scholarly journals DataUp: A tool to help researchers describe and share tabular data

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 6 ◽  
Author(s):  
Carly Strasser ◽  
John Kunze ◽  
Stephen Abrams ◽  
Patricia Cruse
Keyword(s):  
At Risk ◽  
Data Management ◽  
Management Practices ◽  
Management Tool ◽  
Long Term Storage ◽  
Large Numbers ◽  
Metadata Standards ◽  
Heterogeneous Datasets ◽  
Analysis Workflow ◽  
Survey Results

Scientific datasets have immeasurable value, but they lose their value over time without proper documentation, long-term storage, and easy discovery and access. Across disciplines as diverse as astronomy, demography, archeology, and ecology, large numbers of small heterogeneous datasets (i.e., the long tail of data) are especially at risk unless they are properly documented, saved, and shared. One unifying factor for many of these at-risk datasets is that they reside in spreadsheets.In response to this need, the California Digital Library (CDL) partnered with Microsoft Research Connections and the Gordon and Betty Moore Foundation to create the DataUp data management tool for Microsoft Excel. Many researchers creating these small, heterogeneous datasets use Excel at some point in their data collection and analysis workflow, so we were interested in developing a data management tool that fits easily into those work flows and minimizes the learning curve for researchers.The DataUp project began in August 2011. We first formally assessed the needs of researchers by conducting surveys and interviews of our target research groups: earth, environmental, and ecological scientists. We found that, on average, researchers had very poor data management practices, were not aware of data centers or metadata standards, and did not understand the benefits of data management or sharing. Based on our survey results, we composed a list of desirable components and requirements and solicited feedback from the community to prioritize potential features of the DataUp tool. These requirements were then relayed to the software developers, and DataUp was successfully launched in October 2012.

scholarly journals DataUp: A tool to help researchers describe and share tabular data

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 6 ◽  
Author(s):  
Carly Strasser ◽  
John Kunze ◽  
Stephen Abrams ◽  
Patricia Cruse
Keyword(s):  
At Risk ◽  
Data Management ◽  
Management Practices ◽  
Management Tool ◽  
Long Term Storage ◽  
Large Numbers ◽  
Metadata Standards ◽  
Heterogeneous Datasets ◽  
Analysis Workflow ◽  
Survey Results

Scientific datasets have immeasurable value, but they lose their value over time without proper documentation, long-term storage, and easy discovery and access. Across disciplines as diverse as astronomy, demography, archeology, and ecology, large numbers of small heterogeneous datasets (i.e., the long tail of data) are especially at risk unless they are properly documented, saved, and shared. One unifying factor for many of these at-risk datasets is that they reside in spreadsheets. In response to this need, the California Digital Library (CDL) partnered with Microsoft Research Connections and the Gordon and Betty Moore Foundation to create the DataUp data management tool for Microsoft Excel. Many researchers creating these small, heterogeneous datasets use Excel at some point in their data collection and analysis workflow, so we were interested in developing a data management tool that fits easily into those work flows and minimizes the learning curve for researchers. The DataUp project began in August 2011. We first formally assessed the needs of researchers by conducting surveys and interviews of our target research groups: earth, environmental, and ecological scientists. We found that, on average, researchers had very poor data management practices, were not aware of data centers or metadata standards, and did not understand the benefits of data management or sharing. Based on our survey results, we composed a list of desirable components and requirements and solicited feedback from the community to prioritize potential features of the DataUp tool. These requirements were then relayed to the software developers, and DataUp was successfully launched in October 2012.

scholarly journals Intellectual Capital at Risk: Data Management Practices and Data Loss by Faculty Members at Five American Universities

2015 ◽  
Vol 10 (2) ◽  
pp. 96-109 ◽  
Author(s):  
Jaime Schumacher ◽  
Drew VandeCreek
Keyword(s):  
At Risk ◽  
Data Management ◽  
Management Practices ◽  
Personal Data ◽  
Faculty Members ◽  
Digital Object ◽  
American Universities ◽  
Professional Career ◽  
Work Related ◽  
Digital Objects

