The document discusses two case studies of scientific research projects - one tracking marine mammals over 40 years, and the other studying genetic factors in bipolar disorder over 20 years. Both projects grew significantly in size and scope over time. This led to challenges in organizing and managing the large amounts of data collected in a way that was compatible, standardized, and accessible to collaborators. The researchers received training in conducting scientific tasks but not in systematically organizing information on a large scale. The document examines issues that arise when small projects expand and ways to help scientists address data management challenges as projects increase in scale and collaboration.

2. We will see two case studies like marine Mammal science and psychiatric genetics.

8. As we can see from the two cases, there are hardships to go to big science from a small scientific project. The researchers from SPLASH and BP collaborations Are trained for their scientific task, but for organizing Information. If they were trained for organizing Information, it would be a help. In SPLASH, the new system contain three versions of Systems is not made for expanding more. If it wants To expand, it will have some incompatible problems. In BP, even though the numbers of researchers were less than SPLASH, there were problems. They had difficulties in computer programming. For example, they had hards hips to implement EAV with various variables. Furthermore, SAS does not provide ampersand. So, “Total Manic & Depressive Episodes” in paradox Became “total_manic_depressive episodes” in SAS.

9. Style of social interaction in the project SPLASH didn’t “try to force them to do it one way” Jacob Tipton BP project was always very decentralized. Both SPLASH and BP projects have non-dogmatic leaders. This flexible and decentralized form of leadership is common among scientific and creative teams(Mumford, Scott, Gaddis, & Strange, 2002) and is not inherently problematic. Science relies on the freedom of scientists to innovate (Bush, 1945; Gordon, Marquis, & Anderson, 1962), although some recent work suggests that these patterns are chaning in the face of calls for measures of increased accountability and relevance for scientific work (Demeritt, 2000; Harman, 2003). The point is, to what extent data management should require to dictate and to what extent should individual scientists be allowed to ignore or skill issues of compatibility and data availability.

10. Derek de Solla Price (1963) identified some of these issues four decades ago in his work thay helped to develop the field of Scientometrics. More recently, scholars in computer science have addressed issues of scalability (Simmhan, Plale, & Gannon, 2005; Zheng, Venters, & Cornford, 2007). Any number of papers discussing the implementation of Grid enabled projects have identifies scalability as one Of the key issues developers have had to deal with (Pakhira, Fowler, Sastry, & Perring, 2005; Shimojo, Kalia, Nakano, & Vashishta, 2001). Only recently, have researchers begun to pay attention to how small scientific projects negotiate the changes required as they move towards becoming large, collaborative scientific projects (Calson & Anderson, 2006; Walsh & Maloney, 2007). Scientists attempt to sustain these collaborations over time (Bos et al., 2007).