2015 ISS R&D Conference – Evolution or Revolution

The 2015 International Space Station Research & Development Conference (ISS R&D) took place recently in Boston, MA from July 7-9.

It was an amazing week of insights and information on the innovations and discoveries taking place on board the ISS, as well as glimpses of the achievements yet to come.

2015 ISS R&D

A highlight of the first day was a conversation with Elon Musk, who mused on his initial commercial forays into space, the state of his transformative company SpaceX, and a view of his vision for the future of space travel, research and exploration.

Core topics discussed at ISS R&D 2015 included everything from biology and human health, to materials development and plant science, to remote sensing and Earth observation, to space travel and human exploration. Here are a few of the top highlights:

  • NASA and its partner agencies have transitioned from assembling an amazingly complex vehicle in space to now utilizing this vehicle for the benefit of humanity.
  • The feat of building and maintaining the International Space Station is often underrated and overlooked, but it’s an incredible achievement, and everyone is encouraged to explore the marvels of what has been, and continues to be, accomplished.
  • We are advancing to a future where space transport will become commonplace, and it is the science, humanitarian, exploration and business opportunities that will be the new focus of ISS utilization.
  • The ISS is an entrepreneur engine, as evidenced in part by the rise of the new space economy. For example, new markets are emerging in the remote sensing domain, with NanoRacks, Teledyne Brown Engineering and Urthecast all making investments in expanding Earth observation from the ISS.
  • The future of the ISS, and its continued operation, is a direct function of the success or failure of what is happening on the ISS right now. The greater the success, the brighter the future.

Throughout the week a question was often asked whether the ISS is evolutionary or revolutionary… and in the end the answer was both!

Interested in learning more about the ISS? Visit the recently launched website  spacestationresearch.com to “explore the new era of science in space for life on Earth”.

Also, save the data for next year’s conference, which is taking place July 12-14, 2016 in San Diego, California. See you there!

“Space is now closer than you think.”

A Look at What’s New in ENVI 5.2

Earlier this month Exelis Visual Information Solutions released ENVI 5.2, the latest version of their popular geospatial analysis software.

ENVI 5.2

ENVI 5.2 includes a number of new image processing tools as well as various updates and improvements to current capabilities. We’ve already downloaded our copy and started working with the new features. Here’s a look at what’s included.

A few of the most exciting new additions to ENVI include the Spatiotemporal Analysis tools, Spectral Indices tool, Full Motion Video player, and improved integration with ArcGIS:

  • Spatiotemporal Analysis. Just like the name sounds, this feature provides users the ability to analyze stacks of imagery through space and time. Most notably, tools are now available to build a raster series, where images are ordered sequentially by time, to reproject images from multiple sensors into a common projection and grid size, and to animate and export videos of these raster series.
  • Spectral Indices. Expanding on the capabilities of the previous Vegetation Index Calculator, the new Spectral Indices tool includes 64 different indices, which in addition to analyzing vegetation can also be used to investigate geology, man-made features, burned areas and water. The tool conveniently selects only those indices that can be calculated for a given input image dependent on its spectral characteristics. So when you launch the tool you’ll only see those indices that can be calculated using your imagery.
  • Full Motion Video. ENVI 5.2 now supports video, allowing users to not just play video, but also convert video files to time-enabled raster series and extract individual video frames for analysis using standard ENVI tools. Supported file formats include Skybox SkySat video, Adobe Flash Video and Shockwave Flash, Animated GIF, Apple Quicktime, Audio Video Interleaved, Google WebM Matroska, Matroska Video, Motion JPEG and JPEG2000, MPEG-1 Part 2, MPEG-2 Transport Stream, MPEG-2 Part 2, MPEG-4 Part 12 and MPEG-4 Part 14.
  • Integration with ArcGIS. Originally introduced in ENVI 5.0, additional functionality has been added for ENVI to seamlessly interact with ArcGIS, including the ability to integrate analysis tools and image output layers in a concurrent session of ArcMap. For those working in both software domains, this helps simplify your geospatial workflows and more closely integrate your raster and vector analyses.

