Early-Career Coordinated Distributed Experiments: Empowerment Through Collaboration

Pastor, A.; Hernandez-del Amo, E.; Gimenez-Grau, P.; Fillol, M.; Pereda, O.; Flores, L.; Sanpera-Calbet, I.; Bravo, A. G.; Martin, E. J.; Poblador, S.; Arroita, M.; Rasines-Ladero, R.; Ruiz, C.; del Campo, R.; Abril, M.; Reyes, M.; Casas-Ruiz, J. P.; Fernandez, D.; Castro-Catala, N.; Tornero, I.; Palacin-Lizarbe, C.; Arce, M. I.; Mora-Gomez, J.; Gomez-Gener, L.; Monroy, S.; Freixa, A.; Lupon, A.; Gonzalez-Ferreras, A.-M.; Estevez, E.; Rodriguez-Lozano, P.; Solagaistua, L.; Rodriguez-Castillo, T.; Aristi, I.; Martinez, A.; Catalan, N.

doi:10.1101/704502

Home/Archive/bioRxiv

bioRxiv · 10.1101/704502

Early-Career Coordinated Distributed Experiments: Empowerment Through Collaboration

View original source ↗Export metadata ↓

Abstract

Coordinated distributed experiments (CDEs) enable the study of large-scale ecological patterns in geographically dispersed areas, while simultaneously providing broad academic and personal benefits for the participants. However, the effective involvement of early-career researchers (ECRs) presents major challenges. Here, we analyze the benefits and challenges of the first CDE exclusively led and conducted by ECRs (i.e. ECR-CDE), which sets a baseline for similar CDEs, and we provide recommendations for successful CDE execution. ECR-CDEs achieve most of the outcomes identified in conventional CDEs as well as extensive benefits for the young cohort of researchers, including: (i) receiving scientific credit, (ii) peer-training in new concepts and methods, (iii) developing leadership and communication skills, (iv) promoting a peer network among ECRs, and (v) building on individual engagement and independence. We also discuss the challenges of ECR-CDEs, which are mainly derived from the lack of independence and instability of the participants, and we suggest mechanisms to address them, such as resource re-allocation and communication strategies.

Source connections

Published article ↗

Explore related subjects

scientific communication and education Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Pastor, A., Hernandez-del Amo, E., Gimenez-Grau, P., Fillol, M., Pereda, O., Flores, L., Sanpera-Calbet, I., Bravo, A. G., Martin, E. J., Poblador, S., Arroita, M., Rasines-Ladero, R., Ruiz, C., del Campo, R., Abril, M., Reyes, M., Casas-Ruiz, J. P., Fernandez, D., Castro-Catala, N., Tornero, I., Palacin-Lizarbe, C., Arce, M. I., Mora-Gomez, J., Gomez-Gener, L., Monroy, S., Freixa, A., Lupon, A., Gonzalez-Ferreras, A.-M., Estevez, E., Rodriguez-Lozano, P., Solagaistua, L., Rodriguez-Castillo, T., Aristi, I., Martinez, A., Catalan, N.. 2019-07-23. Early-Career Coordinated Distributed Experiments: Empowerment Through Collaboration. https://doi.org/10.1101/704502

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

bioRxiv2019

PlotTwist - a web app for plotting and annotating time-series data

The results from time-dependent experiments are often used to generate plots that visualize how the data evolves over time. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open source tool was created with R/shiny that does not require coding skills to operate. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in three different ways: (i) lines with unique colors, (ii) small multiples and (iii) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color blind friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that were applied during the time-lapse experiments. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and is available online: https://huygens.science.uva.nl/PlotTwist\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC=\"FIGDIR/small/745612v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (34K):\norg.highwire.dtl.DTLVardef@1bad5e6org.highwire.dtl.DTLVardef@1312023org.highwire.dtl.DTLVardef@350cb5org.highwire.dtl.DTLVardef@d556c6_HPS_FORMAT_FIGEXP M_FIG C_FIG

scientific communication and education↗

bioRxiv2019

The Five-Primer Challenge: An inquiry-based laboratory module for synthetic biology

New technologies in DNA synthesis and assembly give genetic engineers complete freedom in genetic design, where virtually any plasmid DNA sequence can be created efficiently and economically. Learning how to design, construct, and test new DNA sequences is a critical skill for researchers in molecular biology and biotechnology. Here we present a student-centered, inquiry-based module in which students learn how to control bacterial gene expression by appplying various DNA assembly techniques. The central activity in this learning module is termed the Five-Primer Challenge. Each student is allowed to order up to five 60-mer oligonucleotide primers to then modify a GFP expression plasmid with the goal of increasing GFP expression as much as possible. This module was developed and implemented at the 2016 Cold Spring Harbor Laboratory Synthetic Biology Course, and was effective at engaging students in critical thinking and in promoting student learning.

scientific communication and education↗