A study of 56 professors at five American universities found that a majority had little understanding of principles, well-known in the field of data curation, informing the ongoing administration of digital materials and chose to manage and store work-related data by relying on the use of their own storage devices and cloud accounts. It also found that a majority of them had experienced the loss of at least one work-related digital object that they considered to be important in the course of their professional career. Despite such a rate of loss, a majority of respondents expressed at least a moderate level of confidence that they would be able to make use of their digital objects in 25 years. The data suggest that many faculty members are unaware that their data is at risk. They also indicate a strong correlation between faculty members’ digital object loss and their data management practices. University professors producing digital objects can help themselves by becoming aware that these materials are subject to loss. They can also benefit from awareness and use of better personal data management practices, as well as participation in university-level programmatic digital curation efforts and the availability of more readily accessible, robust infrastructure for the storage of digital materials.

2011 BEEF FORAGE SURVEY RESULTS

EDIS ◽  
2016 ◽  
Vol 2016 (7) ◽  
Author(s):  
Sonja C. Crawford ◽  
Christa L. Kirby ◽  
Tycee Prevatt ◽  
Brent A. Sellers ◽  
Maria L. Silveira ◽  
...  
Keyword(s):  
Program Evaluation ◽  
Organizational Development ◽  
Management Practices ◽  
South Florida ◽  
University Of Florida ◽  
Development Unit ◽  
Survey Results ◽  
County Extension ◽  
Ranch Management ◽  
The University

The University of Florida / IFAS South Florida Beef Forage Program (SFBFP) is composed of county Extension faculty and state specialists.  The members, in conjunction with the UF/IFAS Program Evaluation and Organizational Development unit, created a survey in 1982, which is used to evaluate ranch management practices.  The survey is updated and distributed every 5 years to ranchers in 14 South Florida counties: Charlotte, Collier, DeSoto, Glades, Hardee, Hendry, Highlands, Hillsborough, Lee, Manatee, Martin, Okeechobee, Polk, and Sarasota.  The responses are anonymous.  

Mental Burden in Data Management for Detection of Pregnant Women at Risk of Preeclampsia

2019 ◽  
Author(s):  
Cahya Tri Purnami ◽  
◽  
Suharyo Hadisaputro ◽  
Lutfan Lazuardi ◽  
Syarief Thaufik H ◽  
...  
Keyword(s):  
At Risk ◽  
Data Management ◽  
Pregnant Women ◽  
Risk Of Preeclampsia

scholarly journals Advantages and potential challenges of data management in e-maintenance

2019 ◽  
Vol 25 (3) ◽  
pp. 378-396 ◽  
Author(s):  
Arian Razmi-Farooji ◽  
Hanna Kropsu-Vehkaperä ◽  
Janne Härkönen ◽  
Harri Haapasalo
Keyword(s):  
Data Management ◽  
Design Methodology ◽  
Management Practices ◽  
Future Research ◽  
Conceptual Approach ◽  
Content Type ◽  
Different Types ◽  
Maintenance Systems ◽  
Industry Leader ◽  
Practical Implications

Purpose The purpose of this paper is twofold: first, to understand data management challenges in e-maintenance systems from a holistically viewpoint through summarizing the earlier scattered research in the field, and second, to present a conceptual approach for addressing these challenges in practice. Design/methodology/approach The study is realized as a combination of a literature review and by the means of analyzing the practices on an industry leader in manufacturing and maintenance services. Findings This research provides a general understanding over data management challenges in e-maintenance and summarizes their associated proposed solutions. In addition, this paper lists and exemplifies different types and sources of data which can be collected in e-maintenance, across different organizational levels. Analyzing the data management practices of an e-maintenance industry leader provides a conceptual approach to address identified challenges in practice. Research limitations/implications Since this paper is based on studying the practices of a single company, it might be limited to generalize the results. Future research topics can focus on each of mentioned data management challenges and also validate the applicability of presented model in other companies and industries. Practical implications Understanding the e-maintenance-related challenges helps maintenance managers and other involved stakeholders in e-maintenance systems to better solve the challenges. Originality/value The so-far literature on e-maintenance has been studied with narrow focus to data and data management in e-maintenance appears as one of the less studied topics in the literature. This research paper contributes to e-maintenance by highlighting the deficiencies of the discussion surrounding the perspectives of data management in e-maintenance by studying all common data management challenges and listing different types of data which need to be acquired in e-maintenance systems.