Other noteworthy additions in this ENVI release include:

  • New data types. ENVI 5.2 now provides support to read and display imagery from AlSat-2A, Deimos-1, Gaofen-1, Proba-V S10, Proba-V S1, SkySat-1, WorldView-3, Ziyuan-1-02C and Ziyuan-3A, as well as data formats GRIB-1, GRIB-2, Multi-page TIFF and NetCDF-4.
  • NNDiffuse Pan Sharpening. A new pan sharpening tool based on nearest neighbor diffusion has been added, which is multi-threaded for high-performance image processing.
  • Scatter Plot Tool. The previous scatter plot tool has been updated and modernized, allowing users to dynamically switch bands, calculate spectral statistics, interact with ROIs, and generate density slices of the displayed spectral data.
  • Raster Color Slice. This useful tool has also been updated, particularly from a performance perspective, providing dynamic updates in the image display according to parameter changes made in the tool.

For those interested in implementing ENVI in the cloud, the ENVI 5.2 release also marks the release of ENVI Services Engine 5.2 , which is an enterprise version of ENVI that facilitates on-demand, scalable, web-based image processing applications. As an example, HySpeed Computing is currently developing a prototype implementation of ESE for processing hyperspectral imagery from the HICO sensor on the International Space Station. The HICO Image Processing System will soon be publically available for testing and evaluation by the community. A link to access the system will be provided on our website once it is released.

HICO IPS

To learn about the above features, and many more not listed here, see the video from Exelis VIS and/or read the latest release notes on ENVI 5.2.

We’re excited to put the new tools to work. How about you?

HyPhoon – Announcing Launch of Geospatial Data Sharing Service

HySpeed computing is proud to announce the release of HyPhoon, a community gateway for the access and exchange of datasets, applications and knowledge.

The inaugural dataset offered through HyPhoon is from Heron Reef, Australia, provided courtesy of the Center for Spatial Environmental Research at the University of Queensland.

Heron ReefHeron Reef (32 km^2) is located at the southern end of the Great Barrier Reef and has been a focus of coral reef research since the early 1900s. The reef contains Heron Island, which hosts one of the longest running, most significant, coral reef research stations in the world. One of the first large scale reef mapping projects in the world was developed on Heron Reef in the 1980s. Since the late 1990s the Biophysical Remote Sensing Group at the University of Queensland has developed and tested remote sensing applications on Heron Reef with collaborators from around Australia and the rest of the world.

Data offered for the Heron Reef dataset currently includes:

  • mosaic of 2002 CASI hyperspectral imagery at 1 m spatial resolution
  • field transects from 2002 of substrate cover for 3,586 photos
  • depth measurements from 2007 for 7,462 individual soundings
  • bathymetric map derived from the 2002 CASI imagery
  • habitat map derived from 2007 QuickBird imagery
  • geomorphic zonation derived from 2007 QuickBird imagery

This data is offered using the Creative Commons Attribution license (CC BY 3.0 Unported), which “lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation.”

The data from HyPhoon is available for the community to use in research projects, class assignments, algorithm development, application testing and validation, and in some cases also commercial applications. In other words, in the spirit of encouraging innovation, these datasets are offered as a community resource and open to your creativity.

We welcome your thoughts for new data you would like to see included, and also encourage you to contribute your own data or derived products to showcase on HyPhoon.

To access HyPhoon: http://hyphoon.hyspeedcomputing.com/

HyPhoon

A Look at What’s New in ENVI 5.1

ENVI 5.1(16-Dec-2013) Today Exelis Visual Information Solutions released ENVI 5.1, the latest version of their popular geospatial analysis software.

We’ve already downloaded and installed our copy, so read below if you want to be one of the first to learn about the new features. Or better yet, if you or your organization are current with the ENVI maintenance program, you too can download the new version and start using it yourself today.

Below are a few highlights of the new features in ENVI 5.1:

