Team Members

  • Team Lead & Data Visualization Expert - Khyati Majmudar
  • Team Member & Developer - Ninad Majmudar

Objective

To propose a Robust Data Enrichment Process for GBIF mediated data using Software based Analytics Framework. The proposed Process includes the Analytics & Data Gap Visualization Framework - GBIF Data Discovery, to analyse all sorts of data gaps, for any region, for any period and at every taxonomic hierarchy level.

The framework consists of built-in Data Import scripts, Intelligent Algorithms to calculate Ignorance Score and Inventory Completeness and has Rich Visualizations to analyse trends and gaps through Interactive Dashboards and Associative Filtering of Data. It can also be easily extended to incorporate other complex algorithms to analyse gaps, create new dashboards with in-built dimensions & features as well as make changes to data-import level logic, even by data users/publishers themselves.

Targeted Audience

  • Data holders: Understand where does the gaps lie and where should the efforts be made to close them
  • Biological knowledge experts: Helps validate the GBIF Data Sets through a user friendly Interface
  • Data users: Helps in assessing whether available data is suitable and sufficient to address their research investigations

Gaps Considered

  • Spatial - Determine Geographical Biases on occurrence data collection and its quality, at every taxonomic hierarchy level
  • Temporal - Determine which time of the year is difficult to collect data, for any region, at every taxonomic hierarchy level
  • Taxonomic - Determine which taxonomic group have bias in collection of data, for any region
  • Data Sets - Find out which contributing institutions can focus more on improving the data quality, and how
  • Inventory Completeness - Determine the completeness of geographical knowledge we have for species
  • Ignorance Factor - Identify which Geographical Grid has Species Ignorance to understand the under-sampled areas

Proposed Data Enrichment Process

We propose a Software Analytics driven Data Enrichment Process to identify gaps or biases in GBIF mediated data and which can help set priorities for data mobilization or enhancements to GBIF.

The process is a 6 step cycle, which repeats until the identified gaps are closed. In order to achieve the purpose, a software based analytics framework - GBIF Data Discovery is presented, which forms an integral part of the entire cycle.

The steps are:

  1. Download Data from GBIF Website or API and Import into the framework
  2. Visualize Trends & Gaps through the User-friendly & Interactive visualizations which can provide analytics for millions of occurrence data rows in seconds
  3. Determine Biases and respective Reasons by analysing the data using charts & maps and study the gaps using in-built algorithms at Every Level of Taxonomy & Region
  4. Determine the biases and reasons for data gaps and plan for reducing the gaps based on the reasons, be it Spatial, Temporal or Taxonomic
  5. Post Implementation of Plan, re-gather occurrence data through the new surveys
  6. Based on existing methods, GBIF can sanitize and store the occurrence data and make it available over API. And then, re-do the analysis till the desired level of data completeness is achieved by restarting from Step 1.

Features

  • Data Enrichment Process Driver - A Robust Data Analytics Framework with in-built scripts to extract data, run algorithms & generate dashboards
  • Data Gap Analysis Framework - Data Gap Analysis can be easily done across Spatial, Temporal and Taxonomic biases
  • Highly Extensible Data Gap Analysis - Data Gap Analysis can be done for any Region, any Time Period and at Every Taxonomic Hierarchy Level
  • Rich Visualizations - In built Dashboards with Trends, KPIs and Data Gap Analysis using Charts & Maps - Provides more Insight than Tabular data
  • Intelligent Algorithms - Ignorance Score and Inventory Completeness calculated on the fly for the current data filters
  • Associative Filters - On the fly filters to view trends and data gap analysis at a granular level along with multi-level drill downs
  • Easy to Use - Can be configured easily with 4-5 lines for Spatial granularity, Time period and Half Ignorance Factor
  • Easy to Extend - Users can easily create their own dashboards using built in Dimensions and Measures
  • High Performance and Scalability - Analyse millions of records in seconds from within the same framework

Inspiration

GBIF is an open-data research infrastructure which provides free and open online access to species occurrence (primary biodiversity) data through their Website & APIs. However, GBIF mediated occurrence data has certain limitations and gaps, due to various reasons like sampling biasness, lack of efforts or lack of coverage of distribution of organisms, etc.

