2016 ESIP Winter Meeting

Join us in furthering the Curation of Virtual Data Collections! Earth Observation Data Collections go back to the prepared thematic CD-ROMs generated by archive centers and science teams in the previous century. Since then, they have evolved to existence on the Internet, but still usually with all the data physically co-located. Virtual Data Collections broaden the types of data resources that can be included to include remote URLs to either data or easy-to-use REST(ish) URLs for data services such as OPeNDAP, w10n, and OGC W*S. Meanwhile, advances in data curation around specific themes are underway in such projects as EONET and Dark Data Curation. Agenda: What the Heck Is a Virtual Data Collection? How Are Virtual Data Collections Created? Cool! What's Next and How Can I Get Involved?

An Earth science Testbed can be a platform for conducting evaluation, verification, validation and integration of data and science technologies, capabilities and services. Many organizations have developed or are developing testbeds. This session will feature a combination of presentations and discussions led by various Earth science testbed developers. The session is designed to inform the ESIP community the current testbed landscape and progress, as well as promote potential collaborations among these testbed activities. Planned Agenda/Presentations: Introduction EarthCube Integration and Test Envrionment (ECITE) - Law/Keiser Earth Science data access and analytics in OGC Testbeds - Percivall NSF XSEDE/Jetstream - Pierce/Fischer Discussion

The objective of this session is to share innovative concepts, emerging solutions, and applications for Big Earth and Space Data for Geoscience. Being able to handle massive amount of data impacts our architectural decisions and approaches. Topics include demonstration, studies, methods, solutions and/or architectural discussion on Common enabling technologies Automated techniques for data analysis Science analysis and visualization Real time decision support Implication of Data Intensive science to education Data management lifecycle functions from data capture through analysis Speakers SciSpark - Chris Mattmann NEXUS - Brian Wilson (Thomas Huang) The Arctic Boreal Vulnerability Experiment and Big Data Analytics for Ecosystem Science and Data Management - Stephen Ambrose Lessons Learned on Optimization Approaches for High-Scalability and High-Resiliency in AWG - Hook Hua

Global Change Information (GCI) is key to informing local and regional decisions on building communities that are resilient to changes in the global climate. Government activities such as Climate Resilience Toolkit (CRT), Climate Data Initiative (CDI) and the Global Change and Information System (GCIS) provide this information to help inform these decisions, but more coordination is needed of agency data and information providers. One of the strategic goals of the US Global Change Research Program (USGCRP) is inform decisions on global change. To accomplish this, the USGCRP is engaging the community of decision makers so that the GCI provided by the government can better serve their needs. This session will focus on the steps necessary to build a solid foundation of GCI that will be used by communities to inform important resilience decisions. Panel Discussion Agenda (Draft): Robert Wolfe - Introduction/Background (5 minutes) Panel Introduction and Short Presentation (5 to 10 min. each) David Meyer, NASA - Climate Data Initiative LuAnn Dahlman, NOAA - Climate Resilience Toolkit Sarah Zerbonne - USGCRP Adaptation Science IWG Jerry Johnston, DOI - GeoPlatform Robert Wolfe, USGCRP - Global Change Information System Discussion (25 minutes or more) - questions from the audience

Metadata is a central piece of data stewardship and preservation. Many systems and tools are available to support an organization's metadata needs. Recent progress on NASA's Common Metadata Repository (CMR) and NOAA's Advanced Tracking and Resource tool for Archive Collections (ATRAC) are examples of systems and tools respectively. This sessions is requesting presentations on these and other metadata systems and tools. Session Agenda (15 minute presentations, 25 minute discussion) Introduction - Anna Milan Metadata Principles - Matthew Mayernik NCEI's ATRAC - Nancy Ritchey GCMD Updates - Tyler Stevens Metadata Management Tool for CMR - Dana Shum Discussion - All

Birds of a feather is free, unscheduled space available if a topic emerges that you'd like to continue a conversation or work collaboratively on a project. If your BoF is open, please add it to the whiteboard schedule at the registration desk. How Do You See Big Data? When you hear "Big Data" what do you visualize? This hands-on exercise will allow us to better understand the differences between everyone's concepts of big data by utilizing drawing exercises similar to those used by Lynda Barry (Wisconsin Institute for Discovery, The Near-sighted Monkey). By making us think differently about big data, we shall better understand what differences we have within our own ESIP community. Not only will this increase engagement within our own community, this will empower us to better communicate big data science concepts to those outside our immediate research community. Follow along using #bigdataviz Bring your own tiara!

