The Electronic Lab Notebook: Piloting a Research Data Management Tool at Cornell University

The maintenance of laboratory records in the digital age can be a complicated and continuously evolving task, yet clear, consistent documentation is critical for tracking, sharing and reproducing research. One tool that can be used to manage and organize such records is an Electronic Lab Notebook (ELN). The Cornell University Library and Cornell’s Academic Technologies Group are currently engaged in a joint pilot program to determine the feasibility of offering a campus-wide ELN service. The pilot, which began in January and runs through June 2013, involves ELN use in research and classroom labs. Shown here are key features of the product being trialed and an early look at the interest and feedback of researchers using the ELN

Request and Augment Presentation

The Request and Augment steps to curating research data are explained in this presentation

Composition and biomass of phytoplankton assemblages in coastal Antarctic waters: A comparison of chemotaxonomic and microscopic analyses

We describe the distribution of phytoplanktonic community composition and biomass from the Western Antarctic Peninsula coast (between 64° and 68° S) using 2 analytical techniques: microscopy and HPLC of photosynthetic pigments. Phytoplankton biomass was estimated as chlorophyll a (chl a) by HPLC and chemotaxonomic quantification of microalgae biomass was performed by multiple regression analysis of pigment concentrations. For the estimation of chl a: diagnostic pigment ratios, it was found of primary importance to differentiate between phytoplankton assemblages within the study area. Three assemblages were differentiated according to their total standing stock and analyzed independently. Phytoplankton biomass was also estimated as carbon (C) concentration by microscopic analysis of cell abundance and biovolumes. Microscopy and chemotaxonomy give a high level of agreement for phytoplankton characterization, showing an on/offshore gradient, with high diatom and cryptophyte biomass in coastal waters, and a mixed assemblage with low biomass in open waters. This gradient was not observed in total cell abundance, indicating that the biomass gradient is controlled by cell size. Microscopy also showed shifts in diatom species throughout the area. C and chl a biomass estimates for the individual microalgae groups were strongly correlated for cryptophytes, chlorophytes and most diatoms, but did poorly for dinoflagellates, prymnesiophytes and chrysophytes. From this study, we conclude that both microscopy and chemotaxonomy can be used to accurately characterize phytoplankton assemblages, but some limitations are present in both techniques. Based on phytoplankton C concentrations, we estimated an average in situ growth rate of 0.28 d-1. In situ cell C:chl a ratios had high variability (from 40 to 220) and were non-linearly related to sample growth rates. Significant differences were found among average C:chl a ratios of low (1 μg chl a l-1), with values of 112 and 74 μg C μg-1 chl a, respectively. In addition, our results support the hypothesis that C quotas of diatoms and other microalgae do not differ greatly from each other, as previously believed.Facultad de Ciencias Naturales y Muse

Composition and biomass of phytoplankton assemblages in coastal Antarctic waters: A comparison of chemotaxonomic and microscopic analyses

We describe the distribution of phytoplanktonic community composition and biomass from the Western Antarctic Peninsula coast (between 64° and 68° S) using 2 analytical techniques: microscopy and HPLC of photosynthetic pigments. Phytoplankton biomass was estimated as chlorophyll a (chl a) by HPLC and chemotaxonomic quantification of microalgae biomass was performed by multiple regression analysis of pigment concentrations. For the estimation of chl a: diagnostic pigment ratios, it was found of primary importance to differentiate between phytoplankton assemblages within the study area. Three assemblages were differentiated according to their total standing stock and analyzed independently. Phytoplankton biomass was also estimated as carbon (C) concentration by microscopic analysis of cell abundance and biovolumes. Microscopy and chemotaxonomy give a high level of agreement for phytoplankton characterization, showing an on/offshore gradient, with high diatom and cryptophyte biomass in coastal waters, and a mixed assemblage with low biomass in open waters. This gradient was not observed in total cell abundance, indicating that the biomass gradient is controlled by cell size. Microscopy also showed shifts in diatom species throughout the area. C and chl a biomass estimates for the individual microalgae groups were strongly correlated for cryptophytes, chlorophytes and most diatoms, but did poorly for dinoflagellates, prymnesiophytes and chrysophytes. From this study, we conclude that both microscopy and chemotaxonomy can be used to accurately characterize phytoplankton assemblages, but some limitations are present in both techniques. Based on phytoplankton C concentrations, we estimated an average in situ growth rate of 0.28 d-1. In situ cell C:chl a ratios had high variability (from 40 to 220) and were non-linearly related to sample growth rates. Significant differences were found among average C:chl a ratios of low (1 μg chl a l-1), with values of 112 and 74 μg C μg-1 chl a, respectively. In addition, our results support the hypothesis that C quotas of diatoms and other microalgae do not differ greatly from each other, as previously believed.Facultad de Ciencias Naturales y Muse

