Elsevier and Norway Agree on New Open-Access Deal | The Scientist Magazine®

“After unsuccessful negotiations between a coalition of Norwegian organizations and the academic publisher Elsevier culminated in cancelled subscriptions earlier this year, the two have successfully established a new nationwide licensing agreement. The deal, which was announced yesterday (April 23), is a pilot program that covers a period of two years, during which articles with corresponding authors from Norway will be published open access in most of Elsevier’s journals….”

Elsevier and Norway Agree on New Open-Access Deal | The Scientist Magazine®

“After unsuccessful negotiations between a coalition of Norwegian organizations and the academic publisher Elsevier culminated in cancelled subscriptions earlier this year, the two have successfully established a new nationwide licensing agreement. The deal, which was announced yesterday (April 23), is a pilot program that covers a period of two years, during which articles with corresponding authors from Norway will be published open access in most of Elsevier’s journals….”

Scientists who share data publicly receive more citations | Science Codex

“A new study finds that papers with data shared in public gene expression archives received increased numbers of citations for at least five years. The large size of the study allowed the researchers to exclude confounding factors that have plagued prior studies of the effect and to spot a trend of increasing dataset reuse over time. The findings will be important in persuading scientists that they can benefit directly from publicly sharing their data.

The study, which adds to growing evidence for an open data citation benefit across different scientific fields, is entitled “Data reuse and the open citation advantage”. It was conducted by Dr. Heather Piwowar of Duke University and Dr. Todd Vision of the University of North Carolina at Chapel Hill, and published today in PeerJ, a peer reviewed open access journal in which all articles are freely available to everyone….”

Scientists who share data publicly receive more citations | Science Codex

“A new study finds that papers with data shared in public gene expression archives received increased numbers of citations for at least five years. The large size of the study allowed the researchers to exclude confounding factors that have plagued prior studies of the effect and to spot a trend of increasing dataset reuse over time. The findings will be important in persuading scientists that they can benefit directly from publicly sharing their data.

The study, which adds to growing evidence for an open data citation benefit across different scientific fields, is entitled “Data reuse and the open citation advantage”. It was conducted by Dr. Heather Piwowar of Duke University and Dr. Todd Vision of the University of North Carolina at Chapel Hill, and published today in PeerJ, a peer reviewed open access journal in which all articles are freely available to everyone….”

Errors in Time-Series Remote Sensing and an Open Access Application for Detecting and Visualizing Spatial Data Outliers Using Google Earth Engine – IEEE Journals & Magazine

“Following here is a case of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) time-series vegetation productivity where we observed several substantial inconsistencies among versions of the same product. The anomalies discovered are not isolated to a particular region, persisting across geographies, and occurring at multiple time intervals. In response to these findings, we developed a simple yet effective open access application for detecting and visualizing remote sensing data outliers using the Google Earth Engine platform. …”

Errors in Time-Series Remote Sensing and an Open Access Application for Detecting and Visualizing Spatial Data Outliers Using Google Earth Engine – IEEE Journals & Magazine

“Following here is a case of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) time-series vegetation productivity where we observed several substantial inconsistencies among versions of the same product. The anomalies discovered are not isolated to a particular region, persisting across geographies, and occurring at multiple time intervals. In response to these findings, we developed a simple yet effective open access application for detecting and visualizing remote sensing data outliers using the Google Earth Engine platform. …”

novel open access web portal for integrating mechanistic and toxicogenomic study results | Toxicological Sciences | Oxford Academic

Abstract:  Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as GSEA, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the two approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for “hands-on” computer programming experience, the selection of one or more analysis methods (e.g. pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogenomics (CTox), an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements “best-practice” methods in computational biology. New study results are compared to over 4,000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.

novel open access web portal for integrating mechanistic and toxicogenomic study results | Toxicological Sciences | Oxford Academic

Abstract:  Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as GSEA, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the two approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for “hands-on” computer programming experience, the selection of one or more analysis methods (e.g. pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogenomics (CTox), an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements “best-practice” methods in computational biology. New study results are compared to over 4,000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.