From 2011-2103, ESIP partnered with NOAA and the Data Conservancy to produce the current version of the "ESIP Data Management for Scientists Short Course" (ESIP Short Course). The members of the ESIP Data Stewardship Committee (the Committee) were able to create 35 unique modules addressing a variety of topics pertaining to data management guidelines and best practices. The Committee is interested in obtaining further funding and to begin developing the next groups of the training modules. However, the Committee also recognizes that there are many training resources that are being generated in parallel by other organizations. In order to understand the current needs for data management training and to build effective training resources, the Committee would like to assess the current landscape Data Management Training (DMT) resources. This session provides the findings as a result of the initial survey and the comparison of the DMT resources conducted using the ESIP Short Course as the baseline. The session will also review and discuss the following results, which help in contributing to the Committee's roadmap for the Short Course and a clearinghouse project. The introduction and the selection rationale for the DMT resources that were used in the comparison. Summary of the gaps and the overlaps in the training topics when the different DMT resources were compared against the ESIP Short Course Identification of potential collaborators. Collaborative Data Management Clearinghouse proposal

It is a truth universally acknowledged that a community in possession of a good challenge must be in want of some data. Whether it is a city worried about climate vulnerability, a neighborhood interested in reducing health impacts from air pollution, or a region concerned about potential conflict from tans-boundary water issues, the utility of data is obvious - especially to the people who have the data. Nevertheless, it is very challenging to connect that data to application. Communities may not appreciate the relevance of data, might not have the time or energy to deal with its complexities, might be overwhelmed by the choice of tools, or have trouble connecting data to the tools and data they already have. All of these challenges, have aspects of a social and interpersonal challenge: how do people from very different backgrounds and cultures, with different skills, rewards, incentives and vocabulary connect and work effectively together. It would be helpful to have a guidebook for data providers who want to engage with community leaders and be part of solving real-world problems. I'd like to propose a session to build such a guidebook. We would begin with a series of lightning talks: ideally 6 success and 6 cautionary tales, drawn from experience. The talks wouldn't be about the projects, they'd be about what worked and what didn't. A round-robin discussion where anyone could add additional stories or experiences would supplement these examples: the result would be a series of vignettes of what has worked and what hasn't. Breakout groups - one for success and one for caution - would distill these vignettes into a series of do's and don'ts. Together, we would be produce a concise actionable guide, with illustrative examples, for data providers who want to work with communities.

We will discuss current results from two metadata improvement efforts: 1) the NASA Big Earth Data Initiative (BEDI) and 2) the NSF EarthCube Building Block CINERGI project. The BEDI work reviews metadata for NASA Climate Initiative Datasets with the OGC Catalog Services for the Web Recommendation and metadata from USGS ScienceBase with the DataCite recommendation. Then we examine several simple questions about metadata from the ESDIS Common Metadata Repository: how are people and organizations in different roles documented and how are additional attributes documented across the collection. The CINERGI Project is described at http://earthcube.org/group/cinergi.

This session will give a broad overview of the first round of technology assessments coordinated by ESIP for NASA AIST, including stepping through the evaluation process as such: AIST Selects Appropriate Candidates Evaluators from Earth Sciences and Informatics Community Evaluators Collaborate with AIST PIs on Test Plan Evaluators Carry Out Testpaln ESIP Coordinates Communication over Slack, OSF, Email, Telecons Evaluators Submit Final Report Content ESIP Edits Final Report We will also discuss evaluator feedback, lessons learned and next steps.

The Disaster Lifecycle Cluster coordinates efforts among data providers, managers and developers of disaster management systems and tools, and end-user communities within ESIP. In 2015, we developed an ESIP testbed for a collaborative Common Operating Picture in support of disaster management, called GeoCollaborate. The cluster is addressing relevant geospatial datasets and testing data sharing mechanisms with selected disaster management communities to identify traits of trusted data and generate best practices. This session will include demonstrations of collaborative data developed under recent ESIP testbed activities, as well as other systems ESIP members are developing to apply geospatial and remote sensing data products to disasters management. The session will also explore criteria for identifying and producing trusted data products. Speakers Anne Rosinski, California Dept. of Conservation: U. S. Dept. of Homeland Security Information Sharing Best Practices - The California Earthquake Clearinghouse Model Robert Chen, Director, Center for International Earth Science Information Network (CIESIN): Rapid Assessment of Hazard Exposure through SEDAC Hazard Mapping Tools (demo) Dave Jones, CEO, StormCenter Communications: GeoCollaborate Testbed Overview and 2016 Plans Holly Schueler, StormCenter Communications: GeoCollaborate Testbed Demonstration - Accessing and Sharing Data Sets Tom Moran, Executive Director, All Hazards Consortium: Lessons Learned During and Post-disaster to Address Issues in Restoring Power Discussion topics How to gather feedback from testbed constituents Steps to establishing an ESIP Trusted Data stamp of approval

