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Data Upload:

Data upload is simply to send data to a remote system such as a server or another client so that the remote system can store a copy.

Guideline to Data Upload:

• All datasets, having passed through the Digitization Team, Quality Assurance Team and then to the Head of Unit for upload approval, must be received directly from the H.O.U.
• Every dataset must be in CSV format except financial statements.
• All datasets must be properly read through and studied to identify any error omitted by the Quality Assurance (QA) Team before it can be uploaded into the portal.
• Where errors in spellings are spotted, it behoves on the Communication Team (CT) members to notify the QA Team of such or effect the necessary correction(s).
• Where errors identified are in the contained in the data, such file should be sent back to the QA Team.
• Communication Team members must ensure that the title of each dataset adequately reflects the content thereof. Situation where the tile does not sufficiently reflect the content, CT members must ensure it does by making the necessary correction(s).
• CT members must ensure that the titles of datasets are unique and distinct no matter the similarity in data content.
• In writing titles of datasets, ALL words must have their initial letter capitalized except for prepositions. For example; Legal Dump Sites in Edo State, Registered Creative Hubs from 2001 to 2005.
• During the upload process, datasets must be adequately described to give end users a better insight into the nature/content of the dataset.
• During data upload, datasets must have at least four (4) tags for easy search by end users.
• The availability of uploaded datasets must be communicated about via Open data social media accounts (Twitter and Facebook).

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Quality Assurance/Quality Control:

Quality control (QC) on data is defined as a system of checks to assess and maintain the quality of the data being compile while Quality assurance (QA) on data is a planned system of review procedures conducted outside the actual data compilation by personnel not directly involved in the inventory development process.

Guideline for Data Quality Assurance/Quality Control:

• Look out for records or rows that are omitted
• Look out for spaces in the record
• Look out for differences in values in the record especially monetary values and verify with original copy
• Look out for inconsistencies in abbreviation of a particular word make sure all the abbreviations are the same
• Look out Merged rows within the data and remove them
• look for Serial number column; this should be removed
• Look out for Summations; these totals should be removed as well if other records in the data are not referencing it.
• All field titles should be CAPITAL LETTERS
• All empty cells should be replaced with the dash sign “-“
• Each column should be aligned Uniformly
• All excel files must be saved in Comma Separated Value (CSV) after QA.


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Data Collection:

Data collection is simply the process of information gathering. There are various methods of data collection, such as personal interview, mail, and via the internet.
Below are some of the activities carried out during Data collection:

Create awareness and sensitization on Open data in the Ministries, Departments and Agencies (MDAs)
To make these MDAs see the benefits of Open data.

Compilation of required data set.
To harness all useful datasets residing in these MDAs.

Prepare Letters to the MDAs requesting for dataset
To officially inform and authorize MDAs to release datasets.

Ensure Letter is signed and dispatched to the MDAS.
To retain letter integrity and ensure its sent from the appropriate Office.

Contact the focal persons (Via SMS or calls) to follow up on data request.
To inform them on the letters sent to their MDA for prompt followup.

Ensuring released datasets are Approved.
To guarantee the data source and Integrity.

Receive datasets, update process log sheet and forward datasets to Digitization unit.
For Process control, confirms receipt of datasets 

Data Digitization Process Guidelines:

Data Digitization is the process by which physical or manual records such as text, images, video, and audio are converted into digital forms.
Data can be collected in either soft or hard copy by the Data Collection team and subsequently passed to the Digitization team. Digitization team receives this data and checks for the following:
• That dataset does not contain merged cells.
• That dataset does not contain personal identifiable information especially for persons not in the public sector of the state.
• That datasets which contains specific entities like (dates or MDA) in the filename or the title of the dataset should be made to include such entities as part of the data.
• That the dataset is not a long list of standalone data like a single column dataset.
• That dataset presented in soft copy word format are represented in spread sheet formats by copying the key variable elements into the spreadsheet file and specifying the appropriate column heading.
• That datasets are free of spelling errors.

For Hard copies make sure to digitize the data in line with the above stated guidelines as well as make sure that the digitized data are consistent with the hard copy.

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