Through data gap analysis, Data users can determine if available data is suitable and sufficient to address their research investigations. Data holders & Publishers can prioritize mobilization and digitization efforts. And so, we feel that there is a need for a flexible Data Analytics Framework which can be an integral part of the Data Enrichment Process. This framework should analyse any GBIF data set without any restrictions to geography, taxonomy and temporal boundaries.

Generally, we feel that data sets are very lengthy, bulky and tedious to obtain the right information. This brings in the idea to get a software driven data analytics in use and get right visualizations for all the data sets in an interactive framework which can help get insights through algorithms and analytics tools, all in the same platform. And so, we present GBIF Data Discovery to help Data Users and Data Publishers get insights in an effective manner.

How We Built It

GBIF Data Discovery is built using the widely used QlikSense platform. As a desktop App, it can be used directly out of the box by installing the QlikSense Desktop - which is a Free Data Visualization Tool

QlikSense Desktop also will allow Data Users & Publishers to create their own Visualizations to gain further insights.

Using QlikSense Enterprise or Cloud, GBIF Data Discovery can also be hosted on Servers/Cloud, wherein Administrators can control the Data sets and Dashboards. But this will increase the reach of the dashboards since they would be available through Web Apps, thereby accessible over Desktops, Laptops, Tablets, Smart Phones, or any Device which can connect to Internet and has a supported browser.

QlikSense Desktop version can be downloaded from the GIT, and the Cloud version (limited data - Animalia in India, 2011-2014, accessible on Web) is available to limited users, shared with the DevPost Team. Contact us, if you wish to have a go on the Web Application!

How to Use

3 easy steps to setup the framework:

  1. Export GBIF Data in Tab Separated Files either through download API or through query explorer from GBIF website.Place 1 or more occurrence.txt files in Folder E:\GBIF\occurrence (Path is configurable)
  2. Open Qlik Sense Desktop and Open the App ‘GBIF Data Discovery’.Open the Data Load Editor and click on ‘Load Data’. The App will automatically generate all the Dashboards.
  3. Open the ‘App Overview’ and View the Trends, using flexible filters and visualize the available data through interactive charts and maps. Obtain insights on Data Gap Analysis, either through the in-built KPIs or through self discovery.

Case Study

To demonstrate the capabilities of GBIF Data Discovery, data for Country-India for Years 2008 to 2014 is analysed. And, Spatial, Temporal, Taxonomic, Data Set Information, Inventory Completeness and Ignorance Score based Gaps are considered and recommended actions are provided to close the gaps.

However, GBIF Data Discovery Framework can be used for any data set, for any continent/country/region, for any taxonomy group. Please download appropriate data from GBIF and use the files within GBIF Data Discovery, as per the instructions provided in GitHub Repository Read Me.

Detailed Case Study are as in the attached PDF as well as depicted as pictorial representation in the images above.

Challenges we ran into

Data downloaded from GBIF using API, in Tab separated format has issues where in new lines are not recognized for certain records. Hence, we had to sanitize the file format prior to importing it in the Framework

What's Next for GBIF Data Discovery

We have tried to ensure correctness of the data import and algorithm logic, which can be verified by GBIF experts. In consultation with them, further algorithms can be incorporated to determine completeness and correctness of GBIF mediated data.

Reference

  • Sousa-Baena MS, Couto Garcia L & Peterson AT (2013) Completeness of digital accessible knowledge of the plants of Brazil and priorities for survey and inventory. Diversity and Distributions 20(4): 369-381. doi:10.1111/ddi.12136
  • Mair L & Ruete A (2016) Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa. PLoS ONE 11(1): e0147796. doi:10.1371/journal.pone.0147796
  • Stropp J, Ladle RJ, Malhado ACM, Hortal J, Gaffuri J, Temperley WH, Olav Skøien J & Mayaux P (2016), Mapping ignorance: 300 years of collecting flowering plants in Africa. Global Ecology and Biogeography. doi:10.1111/geb.12468

Built With

  • gbif
  • qlik-sense
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