An official website of the United States government
Addressing the challenges of sustainable and equitable city management in the 21st century requires innovative solutions and integration from a range of dedicated actors. In order to form and fortify partnerships of multi-sectoral collaboration, expand effective governance, and build collective resiliency it is important to understand the network of existing stewardship organizations. The term ‘stewardship’ encompasses a spectrum of local agents dedicated to the evolving process of community care and restoration. Groups involved in stewardship across Baltimore are catalysts of change through a variety of conservation, management, monitoring, transformation, education, and advocacy activities for the local environment – many with common goals of joint resource management, distributive justice, and community power sharing. The “environment” here is intentionally broadly defined as land, air, water, energy and more. The Stewardship Mapping and Assessment Project (STEW-MAP) is a method of data collection and visualization that tracks the characteristics of organizations and their financial and informational flows across sectors and geographic boundaries. The survey includes questions about three facets of environmental stewardship groups: 1) organizational characteristics, 2) collaboration networks, and 3) stewardship “turfs” where each organization works. The data have been analyzed alongside landcover and demographic data and used in multi-city studies incorporating similar datasets across major urban areas of the U.S. Additional information about the growing network of cities conducting stewmap can be found here: https://www.nrs.fs.usda.gov/STEW-MAP/ Romolini, Michele; Grove, J. Morgan; Locke, Dexter H. 2013. Assessing and comparing relationships between urban environmental stewardship networks and land cover in Baltimore and Seattle. Landscape and Urban Planning. 120: 190-207. https://www.fs.usda.gov/research/treesearch/44985 Johnson, M., D. H. Locke, E. Svendsen, L. Campbell, L. M. Westphal, M. Romolini, and J. Grove. 2019. Context matters: influence of organizational, environmental, and social factors on civic environmental stewardship group intensity. Ecology and Society 24(4): 1. https://doi.org/10.5751/ES-10924-240401 Ponte, S. 2023. Social-ecological processes and dynamics of urban forests as green stormwater infrastructure in Maryland, USA. Doctoral dissertation, University of Maryland, College Park, MD.
more »
« less
This content will become publicly available on December 1, 2025
The Incremental Growth of Data Infrastructure in Ecology (1980–2020)
After decades of growth, a research community's network information system and data repository were transformed to become a national data management office and a major element of data infrastructure for ecology and the environmental sciences. Developing functional data infrastructures is key to the support of ongoing Open Science and Open Data efforts. This example of data infrastructure growth contrasts with the top‐down development typical of many digital initiatives. The trajectory of this network information system evolved within a collaborative, long‐term ecological research community. This particular community is funded to conduct ecological research while collective data management is also carried out across its geographically dispersed study sites. From this longitudinal ethnography, we describe an Incremental Growth Model that includes a sequence of six relatively stable phases where each phase is initiated by a rapid response to a major pivotal event. Exploring these phases and the roles of data workers provides insight into major characteristics of digital growth. Further, a transformation in assumptions about data management is reported for each phase. Investigating the growth of a community information system over four decades as it becomes data infrastructure reveals details of its social, technical, and institutional dynamics. In addition to addressing how digital data infrastructure characteristics change, this study also considers when the growth of data infrastructure begins.