The ESIP Assembly will hold its annual meeting in conjunction with the Winter Meeting. We have the fortunate circumstance that we've had a tremendous amount of activity from our ESIP assembly voting reps. This means that we will need at least 50% of our active members to be present. If you are a voting rep or a proxy, please plan to attend. Call to Order ESIP/FES Structure 2016 Elections2016 ESIP Leadership New ESIP members Type Caucus - Type Representative Elections and Type Positions on Administrative Committees (Constitution & Bylaws, Finance & Appropriations and Partnership) Committee Reports (Move this to State of Federation?) Other BusinessSemantic Technology Committee Proposal Approve the 2016-2020 ESIP Strategic Plan Executive Summary 2016 Summer ESIP - Chapel Hill, NC July 19-22, 2016 Adjourn

We've all been on that telecon, the one punctuated by dead air. What if we could make telecons and cluster sessions and committee meetings the thing you look forward to the most in your work day? How many more people would participate actively and vocally? How much more progress would be achieved? Unfortunately, we don't have he answers to that, but you can help us find those answers. Please join us at the Winter ESIP to brainstorm putting the "fun" in "functional". Or something like that.

When is the last time you were in a fun telecon? The last time you looked forward to that slot in the day and said, "Boy, I can't WAIT for that telecon to start! Hot-diggety!" If your answer is: "Never", or "Before the turn of the century", please help us brainstorm a way to make telecons fun, because we sure do a lot of 'em. And if your answer was "last week", we need to know what your secret is! This will be an informal discussion over lunch, though we may break out to a room if we get up a good head of steam.

We introduce a 3 part course module on SciSpark, our AIST14 funded project for Highly Interactive and Scalable Climate Model Metrics and Analytics. The three part course session introduces a 101, 201, and 301 class for learning how to use Spark for science. SciSpark 201 is a 1.5 hour session teacing two algorithms representative of the motivation for SciSpark - iterative data-reuse algorithms that share information between multiple stages. We will build on SciSpark 101 and Scala for science programming as an entry-course. The first algorithm will be an iterative graph-based algorithm for identifying Mesoscale Convective Complexes in Satellite Infrared data: Whitehall, Kim, et al. "Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets." Earth Science Informatics 8.3 (2015): 663-675. Implementation of Grab Em', Tag Em', Graph Em' (GTG) algorithm in Python. We will demonstrate its implementation in SciSpark and discuss future directions. The second algorithm is a K-means clustering algorithm for identification of Probability Density Functions (PDFs) for Climate Extremes in the North American Regional Climate Change Assessment Program (NARCCAP) data: P. C. Loikith, J. Kim, H. Lee, B. Linter, C. Mattmann, J. J. D. Neelin, D. E. Waliser, L. Mearns, S. McGinnis. Evaluation of Surface Temperature Probability Distribution Functions in the NARCCAP Hindcast Experiment. Journal of Climate, Vol. 28, No. 3, pp. 978-997, February 2015. doi:10.1175/JCLI-D-13-00457.1.

The goals of the Information Quality Cluster (IQC) as expressed in the Strategic Plan are: Bring together people from various disciplines to assess aspects of quality of Earth science data Establish and publish baseline of standards and best practices for data quality for adoption by inter-agency and international data providers Build framework for consistent capture, harmonization, and presentation of data quality for the purposes of climate change studies, Earth science and applications Moving towards these goals, the IQC has been collecting use cases to help identify issues and analyze them to arrive at recommendations for improvements in capturing, describing, enabling discovery and facilitating use of data quality information. The purpose of this session is to focus on analysis of several use cases. A brief introduction will be provided to familiarize new attendees with the IQC, use cases and the analysis process. The number of use cases analyzed will depend on the attendance at the session and the number of breakout groups that can be formed.