  • Region of Interest (ROI) Tool. Previously only accessible in ENVI Classic, users can now define and manage ROIs in the new interface. This includes the ability to manually draw ROIs, generate ROIs from band thresholds, grow existing ROIs, and create multi-part ROIs. Additionally, ROIs are now stored as georeferenced features, which means they can be easily ported between images.
  • Seamless Mosaic Workflow. The Georeferenced Mosaicking tool has been replaced with the new Seamless Mosaic Workflow. This tool allows user to create high quality seamless mosaics by combing multiple georeferenced scenes. Included is the ability to create and edit seamlines, perform edge feathering and color correction, and export finished mosaics to ENVI or TIFF formats.  Also included are tutorials and tutorial data to learn the simple and advanced features of this workflow.
  • Spectral Data. Both the Spectral Profile and Spectral Library viewers include improvements for visualizing and analyzing spectral data. The software also includes updated versions of four key spectral libraries: ASTER Spectral Library Version 2, U.S. Geological Survey Digital Spectral Library 06, Johns Hopkins University Spectral Library, and the NASA Jet Propulsion Laboratory Spectral Library.
  • Additional Data Types. ENVI 5.1 can now open generic HDF5 files, which includes data distributed from sensors like NPP VIIRS, SSOT, ResourceSat-2, and HICO. Additional data types and file formats also now supported include ECRG, GeoEye-1 in DigitalGlobe format, Goktuk-2, KOMPSAT-3, NigeriaSat-1 and -2, RASAT, and others.
  • Added Landsat 8 Support. Various improvements have been included for the handling of Landsat 8 data, such as automatically reading the thermal infrared coefficients from the associated metadata, including the Quality and Cirrus Cloud bands in the Data and Layer Managers, correcting reflectance gains and offsets for solar elevation, and updating FLAASH to process Landsat 8 imagery.

These and other welcome improvements continue to expand the capabilities of ENVI, and we’re excited to start working with the new features.

For more on ENVI: http://www.exelisvis.com/

Open-Access Scientific Data – A new option from the Nature Publishing Group

In May 2014 the Nature Publishing Group will be launching a new online publication – Scientific Data – which will focus on publishing citable descriptions of open-access data.

There are many benefits to open-access data sharing, including enhanced collaboration, greater research visibility, and accelerated scientific discovery. However, the logistics of providing efficient data storage and dissemination, and ensuring proper citations for data usage, can be a challenging process if undertaken individually. Fortunately there are a growing number of government sponsored and privately funded data centers now providing these services to the community.

As one of the newest offerings in this domain, Scientific Data is approaching open-access through the publication of Data Descriptors: “peer-reviewed, scientific publications that provide detailed descriptions of experimental and observational datasets.” Data Descriptors are “designed to be complementary to traditional research publications” and can include descriptions of data used in new journal publications, data from previously published research, and standalone data that has its own intrinsic scientific value.

Scientific Data

Scientific Data’s six key principles (source: nature.com)

Because Scientific Data is open-access, there are no fees associated with user access to the Data Descriptors. However, to support and facilitate this open-access, authors must pay an article processing charge for each Descriptor that is published. Authors have the option of publishing their Data using one of three different Creative Commons licenses: Attribution 3.0 Unported (CC BY 3.0), Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0), or Attribution-NonCommercial-Share Alike 3.0 Unported (CC BY-NC-SA 3.0). Each license requires users to properly cite the source of the data, but with varying levels of requirements on how the data can be used and re-shared.

Note that under this model Scientific Data is only publishing the Data Descriptors, and authors must still place the data itself in approved publically available data repositories. This helps ensure data is made readily available to the community without restriction. Approved repositories within the environmental and geosciences currently include the National Climatic Data Center, the NERC Data Centres, and PANGAEA. However, authors can also propose additional data repositories be included in this list.

Scientific Data is now accepting submissions, and offering early adopting authors a discounted article processing charge.

For more info on Scientific Data: http://www.nature.com/scientificdata/

Data Management and Broader Impact – Satisfying the new NSF Merit Review criteria

NSF LogoEarlier this year the National Science Foundation released an updated version of the Merit Review process, which among other items includes modifications to the criteria used to assess Broader Impact. The following explores a few ideas on how data management strategies can be leveraged towards expanding your broader impact.

The fundamental purpose of the Merit Review process is to ensure that proposals are reviewed in a fair and equitable manner. Recently, after more than a decade since the last in-depth review of these criteria, a task force was established in 2010 to evaluate and revise the principles and descriptions of the Merit Review process. A final report was published by the task force in 2012, and the new criteria have been in effect for all NSF proposals submitted since January 2013.

As stated in the Proposal and Award Policies and Procedures Guide, “the Intellectual Merit criterion encompasses the potential to advance knowledge” and “the Broader Impacts criterion encompasses the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.” While previous guidelines required proposals to address intellectual merit and broader impact within the one-page summary preceding the main proposal, the new guidelines are more explicit, requiring proposers to now include individual stand-alone statements on intellectual merit and broader impacts within the Project Summary. Additionally, proposers must also include a specific section within the Project Description that directly addresses the broader impact of the proposed research.