Seasonal Nonstructural Carbohydrate Patterns in Dewberry (Rubus spp.) Roots

Weed Science ◽  
2021 ◽  
pp. 1-23
Author(s):  
Katherine M. Ghantous ◽  
Hilary A. Sandler
Keyword(s):  
Weed Management ◽  
Management Practices ◽  
Soluble Sugars ◽  
Leaf Expansion ◽  
Control Measures ◽  
Growth Stages ◽  
Phenological Stages ◽  
Long Term Storage ◽  
Nonstructural Carbohydrate ◽  
Fruit Maturity

Abstract Applying control measures when carbohydrate levels are low can decrease the likelihood of plant survival, but little is known about the carbohydrate cycles of dewberry (Rubus spp.), a problematic weed group on cranberry farms. Weedy Rubus plants were collected from areas adjacent to production beds on commercial cranberry farms in Massachusetts, two locations per year for two years. For each site and year, four entire plants were collected at five phenological stages: budbreak, full leaf expansion, flowering, fruit maturity, and after onset of dormancy. Root sections were analyzed for total nonstructural carbohydrate (TNC; starch, sucrose, fructose, and glucose). Overall trends for all sites and years showed TNC were lowest at full leaf expansion or flowering; when sampled at dormancy, TNC concentrations were greater than or equal to those measured at budbreak. Starch, a carbohydrate form associated with long-term storage, had low levels at budbreak, leaf expansion and/or flowering with a significant increase at fruit maturity and the onset of dormancy, ending at levels higher than those found at budbreak. The concentration of soluble sugars, carbohydrate forms readily usable by plants, was highest at budbreak compared to the other four phenological samplings. Overall, our findings supported the hypothesis that TNC levels within the roots of weedy Rubus plants can be predicted based on different phenological growth stages in Massachusetts. However, recommendations for timing management practices cannot be based on TNC cycles alone; other factors such as temporal proximity to dormancy may also impact Rubus plants recovery and further research is warranted. Late-season damage should allow less time for plants to replenish carbohydrate reserves (prior to the onset of dormancy), thereby likely enhancing weed management tactics effectiveness over time. Future studies should consider tracking the relationship between environmental conditions, phenological stages, and carbohydrate trends.

scholarly journals Larval cryopreservation as new management tool for threatened clam fisheries

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
P. Heres ◽  
J. Troncoso ◽  
E. Paredes
Keyword(s):  
Shell Growth ◽  
Natural Populations ◽  
Ruditapes Philippinarum ◽  
Management Tool ◽  
Equilibration Time ◽  
Ruditapes Decussatus ◽  
Larval Stages ◽  
Long Term Storage ◽  
Declining Species ◽  
Conservation Of Endangered Species

AbstractCryopreservation is the only reliable method for long-term storage of biological material that guarantees genetic stability. This technique can be extremely useful for the conservation of endangered species and restock natural populations for declining species. Many factors have negatively affected the populations of high economical value shellfish in Spain and, as a result, many are declining or threatened nowadays. This study was focused on early-life stages of Venerupis corrugata, Ruditapes decussatus and Ruditapes philippinarum to develop successful protocols to enhance the conservation effort and sustainable shellfishery resources. Firstly, common cryoprotecting agents (CPAs) were tested to select the suitable permeable CPA attending to toxicity. Cryopreservation success using different combinations of CPA solutions, increasing equilibrium times and larval stages was evaluated attending to survival and shell growth at 2 days post-thawing. Older clam development stages were more tolerant to CPA toxicity, being ethylene-glycol (EG) and Propylene-glycol (PG) the least toxic CPAs. CPA solution containing EG yielded the highest post-thawing survival rate and the increase of equilibration time was not beneficial for clam larvae. Cryopreservation of trochophores yielded around 50% survivorship, whereas over 80% of cryopreserved D-larvae were able to recover after thawing.