Biodiversity includes not only the species, genes, and ecosystems that make up the natural world but also the processes and functions carried out by those species (Noss 1990) and can be used as a measure of ecosystem health and stability (Duffy et al. 2013). Marine biodiversity observation networks serve as a way to integrate existing data related to marine biodiversity and expand upon that data to assess the status and trends of biodiversity in the oceans (Duffy et al. 2013). However, the breadth and variability in biodiversity means that biological data are messy, with data collection varying greatly from one project to another. Therefore, while a central tenet of biodiversity observation networks is coordinating and standardizing legacy data with new data collection (Scholes et al. 2008, Duffy et al. 2013), the reality of making that possible has been a barrier to advancement. USGS through the Ocean Biogeographic Information System-USA are partnering with NOAA, NASA, and BOEM on the Marine Biodiversity Observation Network demonstration projects. OBIS-USA has been seen as one of the stepping stones in making these data publically accessible but suffers from the perception that it's a "first order" data repository. In fact, OBIS-USA takes in a downstream derivative form of the data, standardized to Darwin Core which describes the presence, absence, and abundance of taxa observations. Part of the MBON strategy for data management involves teaming up with the Integrated Ocean Observing System (IOOS) Regional Associations in Alaska and Florida. Those IOOS RAs work closely with a commercial company, Axiom Data Science, to build and manage data management technologies and are moving ahead with an online digital data management system. The other option being pursued in relation to the Channel Islands Marine Sanctuary part of the project is to team up with the Santa Barbara LTER at UCSB. The questions we are asking come partly from our role as a Federal partner on these efforts that is also concerned with the Open Data and Public Access policies of the U.S. Government and partly from our role as a downstream derivative data producer that needs to have solid provenance and traceability back to source data. How can these data be best managed so that they are available for the long term? Is managing the data through a commercial partner's data management system sufficient or appropriate for long-term preservation and availability of federally funded data assets? What is the role of federal data centers like the National Centers for Environmental Information at NOAA or one or more NASA DAACs in these projects? What guidance should federal program managers for the grants/funding opportunities be providing to the project PIs at this stage of projects already in motion? What guidance should we be putting into future funding opportunities to ensure open, public access for federally funded data production? If federal data centers are to take on a role for these and similar data, are they fully equipped to do so? Do they require additional funding from the project or funding programs to successfully take on the additional workload? If federal grant-based projects are collaborative across agencies with multiple groups putting in funding, who gets responsibility for the data? What might we learn from the developing ideas of the NIH Commons1 idea? Could we construct such a framework across earth science and not just within one agency? 1. Part of the idea with the NIH Commons from a funding perspective is to have project proposals include specific data management and distribution line items in the budget that are issued as credits to specific data facilities in the Commons as opposed to funds transferred directly to the successful institution. This means that data management is funded and conducted in the way that the NIH wants it to be done but it is done at the project level where the PI and team are also invested in the outcome.

Drones: New Frontiers of Data Collection and Management There is an exciting emergence/surge of small drones (unmanned aerial systems or UASs) for use as data collection tools in many (and expanding) sectors. But the "whats" "whys" and most crucially "hows" of drone use in scientific communities are still being negotiated. Many institutions are incorporating drones into their data collection toolbox, and this session will explore: What data? Why drones? And most importantly: How are drones being incorporated? Places like the Center for Transformative Environmental Monitoring Programs (CTEMPs) and the University of Vermont's Spatial Analysis Lab (SAL) have set themselves up as "UAS deployment" labs or departments, but many other researchers, universities and institutes are purchasing their own for easier use. This leads to an exciting new frontier of data management and stewardship, as drones are bringing people from many different backgrounds (different fields of research: environmental science, remote sensing, and transportation and different backgrounds altogether: academia, government, industry and hobbyist), into the "big data" fold, often without prior experience or knowledge. Three Specific Topics to Consider: Data Collection - Imagery, but also other instruments and types of dataData Stewardship / Management - Examples: irregular nature of drone data: time, space, mode of collection, formats, metadata model -- plus how this data is archived and shared.Applications - There are many applications, but this session may focus more heavily on the first two topics with the possibility to discuss applications in another session (example: Ag and Energy session) The Session would include 3-4 Speakers. Details and Speakers are currently being finalized. Session Schedule (proposed):Introduction: Drones: A New Frontier in Data Collection, Management and Application -- possibly some Policy/Regulation Updates?3-4 Speakers: Pairing Imagery with GHG data (Barbieri - or in Ag/Energy Session) "UAS" Team / Lab Models: (UVM Spatial Analysis Lab or CTEMPs) Designing Methodologies for Drones in Research (Messinger?) Open source / Software Issues/Discussion (Wyngaard) Data Management (Peterson?) Applications (Conservation Drones? Precision Ag? Use Cases from NASA or NOAA or USGS or the Department of Transportation?) Wrap Up / Discussion Drone Data and How it Connects with ESIP more broadly.

A complex cyberinfrastructure such as EarthCube requires a well thought out information and system architecture in order to identify the principles, patterns, and relationships between the software, services, and data and their ultimate deployment and integration. The "Scalable Community Driven Architecture" project is funded by EarthCube to develop a conceptual architecture that serves as a high level system blueprint for the definition, construction, and deployment of both existing and new components to ensure that they can be unified and integrated into an evolutionary national infrastructure for EarthCube.This session will feature a combination of presentations and discussions led by the project team. The session is designed to inform the ESIP community about this conceptual architecture and the project's progress, as well as to get feedback from the community to help improve the design.