Keeping in mind that proposals also require a supplemental document describing your Data Management Plan, consider the potential benefits and advantages of interconnecting your data management strategy with your objectives for achieving broader impact. For example:

  • Data sharing. Data that is openly shared with the community can be utilized by multiple researchers for a variety of applications and thus have greater impact than just a single project. Data sharing also increases the awareness of and number of publications citing the research that created the data.
  • Class development. Project data that is utilized for class development and classroom exercises expands impact related to student engagement and education. Student involvement can also be extended to incorporate different aspects of data collection and processing tasks.
  • Learning modules. The development of training tools and learning modules based on project data can add even greater dimension to the impact on education, particularly when shared openly with the greater scientific community.
  • Additional projects. Utilizing data across multiple projects, as well as for multiple proposal efforts, increases impact across a greater range of scientific objectives. Exploring alternative uses for data can also spur new research ideas and encourage interdisciplinary project development.

Data can be extremely valuable, so be sure to leverage its full potential when proposing new projects and expanding the impact of your current research. It benefits both you and the community.

This is Part 3 of a discussion series on data management and data sharing related to government funded research. Visit Part 1 and Part 2 to read the earlier installments of this storyline.

For more information on the NSF Merit Review process: http://www.nsf.gov/bfa/dias/policy/merit_review/

That’s Not Real – Algorithm testing and validation using synthetic data

NIST Hyperspectral Image Projector

Example synthetic image of Enrique Reef, Puerto Rico using the NIST Hyperspectral Image Projector (HIP)

An important aspect of developing new algorithms or analysis techniques involves testing and validation. In remote sensing this is typically performed using an image with known characteristics, i.e. field measurements or other expert on-the-ground knowledge. However, obtaining or creating such a dataset can be challenging. As an alternative, many researchers have turned to synthetic data to address specific validation needs.

So what are the challenges behind using “real” data for validation? Let’s consider some of the common questions addressed through remote sensing, such as classifying images into categories describing the scene (e.g. forest, water, land, buildings, etc…) or identifying the presence of particular objects or materials (e.g. oil spill, active fire areas, coastal algae blooms, etc…). To validate these types of analyses, one needs knowledge of how much and where these materials are located in the given scene. While this can sometimes be discerned through experience and familiarity with the study area, in most cases this requires physically visiting the field and collecting measurements or observations of different representative points and areas throughout the scene. The resulting data is extremely useful for testing and validation, and recommended whenever feasible; however, conducting thorough field studies is not always practical, particularly when time and budget is limited.

Here we explore a few options that researchers use for creating synthetic images, from the simple to the complex:

  • A simple approach is to create an image with a grid of known values, or more specifically known spectra, where each cell in the grid represents a different material. Subsequent validation analysis can be used to confirm that a given methodology accurately categorizes each of the known materials. To add greater variability to this approach, different levels of noise can be added to the input spectra used to create the grid cells, or multiple spectra can be used to represent each of the materials. While seemingly simplistic, such grids can be useful for assessing fundamental algorithm performance.
  • The grid concept can be further extended to encompass significantly greater complexity, such as creating an image using a range of feasible parameter combinations. As an example from the field of coral reef remote sensing, a model can be used to simulate an image with various combinations of water depth, water properties, and habitat composition. If water depth is segmented into 10 discrete values, water properties are represented by 3 parameters, each with 5 discrete values, and habitat composition is depicted using just 3 categories (e.g. coral, algae and sand), this results in 3750 unique parameter combinations. Such an image can be used to test the ability of an algorithm to accurately retrieve each of these parameters under a variety of conditions.
  • To add more realism, it is also feasible to utilize a real image as the basis for creating a synthetic image. This becomes particularly important when there is a need to incorporate more realistic spatial and spectral variability in the analysis. From the field of spectral unmixing, for example, an endmember abundance map derived from a real image can be used to create a new image with a different set of endmembers. This maintains the spatial relationships present in the real image, while at the same time allowing flexibility in the spectral composition. The result is a synthetic image that can be used to test endmember extraction, spectral unmixing and other image classification techniques.
  • Another approach based on “real” imagery is the NIST Hyperspectral Image Projector (HIP), which is used to project realistic hyperspectral scenes for testing sensor performance. In other words, the HIP is used to generate and display synthetic images derived from real images. As with the above example, a real image is first decomposed into a set of representative endmember spectra and abundances. The HIP then uses a combination of spectral and spatial engines to project these same spectra and endmembers, thereby replicating the original scene. The intent here is not necessarily to use the synthetic data to validate image processing techniques, but rather to test sensor performance by differentiating environmental effects from sensor effects.