more »
« less
- Award ID(s):
- 2025755
- PAR ID:
- 10644571
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Ecology and Evolution
- Volume:
- 14
- Issue:
- 12
- ISSN:
- 2045-7758
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This study analyzes and compares how the digital semantic infrastructure of U.S. based digital news varies according to certain characteristics of the media outlet, including the community it serves, the content management system (CMS) it uses, and its institutional affiliation (or lack thereof). Through a multi-stage analysis of the actual markup found on news outlets’ online text articles, we reveal how multiple factors may be limiting the discoverability and reach of online media organizations focused on serving specific communities. Conceptually, we identify markup and metadata as aspects of the semantic infrastructure underpinning platforms’ mechanisms of distributing online news. Given the significant role that these platforms play in shaping the broader visibility of news content, we further contend that this markup therefore constitutes a kind of infrastructure of visibility by which news sources and voices are rendered accessible—or, conversely—invisible in the wider platform economy of journalism. We accomplish our analysis by first identifying key forms of digital markup whose structured data is designed to make online news articles more readily discoverable by search engines and social media platforms. We then analyze 2,226 digital news stories gathered from the main pages of 742 national, local, Black, and other identity-based news organizations in mid-2021, and analyze each for the presence of specific tags reflecting the Schema.org, OpenGraph, and Twitter metadata structures. We then evaluate the relationship between audience focus and the robustness of this digital semantic infrastructure. While we find only a weak relationship between the markup and the community served, additional analysis revealed a much stronger association between these metadata tags and content management system (CMS), in which 80% of the attributes appearing on an article were the same for a given CMS, regardless of publisher, market, or audience focus. Based on this finding, we identify the organizational characteristics that may influence the specific CMS used for digital publishing, and, therefore, the robustness of the digital semantic infrastructure deployed by the organization. Finally, we reflect on the potential implications of the highly disparate tag use we observe, particularly with respect to the broader visibility of online news designed to serve particular US communities.more » « less
-
null; null; null; null; null; null (Ed.)The National Ecological Observatory Network (NEON) is a continental-scale observatory with sites across the US collecting standardized ecological observations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision data in near-real time to the NEON Data Portal and to High Performance Computing (HPC) systems, running ensembles of Earth system models (ESMs) that assimilate the data. For the first time gridded EC data products and response functions promise to offset pervasive observational biases through evaluating, benchmarking, optimizing parameters, and training new ma- chine learning parameterizations within ESMs all at the same model-grid scale. Leveraging open-source software for EC data analysis, we are al- ready building software infrastructure for integration of near-real time data streams into the International Land Model Benchmarking (ILAMB) package for use by the wider research community. We will present a perspective on the design and integration of end-to-end infrastructure for data acquisition, edge computing, HPC simulation, analysis, and validation, where Artificial Intelligence (AI) approaches are used throughout the distributed workflow to improve accuracy and computational performance.more » « less
-
Design is a human activity that encompasses a broad array of tasks. In engineering design, individual efforts can be aggregated into teams to maximize collective progress. Effective teamwork, however, requires extensive management, organization and communication. Furthermore, modern challenges encompass complicated multi-disciplinary problems with faster schedules, fewer resources, and greater demands. Design, as a process, can be dissected into characteristic phases. Within each phase, design solutions are gradually developed. Technological tools have prioritized the structured analyses of the detailed and final design phases and have proven to be powerful multipliers for effective design efforts. It has long been the case, however, that major commitments of intangible resources are made as a result of efforts in the less emphasized earlier phases. These commitments and lack of modern toolsets for requirement development and conceptual design activities materialize as major sources of design pitfalls, both in industry and on student design projects. This paper presents a digital Ecosystem for Engineering Design Learning as a comprehensive, yet flexible, framework for capstone design teams. The digital Ecosystem has been developed as a feasible technology to bolster student information management, teamwork, communication, and proficiency in fundamental design principles, and as a technology capable of alleviating rework and process-related productivity interruptions. Its primary innovation, for capstone applications, is the ability to assess design work automatically against the design process, as well as against ABET compliant learning objectives, and provide prompt advisories in case of design oversights. The digital Ecosystem is compared to tools for project management, team communication, and requirement management.more » « less
-
Abstract Macrosystem‐scale research is supported by many ecological networks of people, infrastructure, and data. However, no network is sufficient to address all macrosystems ecology research questions, and there is much to be gained by conducting research and sharing resources across multiple networks. Unfortunately, conducting macrosystem research across networks is challenging due to the diversity of expertise and skills required, as well as issues related to data discoverability, veracity, and interoperability. The ecological and environmental science community could substantially benefit from networking existing networks to leverage past research investments and spur new collaborations. Here, we describe the need for a “network of networks” (NoN) approach to macrosystems ecological research and articulate both the challenges and potential benefits associated with such an effort. We describe the challenges brought by rapid increases in the volume, velocity, and variety of “big data” ecology and highlight how a NoN could build on the successes and creativity within component networks, while also recognizing and improving upon past failures. We argue that a NoN approach requires careful planning to ensure that it is accessible and inclusive, incorporates multimodal communications and ways to interact, supports the creation, testing, and promulgation of community standards, and ensures individuals and groups receive appropriate credit for their contributions. Additionally, a NoN must recognize important trade‐offs in network architecture, including how the degree of centralization of people, infrastructure, and data influence network scalability and creativity. If implemented carefully and thoughtfully, a NoN has the potential to substantially advance our understanding of ecological processes, characteristics, and trajectories across broad spatial and temporal scales in an efficient, inclusive, and equitable manner.more » « less