Systems of systems and services have been emerging and growing recent years for facilitating the automatic preparation and feeding of both historic and near-real time Earth Observation customized data and on-demand derived products into Earth science models. To bridge the Earth Observations and Earth Science Models, standards and specifications have also been developed and become mature to be adopted by difference communities. Open geospatial standards/specifications, including the ISO geospatial data and metadata standards and standard-based geospatial web service, workflows, and sensor web technologies are the foundation glues to find, access, analyze, and disseminate data and products. The standard interfaces allow the automatic handshaking between components with workflow designers and underlying workflow execution language. This session invites experts and scientists to discuss the emerging technologies and solutions to enable the interoperations among Earth Observations and Earth Science Models. Talks: Interoperability among EO data and ES models: applications and development (Liping Di) OGC activities and standards for model-sensor interoperability and the Model Web (George Percivall) Needs for Earth observational data from coastal oceanographic modeling perspective (Haosheng Huang) Realize effortlessly feeding FVCOM and CRM with multisource earth observation data: two applications of CyberConnector (Ziheng Sun) Geospatial workflows for reusing standard Web services (Eugene Yu) Refs: Di, L., B. Domenico, X. Wu, H. Huang, Q. Tong, E.G. Yu, and J. Song, 2015. CyberConnector: Bridging the Earth Observations and Earth Science Models. Presented at EarthCube All Hands Meeting, Washington, DC, 27-29 May 2015. URL: http://earthcube.org/sites/default/files/doc-repository/CyberConnectorEa...

The NOAA OneStop Data Discovery and Access Framework Project was initialized in FY15 and is now working rapidly to improve discovery and access to NOAA's data. This session will feature a combination of presentations and discussions led by OneStop Projects teams (for example, the Architecture Team and the Metadata Team), designed to both inform the ESIP community and solicit feedback on progress to date.

This is a meeting for anyone interested in learning about Drupal and how it is being used as a tool for science, to plan for 2016, review outcomes of the past year, talk about upcoming projects, and review the budget. All our welcome to attend.

Birds of a feather is free, unscheduled space available if a topic emerges that you'd like to continue a conversation or work collaboratively on a project. If your BoF is open, please add it to the whiteboard schedule at the registration desk. How Do You See Big Data? When you hear "Big Data" what do you visualize? This hands-on exercise will allow us to better understand the differences between everyone's concepts of big data by utilizing drawing exercises similar to those used by Lynda Barry (Wisconsin Institute for Discovery, The Near-sighted Monkey). By making us think differently about big data, we shall better understand what differences we have within our own ESIP community. Not only will this increase engagement within our own community, this will empower us to better communicate big data science concepts to those outside our immediate research community. Follow along using #bigdataviz Bring your own tiara!

The governance of ontologies, vocabularies, and other shared resources has been coming up across communities (RDA, EarthCube, ESIP, etc) and across domains (semantic web, software sustainability, documentation, and others). Topics for discussion include reporting out related to current community efforts, what are the commonalities we can identify for managing and preserving these research objects, what systems are currently being used for this type of governance, what are some of the discussions or efforts for citation of ontologies, and, finally, who is responsible for managing the systems and the objects they contain and what role does (or should) ESIP play in that management.

NASA's Earth Observing System Data and Information System (EOSDIS) continues its work on a number of different projects, systems, and initiatives. These efforts are aimed at increasing discovery and use of EOSDIS data, improving the quality of the holdings, and lowering the bar to data use and participation in Earth Science Research. This session will introduce new capabilities and offer a forum for discussion on: EOSDIS' Next Generation Application Platform (NGAP) and its support for Cloud Deployment The Common Metadata Repository (CMR) and Unified Metadata Model (UMM) updates New support for Virtual Products, DIF interoperability, and APIs to simplify web application development Approaches to improving metadata quality and authoring through CMR's new Metadata Management Tool Updates and roadmaps for Earthdata, the Earthdata Code Collaborative, and Earthdata Search Updates on the EOSDIS Open Source Governance plan

Response of Tribal nations and Tribal communities to current and emerging climate change challenges requires active participation of stakeholders who have effective access to relevant data, information and analytical tools. The Tribal Lands Collaboratory (TLC), inspired by ESIP's Earth Science Collaboratory and currently under conceptual development, is a joint effort between the American Indian Higher Education Consortium (AIHEC), the Environmental Systems Research Institute (Esri), and the National Ecological Observatory Network (NEON). The vision of the TLC is to create an integrative platform that enables coordination between multiple stakeholders (e.g. Tribal resource managers, Tribal College faculty and students, farmers, ranchers, and other local community members) to collaborate on locally relevant climate change issues. The TLC is intended to facilitate the transformation of data into actionable information that can inform local climate response planning. The TLC will provide the technical mechanisms to access, collect and analyze data from both internal and external sources while also providing the social scaffolds to enable collaboration across Tribal communities and with members of the national climate change research community. The prototype project focuses on phenology, a branch of science focused on relationships between climate and the seasonal timing of biological phenomena. Monitoring changes in the timing and duration of phenological stages in plant and animal co----mmunities on Tribal lands can provide insight to the direct impacts of climate change on culturally and economically significant Tribal resources. The project will leverage existing phenological observation protocols created by the USA-National Phenology Network and NEON to direct data collection efforts and will be tailored to the specific needs and concerns of the community. Phenology observations will be captured and managed within the Collaboratory environment where these data may then be correlated with regional climate data to investigate interactions between large-scale environmental changes and local impacts. Esri's Story Maps is a candidate mechanism for sharing of those findings among Tribal stakeholders.