Convergence: Integrating diverse perspectives to provide a single point of service

Poster presented at Research Data Access and Preservation Summit, 22-24 March 2012, New Orleans, LA.In response to an increased awareness of the data management needs of researchers, Cornell’s Research Data Management Service Group (RDMSG) was created with the goal of making it as simple as possible for researchers to obtain the data management services they require. As more libraries get involved in data management, there are several different service models emerging, with some institutions establishing dedicated staff whose main responsibility it is to work with researchers on research data management and others using existing library staff. An example of another possible model, the RDMSG is a cross-disciplinary virtual group that relies on representatives from various service groups on campus to do the work of consulting with researchers on data management planning. With representatives from Cornell University Libraries (CUL), Cornell Information Technologies (CIT), the Center for Advanced Computing (CAC), and Cornell Institute for Social and Economic Research (CISER), the RDMSG consultant pool includes staff from both business and mission driven departments, as well as having very different backgrounds and areas of expertise. The diverse perspectives of the consultants is both a strength and a challenge for the group. In order to provide consistent high quality consultations, the group developed a set of operating principles to guide all consultants in their interactions with researchers. Developing best practices allowed the group to reach consensus on what kinds of interactions were desirable, and to focus their efforts on providing that level of research data management service. We will discuss our experience working as a cross-disciplinary group, including the advantages and disadvantages both expected and unexpected that we’ve encountered. We will also summarize our activities to date and offer some best practices for providing research data management services

Checklist of DCN CURATE Steps

A checklist and introduction to the Data Curation Network CURATE Steps for curating research data

Putting theory into practice: Lessons from the Data Curation Network

Presentation at the 2018 Digital Library Federation forum in Las Vegas, NV.“Launching the Data curation Network: A cross-institutional staffing model for curating research data” funded 2018-2021 by the Alfred P. Sloan Foundation grant G-2018-10072

How Important Are Data Curation Activities to Researchers? Gaps and Opportunities for Academic Libraries

Introduction: Data curation may be an emerging service for academic libraries, but researchers actively “curate” their data in a number of ways—even if terminology may not always align. Building on past user-needs assessments performed via survey and focus groups, the authors sought direct input from researchers on the importance and utilization of specific data curation activities. Methods: Between October 21, 2016, and November 18, 2016, the study team held focus groups with 91 participants at six different academic institutions to determine which data curation activities were most important to researchers, which activities were currently underway for their data, and how satisfied they were with the results. Results: Researchers are actively engaged in a variety of data curation activities, and while they considered most data curation activities to be highly important, a majority of the sample reported dissatisfaction with the current state of data curation at their institution. Discussion: Our findings demonstrate specific gaps and opportunities for academic libraries to focus their data curation services to more effectively meet researcher needs. Conclusion: Research libraries stand to benefit their users by emphasizing, investing in, and/or heavily promoting the highly valued services that may not currently be in use by many researchers

Extending the Research Data Toolkit: Data Curation Primers

Niche and proprietary data formats used in cutting-edge research and technology have specific curation considerations and challenges. The increased demand for subject liaisons, library archivists, and digital curators to curate this variety of data types created locally at an institution or organization poses difficulties. Subject liaisons possess discipline knowledge and expertise for a given domain or discipline and digital curation experts know how to properly steward data assets generally. Yet, a gap often exists between the expertise available within the organization and local curation needs. While many institutions and organizations have expertise in certain domains and areas, oftentimes the heterogeneous data types received for deposit extend beyond this expertise. Additionally, evolving research methods and new, cutting-edge technology used in research often result in unfamiliar and niche data formats received for deposit. Knowing how to ‘get-started’ in curating these file types and formats can be a particular challenge. To address this need, the data curation community have been developing a new set of tools – data curation primers. These primers are evolving documents that detail a specific subject, disciplinary area or curation task, and that can be used as a reference or jump-start to curating research data. This paper will provide background on the data curation primers and their content detail the process of their development, highlight the data curation primers published to date, emphasize how curators can incorporate these resources into workflows, and show curators how they can get involved and share their own expertise