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Data Standards

To address the global data gap on ASM and improve data quality there is a need to define and refine sector-wide standards for data collection and reporting.

These are the minimum technical, legal, and ethical requirements and best practices to guide the Delve platform and sector stakeholders in filling the global data gaps.

The key Delve principle guiding this process is transparency. In this way, Delve recognises the wide diversity of methodologies, approaches, and ongoing challenges of ASM data collection and reporting while also striving to collaborate with sector stakeholders to improve methodologies. The approach also enables an open and honest discussion concerning the reliability of estimates.

Although there is no single, robust data collection protocol and quality assurance/quality control mechanism, the following are outlined as start-points to implementing sector-wide data collection and reporting standards.

Data quality checklist

For new primary data entries to be accepted by Delve, users must adhere to three general reporting principles that the data is reliable, participant consent was obtained, and ethical issues were considered. These must be transparently and clearly outlined when submitting data sets for review and upload.

  1. Is the data reliable? Reliability concerns the extent to which data collection techniques will yield consistent findings and similar observations or conclusions if repeated and ensuring that there is transparency in how the data was collected, processed, and analyzed. Key questions therefore centre on whether a consistent data collection process and standards have been used and reported.
  2. Has free, prior and informed consent been obtained from participants? Where possible, the highest academic standards of consent should apply. Participants must be made fully aware of what their involvement in a study entails before agreeing to take part. This can take the form of written or verbal consent and should be recorded for future reference. The participant must also be made aware that they are free to withdraw at any time, and their rights of free will, privacy, confidentiality and well-being should be respected. Data should be stored in accordance to relevant data protection laws and participants should be free to withdraw from a study at any time.
  3. Have ethical issues been considered? A general approach of ‘do not harm’ should apply to all research activities. Surveys, interviews and questionnaires should consider the community’s involvement, authorization, anonymity and confidentiality. Asking questions such as how participants will benefit from the research, and if any unequal relationships exists between data collectors and participants are important to mitigate against bias or a conflict of interest. It is therefore important that that ethical issues resulting from design decisions, and considered all risks to the research team, participants, data collected, and research organization/partners/funders have been considered, and steps been taken to mitigate them.

Data collection and reporting

The following best-practice for data collection and reporting have been identified. This is a draft list that remains under review.

  1. Regular on-the-ground surveys are the best sources of data. By repeating surveys regularly (yearly or bi-annually) using the same metrics across time and different geographies, it is possible to rapidly build a detailed picture of ASM activities and draw comparisons between countries and communities. By going direct to the field, it is also possible to gain a better understanding of the realities at the local level and helps to join the international and national policies and initiatives directly to communities.
  2. Always reference original, primary data sources. This enables the reader to locate the original data source and prevents data recycling. Sometimes it may not be possible to find the original source if it is embedded in a long trail of references and sub-references; if this is the case it should be clearly stated. This will also help to future characterize the extent of the data gap making it clear what areas need to be addressed.
  3. The methodology and approach used should be clearly outlined. Clear notes on methodologies should be described and supplemented with background evidence and/or data. Outlining methodologies enables replication and repetition of studies by others, helping to promote improved methodologies, collaboration, and data sharing, as well as for scrutiny of results and findings.
  4. Authors should state their confidence in estimates and any limitations. By stating the limitations and extent to which the findings of a study are generalizable to other contexts, it helps ensure that data are used appropriately to inform evidence-based decision making.
  5. Benchmarking and triangulation should be used to compare estimates and assess reliability. This is a simple sense-checking exercise to determine whether the data and findings could be considered reliable and accurate. It is an easy and quick way to spot any inaccuracies or inconsistencies.
  6. Quantitative data should be accompanied by qualitative analysis. It is only through accompanying qualitative information and in-depth socioeconomic and political analysis that quantitative data can be made sense of and contextualized to support the development of more effective formalization strategies and policies.

Aggregating primary and secondary data

Combining data sets and being consistent in use is not straightforward. There are many variables including the reliability of primary data collection techniques and secondary data analyses depending on which methodologies are used. Delve identified two main types of ASM data that are available:

  1. Big, country-level quantitative estimates. Usually these are broad-based “best guess” by experts based on a combination of field-based observation, surveys and anecdotal evidence. The reliability of an expert can be challenging to assess, and in general these have a high standard deviation.
  2. Small, community-level qualitative field studies. These are often detailed, analytical, sometimes ethnographic studies completed in one small mining locale, often with extensive qualitative insights on social or environmental themes. Whilst enlightening, these studies may not be widely comparable to other data sets and have different methodologies.

The Delve approach to aggregating data from these different sources is again based on transparency. All existing available data has been accepted into the resource library and database, and where possible, the original primary data source for a statistics is identified and clearly reference. In addition, benchmarking and triangulation to cross-check and compare estimates both from different sources, and over time has been undertaken, and key country experts have been consulted to provide the best current estimates as well as to 'sense-check' reported figures. In general, the most recent year estimates have been used, and where multiple estimates have been found they are clearly reported.