The FAA projects that more than 1 million recreational drones--Unmanned Aircraft Systems or UAS--will be received as gifts through the holiday season! Many of these have built in cameras, and when drones collect data, they connect perfectly to ESIP. The Education Committee views the widespread and growing availability of recreational drones as an opportunity to encourage STEM learning and leverage ESIP collaborations. Presentations and discussion in the session will provide input for a professional development project for educators (two Webinars and a one-day workshop) and a collection of activity suggestions to encourage youth to consider how they can use recreational drones for science explorations. Beginning with questions such as How high can it go? and How fast can it fly?, we'll compile suggested activities that youth might use for science fair projects. We'll draw inspiration from fields in which drones are now being deployed for science missions. We'll also emphasize activities to help drone flyers understand the value of organizing and archiving their data, and offer applications of that value by encouraging them to access data from ESIP projects. The session will feature a mix of short presentations and discussions. One of our remote presentations will be by Jamey Jacobs, PI of the NSF Cloud-Map project at Oklahoma State University. Education Committee members--Preston Lewis, Daniel Zalles, Becky Reid, and Alan Gould--will also make short presentations. We welcome contributions from all ESIP committees and working groups, and we extend a special invitation to folks who received drones for the holidays!

Participants in the Data Stewardship Committee will report out on their relevant activities over the past six months and will discuss future directions.

Lumped together, the numerous and varied obstacles to interoperability form a monster that--akin to the Minotaur of Greek myth--devours scientists and engineers who enter its labyrinth. This panel will illuminate the beast by featuring speakers who have traversed its maze, avoiding dead-ends, contriving helpful constructs, and laying threads to enable exit. The stories will include tests of skill and cunning, where tools must be applied to distant, seemingly incompatible data types, and where dissimilar samplings must be remapped or transformed to enable new syntheses and integrations. At the heart of the creature are the challenges of using data in ways not foreseen. Panelists also will be asked to comment on interoperability across scales, certainly one of the Minotaur's most impenetrable armaments. While encouraging them to address novel problems and/or solutions, panelists are being sought who (collectively) have expertise that covers the following topics, each of which represents a distinct form of obstacle to interoperability: Mismatched Domains Mismatched Ranges Effects of (Analytic) Transformation Mismatched Concepts Mismatched Structures/Encodings Problems Arising in Software

Data containers are infrastructures that facilitate storage, retrieval, and analysis of data sets. Big data applications in Earth Science require a mix of processing techniques, data sources and storage formats that are supported by different data containers. Some of the most popular data containers used in Earth Science studies are Hadoop, Spark, SciDB, AsterixDB, RasDaMan, and HDF. The goal is to develop an evaluation plan for these infrastructures to assess their suitability for Earth Science data processing needs. We have identified a selection of test cases that are relevant to most data processing exercises in Earth Science applications and we aim to evaluate these systems for optimal performance against each of these test cases. The use cases identified as part of this study are (i) data fetching, (ii) data preparation for multivariate analysis, (iii) data normalization, (iv) distance (kernel) computation, and (v) optimization. Technologies to be discussed: Rasdaman - P. Yang and Q. Huang SciSpark and AstrixDB- C. Mattmann HDF - A. Jelenak and T. Haberman

Throughout the data life cycle, data may no longer meet the needs of the user community or it may no longer be supported by the data provider/producer. Removing data from the repository is one option. Preservation of the software and input to recreate the data is another. After brief presentations, we will break into small groups to brainstorm additional ways to handle end of life data. We will then report back to the whole and discuss potential pros and cons of suggested solutions. Session Agenda (15 min. presentations, 30 min discussion) Introduction - Nancy Ritchey Hibernating Software - Valerie Toner Agile Data Curation - Denise Hills Discussion - All Wrap-up - Sarah Ramdeen

Linked Data describes a method of publishing structured data that is interlinked and open. By using Semantic Web Technologies and unique identifiers, Linked Data can enhance sharing and utility of data while also making it machine understandable. The geosciences have seen initial successes in Linked Data with systems such as the Global Change Information System [1]. Yet, there remain surprisingly few geoscience Linked Data sets. Moreover, Linked Data in general has recently come under scrutiny within the broader Semantic Web community due to difficulties in consuming the data. This session reviews the current state-of-the-art in Linked Data and discusses steps toward building and maintaining a Linked Data cloud for the geosciences. Specifically, we will look at techniques for modeling and publishing Linked Data that can maximize reuse. We will also explore issues surrounding data quality and its implications for end users. And, finally, we will explore ESIP's potential role in serving as a hub for the registration and cataloging of linked geoscience data sets. [1] https://data.globalchange.gov/