Increasing Operational Efficiency in a Radioactive Waste Processing Plant

2009 ◽  
Author(s):  
T. W. Turner ◽  
S. N. Watson
Keyword(s):  
Radioactive Waste ◽  
Solid Waste ◽  
Management Practices ◽  
Operational Efficiency ◽  
Processing Plant ◽  
Waste Processing ◽  
Long Term Storage ◽  
Below Ground ◽  
Pass Through ◽  
Intermediate Level Radioactive Waste

The solid waste plant at Harwell in Oxfordshire, contains a purpose built facility to input, assay, visually inspect and sort remote handled intermediate level radioactive waste (RHILW). The facility includes a suite of remote handling cells, known as the head-end cells (HEC), which waste must pass through in order to be repackaged. Some newly created waste from decommissioning works on site passes through the cells, but the vast majority of waste for processing is historical waste, stored in below ground tube stores. Existing containers are not suitable for long term storage, many are already badly corroded, so the waste must be efficiently processed and repackaged in order to achieve passive safety. The Harwell site is currently being decommissioned and the land is being restored. The site is being progressively delicensed, and redeveloped as a business park, which can only be completed when all the nuclear liabilities have been removed. The recovery and processing of old waste in the solid waste plant is a key project linked to delicensing of a section of the site. Increasing the operational efficiency of the waste processing plant could shorten the time needed to clear the site and has the potential to save money for the Nuclear Decommissioning Authority (NDA). The waste processing facility was constructed in the mid 1990s, and commissioned in 1999. Since operations began, the yearly throughput of the cells has increased significantly every year. To achieve targets set out in the lifetime plan (LTP) for the site, throughput must continue to increase. The operations department has measured the overall equipment effectiveness (OEE) of the process for the last few years, and has used continuous improvement techniques to decrease the average cycle time. Philosophies from operational management practices such as ‘lean’ and ‘kaizen’ have been employed successfully to drive out losses and increase plant efficiency. This paper will describe how the solid waste plant at Harwell has continuously increased the throughput of RHILW, which should lead to significant programme savings.

Volatility Forecast by Volatility Index and Its Use as a Risk Management Tool Under a Value-at-Risk Approach

2018 ◽  
Vol 21 (02) ◽  
pp. 1850010 ◽  
Author(s):  
Yam Wing Siu
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Risk Premium ◽  
Value At Risk ◽  
Realized Volatility ◽  
Management Tool ◽  
Spot Price ◽  
Pricing Options ◽  
Significance Levels ◽  
Risk Approach

This paper examines the predicting power of the volatility indexes of VIX and VHSI on the future volatilities (or called realized volatility, [Formula: see text] of their respective underlying indexes of S&P500 Index, SPX and Hang Seng Index, HSI. It is found that volatilities indexes of VIX and VHSI, on average, are numerically greater than the realized volatilities of SPX and HSI, respectively. Further analysis indicates that realized volatility, if used for pricing options, would, on some occasions, result in greatest losses of 2.21% and 1.91% of the spot price of SPX and HSI, respectively while the greatest profits are 2.56% and 2.93% of the spot price of SPX and HSI, respectively, making it not an ideal benchmark for validating volatility forecasting techniques in relation to option pricing. Hence, a new benchmark (fair volatility, [Formula: see text] that considers the premium of option and the cost of dynamic hedging the position is proposed accordingly. It reveals that, on average, options priced by volatility indexes contain a risk premium demanded by the option sellers. However, the options could, on some occasions, result in greatest losses of 4.85% and 3.60% of the spot price of SPX and HSI, respectively while the greatest profits are 4.60% and 5.49% of the spot price of SPX and HSI, respectively. Nevertheless, it can still be a valuable tool for risk management. [Formula: see text]-values of various significance levels for value-at-risk and conditional value-at-value have been statistically determined for US, Hong Kong, Australia, India, Japan and Korea markets.

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