Software Process Assessment (SPA)

NASA's environment mirrors the changes taking place in the nation at large, i.e. workers are being asked to do more work with fewer resources. For software developers at NASA's Goddard Space Flight Center (GSFC), the effects of this change are that we must continue to produce quality code that is maintainable and reusable, but we must learn to produce it more efficiently and less expensively. To accomplish this goal, the Data Systems Technology Division (DSTD) at GSFC is trying a variety of both proven and state-of-the-art techniques for software development (e.g., object-oriented design, prototyping, designing for reuse, etc.). In order to evaluate the effectiveness of these techniques, the Software Process Assessment (SPA) program was initiated. SPA was begun under the assumption that the effects of different software development processes, techniques, and tools, on the resulting product must be evaluated in an objective manner in order to assess any benefits that may have accrued. SPA involves the collection and analysis of software product and process data. These data include metrics such as effort, code changes, size, complexity, and code readability. This paper describes the SPA data collection and analysis methodology and presents examples of benefits realized thus far by DSTD's software developers and managers

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Development of Safe Methods for Canning Blackeye Peas at Home.

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Indicative Distribution Maps for Ecological Functional Groups - Level 3 of IUCN Global Ecosystem Typology

This dataset includes the original version of the indicative distribution maps and profiles for Ecological Functional Groups - Level 3 of IUCN Global Ecosystem Typology (v2.0). Please refer to Keith et al. (2020). The descriptive profiles provide brief summaries of key ecological traits and processes for each functional group of ecosystems to enable any ecosystem type to be assigned to a group. Maps are indicative of global distribution patterns are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Most maps were prepared using a coarse-scale template (e.g. ecoregions), but some were compiled from higher resolution spatial data where available (see details in profiles). Higher resolution mapping is planned in future publications. We emphasise that spatial representation of Ecosystem Functional Groups does not follow higher-order groupings described in respective ecoregion classifications. Consequently, when Ecosystem Functional Groups are aggregated into functional biomes (Level 2 of the Global Ecosystem Typology), spatial patterns may differ from those of biogeographic biomes. Differences reflect the distinctions between functional and biogeographic interpretations of the term, biome