Even though it’s a powerful tool, keep in mind that synthetic data won’t solve all your validation needs. You still need to demonstrate that your algorithm works in the “real world”, so it’s best to also incorporate actual measured data in your analysis.

Big Data and Remote Sensing – Where does all this imagery fit into the picture?

There has been a lot of talk lately about “big data” and how the future of innovation and business success will be dominated by those best able to harness the information embedded in big data. So how does remote sensing play a role in this discussion?

We know remote sensing data is big. For example, the NASA Earth Observing System Data and Information System (EOSDIS), which includes multiple data centers distributed around the U.S., currently has more than 7.5 petabytes of archived imagery. Within the EROS data center alone there are over 3.5 million individual Landsat scenes totaling around 1 petabyte of data. And this is but a subset of all the past and currently operating remote sensing instruments. There are many more, particularly when considering the various international and commercial satellites, not to mention the array of classified military satellites and the many instruments yet to be launched. Remote sensing imagery therefore certainly satisfies the big data definition of size.

But what about information content? A significant aspect of the big data discussion is geared towards developing large-scale analytics to extract information and applying those results towards answering science questions, addressing societal needs, spurring further innovation, and enhancing business development. This is one of the key aspects – and challenges – of big data, i.e., not just improving the capacity to collect data but also developing the software, hardware and algorithms needed to store, analyze and interpret this data.

Remote sensing researchers have long been using remote sensing data to address localized science questions, such as assessing the amount of developed versus undeveloped land in a particular metropolitan area, or quantifying timber resources in a given forested area. Subsequently, as software and hardware capabilities for processing large volumes of imagery became more accessible, and image availability also increased, remote sensing correspondingly expanded to encompass regional and global scales, such as estimating vegetation biomass covering the Earth’s land surfaces, or measuring the sea surface temperatures of our oceans. With today’s processing capacity, this has been extended yet further to include investigations of large-scale dynamic processes, such as assessing global ecosystem shifts resulting from climate change, or improving the modeling of weather patterns and storm events around the world.

Additionally, consider the contribution remote sensing makes to the planning and development of transportation infrastructure in the northern hemisphere, where the opening of new trans-arctic shipping routes and changes to other existing high-latitude shipping routes are being predicted using models that depend on remote sensing data for input and/or validation. And also consider agricultural crop forecasting, which relies heavily on information and observations derived from remote sensing data, and can not only have economic impacts but also be used to indicate potential regions of economic and political instability resulting from insufficient food supplies.

Such examples, and others like them, represent a logical progression as research and applications keep pace with greater data availability and ongoing improvements in processing tools. But the field of remote sensing, and its associated data, is continuing to grow. What else can remote sensing tell us and how else can this immense volume of data be used? Are there relationships yet to be exploited that can be used to indicate consumer behavior and habits in certain markets? Are there geospatial patterns in population expansion that can be used to better predict future development and resource utilization?

There’s a world of imagery out there. What are your ideas on how to use it?

Data Management and You – A broader look at research data requirements

This is Part 2 of a discussion series on data management requirements for government funded research.

As discussed in the previous installment of this series, data management has become an integral requirement of government funded research projects. Not only are there considerations related to the fact that the research was supported using taxpayer funding, and hence the data should be made available to the public, but data sharing also helps expand the impact and influence of your own research.

Part 1 of this series focused on the data management requirements of the National Science Foundation (NSF). In Part 2 below we look at the National Aeronautics and Space Administration (NASA), the Australian Research Council (ARC), and the Research Councils United Kingdom (RCUK).

NASAAs with the NSF proposal process, NASA requires a data-sharing plan to be incorporated as part of any proposal response. Specifically, as described in the NASA Guidebook for Proposers, the “Proposer shall provide a data-sharing plan and shall provide evidence (if any) of any past data-sharing practices.” Unlike NSF, which requires a separate two-page plan, the NASA data-sharing plan must be incorporated within the main body of the proposal as part of the Scientific-Technical-Management section. Additionally, as something important to keep in mind, NASA also specifies that “all data taken through research programs sponsored by NASA are considered public”, “NASA no longer recognizes a ‘proprietary’ period for exclusive use of any new scientific data”, and that “all data collected through any of its funded programs are to be placed in the public domain at the earliest possible time following their validation and calibration.” This means no more holding data in reserve until such time as a researcher has completed their work and published their results. Instead, NASA is taking a strong stand on making its data publically available as soon as possible.