The Earth Science Data Analytics (ESDA) Cluster has made great strides in understanding the utilization of data analytics in Earth science, an area virtually untouched in the literature. In achieiving its goal to support advancing science research that increasingly includes very large volumes of heterogeneous data, the ESDA Cluster has defined terms, documented use cases, and loosely identified tools and technologies that faciltate a better understanding of the needs of Earth science research. ESDA Definition: Earth Science Data Analytics is The process of examining large amounts of spatial (3D), temporal, and/or spectral data of a variety of data types to uncover hidden patterns, unknown correlations and other useful information, involving one or more of the following: Data Preparation - Preparing heterogeneous data so that they can 'play' together Data Reduction - Smartly removing data that do not fit research criteria Data Analysis - Applying techniques/methods to derive results This cluster session will discuss and initate the work still to be done, including evaluating use cases, extracting data analytics requirements from use cases (this will be a major part of the discussion), survey exisiting data anlytics tools and techniques, and sharing derived ESDA requirements and found technology gaps with the ESIP group interested in 'Emerging Big Data Technologies for Geoscience'.

This Workshop continues the theme of the joint sessions (Ag&Climate Cluster and Energy & Climate Workgroup) at the ESIP Summer Meeting in Asilomar on CDI (Climate Data Initiative), CRT (Climate Resilience Toolkit), and ongoing work that could form the bases for CRT Case Studies. Workshop agenda: - Introduction to the workshop, logistics, etc. - Introduction to CRT, LuAnn Dahlman - Brief description of work related to energy that forms the basis for a potential CRT Case Study, Paul Stackhouse (LaRC) - Brief description of work related to agriculture that forms the basis for a potential CRT Case Study, Jason Barnett (LaRC) - Two concurrent breakout groups, one for energy and one for agriculture, to be led by Paul and Jason, respectively. The two groups discuss and draft the incipient CRT Case Studies, using the CRT "templates." - The two groups recombine and share results, thoughts, next steps, etc.

Due to advancements in web services like CSW, OpenSearch, CKAN, WFS, WCS, SOS, WMS, OPeNDAP and Esri REST, the environmental data community can now build applications that start with a catalog search, extract web service endpoints from discovered datasets, access data or map products from the endpoints using standard tools, and create products customized for their users. Although different tools are needed for emergency responders, environmental managers, scientists, engineers and software developers, all can benefit from the advances in the standardized infrastructure. In this session we will share success stories, challenges, and lessons-learned building these catalog-driven, standards-based applications.

This session invites four cloud computing leaders to discuss cloud computing from their projects to address relevant topics: Hook Hua: Hybrid Cloud -- Application Amazon spot pricing and management Mike Little: AIST Cloud Christopher Lynnes: EOSDIS and Cloud Computing Brian Wilson: Private Cloud and cluster computing Relevant topics 1. The lastest from cloud offers. 2. Cloud enable Earth Science projects. 3. Pros and Cons of migrating to cloud comupting. 4. Organizational strategies on cloud computing.

There is an exciting emergence of new technologies and tools for data collection and decision making in agriculture, such as the use of unmanned aircraft systems (UASs - drones) for precision monitoring. And not just in yield or performance monitoring, but also in collecting data for climate resilience, adaptation and mitigation in agricultural environments. These new frontiers of data collection also open up questions about data stewardship, and often times bring people into the "big data" fold that have little prior experience. Specific Topic Focus: Data Collection Technologies - Unmanned Aircraft Systems (UASs - drones)Applications / Techniques / Tools - Monitoring Agricultural Activity, Landuse Change and Climate Indicators Session Schedule: Introduction: The Landscape of Emerging Frontiers in Agricultural Data Collection (Lindsay Barbieri will provide Introduction) Brief Overview of "New Frontiers" in Agriculture Unmanned Aircraft Systems in Agriculture Monitoring Deforestation, Agriculture and Landuse - Max Messinger USDA Use of Unmanned Aircraft Systems (UASs) - Ray Hunt, USDA Agricultural Activity and Greenhouse Gas Emissions - Lindsay Barbieri Discussion: "Big Data" Users and Creators in these New Frontiers and exploring the connections with ESIP and Data Science more broadly. Data Examples: Irregular nature of drone data: time, space, mode of collection, formats, metadata model, etc.

The Information Quality Cluster was reactivated during ESIP Federation Meeting in July 2015. The objective of IQ cluster is to bring together people from various disciplines to assess aspects of quality of Earth science data. We will be learning and sharing best practices with a goal to build a framework for consistent capture, harmonization, and presentation of data quality for the purposes of climate change studies, Earth science and applications. The efforts and goals of this cluster are not predefined and are motivated by the participants of the cluster, so new ideas and participants are always welcome. The purpose of the session is to provide an introduction to the cluster's activities and report on the progress made during July through December 2015. Following this, the session will provide a brief turtorial on use case development in preparation for the follow-on session on Use Case Development. Agenda (Draft): Introduction - Ramapriyan - 10* NASA Data Quality Working Group (DQWG) Update - Moroni - 10 DQWG Data System Integration Committee - Downs - 10 NOAA Stewardship Maturity Matrix Update - Peng - 10 Use Case Development Tutorial - Moroni - 40 Discussion/Preparation for follow-on session - 10 *Numbers shown are minutes assigend to each agenda item.

