EnviroSensing Monthly telecons

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Telecons are on the fourth Tuesday of every month at 4:00pm ET. Click on 'join'

Contents

Schedule

  • December 23, 2014, 4:00 PM EST. No telecon - Happy Holidays
  • January 27,2015, 4:00 PM EST. Rick Susfalk, Division of Hydrologic Sciences, Desert Research Institute

Notes and recordings from past telecons

To watch the recordings make sure you allow popups for https://esipfed.webex.com


01/27/2015

Rick Susfalk from the Desert Research Institute presented about Acuity Data Portal. Notes from meeting taken by Fox Peterson, please edit as you see fit: We had in attendance 8 persons.

Acuity Portal System

  • Started in 2006
  • originally VDV data solution
  • improvements to web-interface; sits on top of VDV as Acuity server
  • Acuity is a continuous monitoring of key client-driven data
  • it includes sensors and data logging deployment and maintenance, telemetry, data storage and analysis, automated airing, web portal for data access
  • individualized web presence tailored to client needs
  • not a single tool, but instead integrates commercial, open source, and proprietary hardware and tools
  • customizable project specific descriptions
  • common tools used to provide rapid, cost-effective deployment of individualized portals
  • physical infrastructure is shared amongst smaller clients for cost-saving or it can be segregated for larger clients.
  • access is controlled down to the variable level-- "we can define who gets to see what"-- for example, public can not see some features
  • one view could be "pre-defined graphs" without logging in, but if you want to download the data you must log in at your permissions level

SECURITY


ANALYSIS

  • customized thresholding and data-freshness
  • trending alerts, for example, know if battery will go bad
  • stochastic and numerical modeling
  • scoring incoming data for QA/QC processing

QA/QC tools

  • web-based GUI
  • users and managers can create, edit, and modify alerts online
  • groups can be created so that you can schedule management and alerts
  • also offers localized redundant alerting
  • two-way communication with the campbell data loggers (cr1000)
  • more about getting the data to the data managers for more in-depth QA/QC than about providing that part of the tooling

Data graphing features

  • Pre-determined graphs for basic users
  • Data selector for more advanced users
  • "we don't know what the users want to see so we give them the tools to do it" (good idea!)
  • anything you can change in Excel you can change in their graphs on the website -

Links

Metadata

  • relates your parameters to the network and what other sensors are doing
  • current system is getting more flexible
  • metadata is still largely user responsibility

Flight plan - safety tool

  • field personnel are data
  • users put in the travel time for safety
  • buddy system, alerted right before you return, then calls boss etc. Many levels hierarchy

Demos

  • Portals that are monitoring things
  • ability for data refreshment
  • colors for indication, ex., data would not be gray if there was lots of new data
  • users can change the settings on the data logger
  • scrolling, scaling, plotting, etc. via interaction with the user
  • can save your own graphs

graphs and alerts

  • many parameters
  • you can save!
  • email, sms, phone
  • default settings for users
  • lots of personnel management tools in this in general
  • cross-station "truly alarm or not" if station1 has a value but station2 has a different one, don't alarm sorts of rules
  • lists/user groups appear to be very important with this tool
  • sensor and triggers: customize one or more parameters that you are bringing into your database

real-time updates on loggers

  • ex. 10 minute data, user comes in and makes a change, the information is saved to the database and then is presented to all other users
  • the person will request a change and say what that change is
  • when there are different levels of connectivity ie. analog phone modems, before the data has the chance to work its way back into the system there is a lot of validation being done

example

measurements/metadata

  • extends beyond the vdv, more than 1 .dat file
  • integrate multiple .dat into many tables
  • managed by the data managers at DRI
  • workflow :

logger net --> vdv --> acuity, ok, let's give access to the DM for all these variables, click on it ok now the manager can see it --> generate an excel file with tables for all this metadata --> enter the data into the excel files--> send back to acuity --> injests, runs queries, back to db-- > metadata in bulk, quickly

  • we asked if the system ends before the qa/qc process begins, answer: qa/qc is done at the DRI, near real time QA though

future capability

  • direct the managers to the future data problems
  • manual decision making

Scotty asked about the duration (long and short term) of projects and how affects funding. most is funded by long-term projects; this is why they do the stats and numerical methods in the future

