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1. IDTReeS 2020 Competition Data

   https://doi.org/10.5281/zenodo.3700196  

   Graves, Sarah; Marconi, Sergio (January 2020, Zenodo)

   {"Abstract":["Data provided by the Integrating Data science with Trees and Remote Sensing (IDTReeS) research group for use in the IDTReeS Competition.<\/p>\n\nGeospatial and tabular data to be used in two data science tasks focused on using remote sensing data to quantify the locations, sizes and species identities of millions of trees and on determining how these methods generalize to other forests.<\/p>\n\nVector data are the geographic extents of Individual Tree Crown boundaries that have been identified by researchers in the IDTReeS group. The data were generated primarily by Sarah Graves, Sergio Marconi, and Benjamin Weinstein, with support from Stephanie Bohlman, Ethan White, and members of the IDTReeS group.<\/p>\n\nRemote Sensing and Field data were generated by the National Ecological Observatory Network (NEON, Copyright © 2017 Battelle). Data were selected, downloaded, and packaged by Sergio Marconi. The most recent available data of the following products are provided:<\/p>\n\nNational Ecological Observatory Network. 2020. Data Product DP1.30010.001, High-resolution orthorectified camera imagery. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.<\/p>\n\nNational Ecological Observatory Network. 2020. Data Product DP1.30003.001, Discrete return LiDAR point cloud. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.<\/p>\n\nNational Ecological Observatory Network. 2020. Data Product DP1.10098.001, Woody plant vegetation structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.<\/p>\n\nNational Ecological Observatory Network. 2020. Data Product DP3.30015.001, Ecosystem structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020.<\/p>\n\nNEON has the following data policy:<\/p>\n\n\u2018The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program.\u2019<\/p>\n\nTHE NEON DATA PRODUCTS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE NEON DATA PRODUCTS BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE NEON DATA PRODUCTS.<\/p>"],"Other":["This data is supported by the National Science Foundation through grant 1926542 and by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through grant GBMF4563 to E.P. White, and the NSF Dimension of Biodiversity program grant (DEB-1442280) and USDA/NIFA McIntire-Stennis program (FLA-FOR-005470)."]} 

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2. Indicators of Participation: A Critical Review of Publicly-available STEM Data Sources

   Pee, C.M.; Lee, W.C.; London, J.S.; Gew, G.; Holloman, T.K.; Hawkins Ash, C.D.; Watford, B.A. (January 2019, The Collaborative Network for Engineering and Computing Diversity)

   Several national reports convey the need for better data on the participation of underrepresented groups in engineering. The purpose of this paper is to 1) catalogue data sources that collect STEM-related information at a national level, and 2) critique their usefulness as it relates to informing efforts aimed at broadening participation of underrepresented racial/ethnic groups in engineering. To this end, we identified and reviewed multiple STEM-related data sources published by Child Trends, American Society of Engineering Education, National Center for Education Statistics, and the National Science Foundation. A critical look across these data sources revealed important themes related to reporting practices. While data at the compulsory education level related to preparedness via math and science performance indicators, data focused on higher education and workforce segments related to participation via overall numbers (e.g., degrees award). Data on gender and race intersections were largely missing. The implications of this study highlight the ways that publicly available data sources can be improved through more thorough, systematic collection, publication, and disaggregation of data. 

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3. The Personification of Big Data

   https://doi.org/10.1017/dsi.2019.409  

   Stevenson, Phillip Douglas; Mattson, Christopher Andrew (July 2019, Proceedings of the Design Society: International Conference on Engineering Design)

   Abstract Organizations all over the world, both national and international, gather demographic data so that the progress of nations and peoples can be tracked. This data is often made available to the public in the form of aggregated national level data or individual responses (microdata). Product designers likewise conduct surveys to better understand their customer and create personas. Personas are archetypes of the individuals who will use, maintain, sell or otherwise be affected by the products created by designers. Personas help designers better understand the person the product is designed for. Unfortunately, the process of collecting customer information and creating personas is often a slow and expensive process. In this paper, we introduce a new method of creating personas, leveraging publicly available databanks of both aggregated national level and information on individuals in the population. A computational persona generator is introduced that creates a population of personas that mirrors a real population in terms of size and statistics. Realistic individual personas are filtered from this population for use in product development. 

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4. AAU APLU Guide to Accelerate Public Access to Research Data

   https://doi.org/10.31219/osf.io/tjybn  

   Smith, Tobin L.; Redd, Kacy; Nusser, Sarah; Samors, Robert; and Miller, Emily R. (April 2021, AAU APLU)

   The Association of American Universities (AAU) and the Association of Public and Land-grant Universities (APLU) have collaborated and led national discussions to improve public access to data resulting from federally funded research. As part of the NSF-funded (NSF # 1939279) Accelerating Public Access to Research Data Initiative, AAU and APLU convened representatives from the university teams at an Acceleration Conference in 2020 and facilitated two national Summits to help universities create robust systems for ensuring effective public access to high-quality research data and develop the current Guide. The Guide has been informed by 261 campus representatives from 111 institutions, representatives from several federal agencies, and other key stakeholders. The Guide is designed to help institutions develop and promote systems to support sharing of research data. It provides advice concerning actions that can be taken to improve access to research data on campuses. It also contains information about the infrastructure and support that may be required to facilitate data access, and it offers specific examples of how various institutions are approaching challenges to sharing research data and results. 

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5. Digital Forensics in the Next Five Years

   https://doi.org/10.1145/3230833.3232813  

   Luciano, Laoise; Baggili, Ibrahim; Topor, Mateusz; Casey, Peter; Breitinger, Frank (January 2018, Proceedings of the 13th International Conference on Availability, Reliability and Security - ARES 2018)

   Cyber forensics has encountered major obstacles over the last decade and is at a crossroads. This paper presents data that was obtained during the National Workshop on Redefining Cyber Forensics (NWRCF) on May 23-24, 2017 supported by the National Science Foundation and organized by the University of New Haven. Qualitative and quantitative data were analyzed from twenty-four cyber forensics expert panel members. This work identified important themes that need to be addressed by the community, focusing on (1) where the domain currently is; (2) where it needs to go and; (3) steps needed to improve it. Furthermore, based on the results, we articulate (1) the biggest anticipated challenges the domain will face in the next five years; (2) the most important cyber forensics research opportunities in the next five years and; (3) the most important job-ready skills that need to be addressed by higher education curricula over the next five years. Lastly, we present the key issues and recommendations deliberated by the expert panel. Overall results indicated that a more active and coherent group needs to be formed in the cyber forensics community, with opportunities for continuous reassessment and improvement processes in place. 

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