RCUKLooking now to the United Kingdom, the RCUK explicitly defines data sharing as a core aspect of its overall mission and responsibility as a government organization. As part of its Common Principles on Data Policy, RCUK states that “publically funded research data are a public good, produced in the public interest, which should be made openly available with as few restrictions as possible in a timely and responsible manner.” To achieve this objective, the individual Research Councils that comprise the RCUK each incorporate their own specific research requirements that conform to this policy. For example, the Natural Environment Research Council (NERC) specifies in its Grants and Fellowships Handbook that each proposal must include a one-page Outline Data Management Plan. If funded, researchers will then work with the NERC Environmental Data Centres to devise a final Data Management Plan. And at the conclusion of the project, researchers will coordinate with the Data Centres to transfer their data and make it available for others to use.

ARCThe Australian Research Council also encourages data sharing as an important component to funded research projects. While the ARC does not specify the need for data management plans in its proposals, the policies listed in the ARC Funding Rules explicitly encourage “depositing data and any publications arising from a research project in an appropriate subject and/or institutional repository.” Additionally, as part of the final reporting requirements for most ARC awards, the researcher must specify “how data arising from the project have been made publically accessible where appropriate.” It is also common amongst the various funding opportunities to include a discussion in the required Project Description on strategies to communicate research outcomes. While not explicitly stated, data sharing can certainly play an important role in meeting such needs to disseminate and promote research achievements.

Government agencies clearly recognize the importance of data, and are making it a priority in their research and proposal requirements. So don’t forget to include data management as part of your next proposal planning process.

Data Management and You – A look at NSF requirements for data organization and sharing

This is Part 1 of a discussion series on data management requirements for government funded research.

NSF LogoData is powerful. From data comes information, and from information comes knowledge. Data is also a critical component in quantitative analysis and for proving or disproving scientific hypotheses. But what happens to data after it has served its initial purpose? And what are your obligations, and potential benefits, with respect to openly sharing data with other researchers?

Data management and data sharing is viewed with growing importance in today’s research environment, particularly in the eyes of government funding agencies. Not only is data management a requirement for most proposals using public funding, but effective data sharing can also work in your favor in the proposal review process. Consider the difference between two accomplished scientists, both conducting excellent research and publishing results in top journals, but only one of the scientists has made their data openly available, with 1000s of other researchers already accessing the data for further research. Clearly, the scientist who has shared data has created substantial additional impact on the community and facilitated a greater return on investment beyond the initially funded research. Such accomplishments can and should be included in your proposals.

As one example, let’s examine the data management requirements for proposals submitted to the U.S. National Science Foundation. What is immediately obvious when preparing a NSF proposal is the need to incorporate a two-page Data Management Plan as an addendum to your project description. Requirements for the Data Management Plan are outlined in the “Proposal and Award Policies and Procedures Guide” (2013) within both the “Grant Proposal Guide” and the “Award & Administration Guide.” Note that in some cases there are also specific data management requirements for particular NSF Directorates and Divisions, which need to be adhered to when submitting proposals for those programs.

To quote from the Data Management Plan: “Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing.” Accordingly, the proposal will need to describe the “types of data… to be produced in the course of the project”, “the standards to be used for data and metadata format”, “policies for access and sharing”, “policies and provisions for re-use, re-distribution, and the production of derivatives”, and “plans for archiving data… and for preservation of access.” Proposals can not be submitted without such a plan.

As another important consideration, if “any PI or co-PI identified on the project has received NSF funding (including any current funding) in the past five years”, the proposal must include a description of past awards, including a synopsis of data produced from these awards. Specifcally, in addition to a basic summary of past projects, this description should include “evidence of research products and their availability, including, but not limited to: data, publications, samples, physical collections, software, and models, as described in any Data Management Plan.”

Along these same lines, NSF also recently adjusted the requirements for the Biographical Sketch to specify “Products” rather than just “Publications.” Thus, in addition to previous items in this category, such as publications and patents, “Products” now also includes data.

The overall implication is that NSF is interesting in seeing both past success in impacting the community through data sharing and specific plans on how this will be accomplished in future research. Be sure to keep this this in mind when writing your next proposal. And remember… data is powerful.

For more information on NSF proposal guidelines: http://www.nsf.gov/bfa/dias/policy/