserving data that are collected and reported during the scientific research process. RESUMEN en proyectos complejos, un manejo apropiado de datos (aplicación coordinada de estándares y estructuras a recolección, mantenimiento, recuperación y documentación) resulta esencial para asegurar la precisión y accesibilidad de los mismos. En la presente contribución se utiliza un proyecto de evaluación de los cambios en el crecimiento, condición y reclutamiento del coregono en los Grandes Lagos, como caso de estudio para ilustrar cómo un manejo adecuado de datos puede incrementar y mejorar la investigación. Las mejores prácticas en cuanto a manejo de datos incluyen: dedicar personal a la curación de datos, fijar estándares en los datos, construcción de una base de datos relacional, manejo de actualización de datos, revisión y filtro de errores en los datos, extracción de datos, documentación de bases de datos y coordinación con colaboradores del proyecto. Las acciones de manejo de datos que se tomaron resultaron en la producción de un cuerpo importante de publicaciones y en una base de datos robusta, disponible para investigaciones futuras. Los recursos invertidos en el manejo de datos permitieron que este proyecto sirviera de modelo para tomar los primeros pasos hacia el objetivo común de compartir, documentar y preservar datos que son recolectados y reportados durante el proceso de una investigación científica.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".