Amber asked about pricing; pricing is by hour to get up, then a price for maintaining the system for the duration of the project 5-10 .dat files, only 8 hours of person time at DRI to make a portal

11/25/2014

Jordan Read presented the SensorQC R package

Recorded Session Play | Download

10/28/2014

Wade Sheldon presented the GCE Data Toolbox – a short summary follows:

  • Community-oriented environmental software package
  • Lightweight, portable, file-based data management system implemented in MATLAB
  • generalized technical analysis framework, useful for automatic processing, and it's a good compromise using either programmed-in or file-based operations
  • Generalized tabular data model
  • Metadata, data, robust API, GUI library, support files, MATLAB databases
  • Benefits and costs: platform independent, sharing both code and data seamlessly across the systems, version independent as far as MATLAB goes, and is now "free and open source" software. There is a growing community of users in LTER.

Toolbox data model


  • Data model is meant to be a self-describing environmental data set-- the metadata is associated with the data, create date and edit date and such are maintained, and its lineage.
  • Quality control criteria- can apply custom function or one already in the toolbox
  • Data arrays, corresponding arrays of qualifier flags -- similar to a relational database table but with more associated metadata

Toolbox function library


  • The software library is referred to as a "toolbox"
  • a growing level of analytical functions, transformations, aggregation tools
  • GUI functions to simplify the usage
  • indexing and search support tools, and data harvest management tools
  • Command line API but there is also a large and growing set of graphical form interfaces and you can start the toolbox without even using the command line

Data management framework


  • Data management cycle - designed to help an LTER site do all of its data management tasks
  • Data and metadata can be imported into the framework and a very mature set of predefined import filters exist: csv, space- and tab-delimited and generic parsers. Also, specialized parsers are available for Sea-Bird CTD, sondes, Campbell, Hobo, Schlumberger, OSIL, etc.
  • Live connections i.e. Data Turbine, ClimDB, SQL DB's, access to the MATLAB data toolbox
  • Can import data from NWIS, NOAA, NCDC, etc.
  • Can set evaluation rules, conditions, evaluations, etc.
  • Automated QC on import but can do interactive analysis and revision
  • All steps are automatically documented, so you can generate an anomalies report by variable and date range which lets you communicate more to the users of the data

Recorded Session Play | Download

9/23/2014

  • Fox Peterson (Andrews LTER) reported on QA/QC methods they are applying to historic climate records (~13 million data points for each of 6 sites).

The challenge was that most automated approaches still produced too many flagged data that needed to be manually checked. Multiple statistical methods were tested based on long-term historical data. The method they selected was to use a moving window of data from the same hour over 30 days and test for 4 standard deviations in that window; E.g., use all data for 1 pm for days 30 - 60 of the year, compute four standard deviations, and set the range for the midpoint day (45) at the 1pm hour to that range.

  • Josh Cole reported on his system, which is in development and he will be able to share scripts with the group.
  • Brief discussion of displaying results using web tools.
  • Great Basin site discussed the variability in their data, which "has no normal"-- how could we perform qa/qc based on statistics and ranges in this case?
  • Discussion of bringing Wade Sheldon to call next time / usefulness of the toolbox for data managers
  • Discussion of using Pandas package- does anyone have experience, can we get them on?
  • Discussion of the trade off between large data stores, computational strength, and power. Good solutions?
  • ESIP email had some student opportunities which may be of interest
  • Overall, it was considered helpful if people were willing to share scripts. Discussion of a GIT repository for the group, or possibly just use the Wiki.

Recorded Session: Play | Download

8/26/2014

Suggestions for future discussion topics

  • Citizen Science contributions to environmental monitoring
  • 'open' sensors - non-commercial sensors made in-house, technology, use, best practices
  • Latest sensor technologies
  • Efficient data processing approaches
  • Online data visualizations
  • New collaborations to develop new algorithms for better data processing
  • Sensor system management tools (communicating field events and associating them with data)

Recorded session: Play | Download

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