Semantic Technologies, a spectrum of maturing methods and tools, are helping to vastly improve the discovery and dissemination of scientific research. What are the areas where semantic technologies would be beneficial to other clusters within the ESIP community? Are there potential areas of overlap? This session aims to provide a platform for open discussion and brainstorming where cluster participants can share the pulse of their respective community, and discuss potential use-cases (or needs) with participating members of the semantic web community. One of the goals of this session is to align communities/clusters with relevant semantic technologies, methodologies, and ontologies in their domain of interest.

Agenda: https://docs.google.com/spreadsheets/d/1HtyDleCsdWZ0ClI2SlC-EsLN-H1TU7Zx... 7:00 Meeting Opens 7:00 EarthCube updates M.Ramamurthy 7:15 COPDESS Updates B.Hanson/ K. Lehnert 7:30 AGU Data Fair Report B.Hanson/ K. Lehnert 7:45 BREAK 8:00 ESIP Plenary - State of the Federation 9:00 DMM Joint Presentation 10:00 BREAK (note ESIP break is 30 min) 10:15 Filling Vacancies on Exec Com 10:30 CDF-TAC Collaboration Ideas TAC Rep 11:00 New Working Groups-Discussion 11:30 Funding and Sustaining CDF Activities 12:00 Pick up box lunches 12:15 Presentation by CDF Members 13:00 Sustaining Data Repositories 13:45 Other Business/ Closing Remarks 14:00 Adjourn AGU's Data Management Maturity Assessment- Overview (Part of CDF) - 9am Calling all data stakeholders! Come hear an overview of the American Geophysical Union's Data Management Maturity Assessment, from model to pilot facility experiences. Topics will include: - Purpose of the assessment (benefits and techniques) - High level review of the assessment method (planning/scoping, onsite time, final report/executive briefing) - Pilot facility summaries (drivers for participation, expectation, preparation, execution including findings, and next steps) Federal - USGS ScienceBase Academic - BCO-DMO - Lessons learned - Going forward/next steps Notes: https://docs.google.com/document/d/1EpOc9T2VmdPQy__NNbwoAW3Z--sXZsoVd8RG...

Bring your boxed lunch and join us Friday at lunch to learn about the tools available to support your ESIP collaborations like wiki workspaces, list-servs, and WebEx tutorials. Great for new ESIP leaders and Student Fellows.

The Information Quality Cluster's objective is to bring together people from various disciplines to assess aspects of quality of Earth science data. During July through December 2015, the IQC has discussed an approach for developing and collecting use cases. Use cases provide a framework to identify specific needs of users regarding expressing information on data quality, and have proven useful in developing recommendations to meet those needs in the NASA Earth Science Data System environment. The IQC is planning to broaden consideration of use cases to include uses of data provided by other ESIP Federation members as well. This session is a follow-on to the earlier session titled "Information Quality Cluster - Introduction, Reporting and Use Case Tutorial." The purpose of this session is to develop a number of use cases, employing a template developed by NASA's Data Quality Working Group during 2014-2015 and adopted by the IQC with some modifications during July-Nov. 2015. Agenda (draft) Introduction - H. K. Ramapriyan - 5* Identification of use cases - All - 10 Breakout for developing use cases - Bob Downs - 5 Develop use cases in small groups - All - 55 Report Back to full group - one from each group - 15 *Numbers indicate minutes assigned to each agenda item

From enhancing search functionality to encoding domain models and theories, semantic technologies are everywhere. As the technologies, methods, and models become more prevalent, questions arise. This session aims to foster the discussions associated with semantic models (i.e. ontologies), and related technologies, pertaining to governance, efficacy, best practices, lessons learned, etc., under the aegis of the Semantic Web Cluster and wider ESIP community. The overarching goal of this session is to share disparate perspectives and outline areas of need from immediate to long-term, as we work towards a short-term and long-term plan for the community.

An initial planning session with a brief overview of the discoverability of web services, how semantic web technologies are being used in that work, and how "big data" streaming data services are used in Earth Observation systems in the first half and use that baseline to start the conversation about potential cluster activities moving forward. With this background, use the rest of the time in the session as a planning session for the cluster. Absent any other consensus at this point, I would like to keep the discussion around what we can do to promote the discovery of these web services with some thought to pragmatic implementations. In light of that, we'll talk about the earlier work on a general web service description specification and how new use cases and technologies affect that effort.