DataverseLV Repository User Guide 

DataverseLV is Latvia’s research data repository that offers a secure and reliable environment for storing, publishing, and sharing research data in accordance with FAIR principles and open science requirements. It is intended for researchers in Latvia.  
We have prepared a guide that will help you understand the data deposit process step by step, ensure data quality, legal compliance, and appropriate documentation. A summary of the guide is also available in document format, which you can print.  
ResearcherSTARTENDDoes not complyCompliesCreate account in

institutional e-mail address, usingdv.dataverse.lvRequest access to the
appropriate collection using
the "Contact" button.If you cannot find your
institution's collection, contactus atdatukuratori@vpc.lvGet acquainted with the
regulatory documents:Start creating a dataset
using the "Add Data"
function (see the tutorials).(see the tutorials)enter the metadata and
attach accompanying

etc.) and save the dataset
using the "Save" button.documentation (,Following theSupplement the metadata
as needed, adjust access
levels for files, and specify
the dataset usage license.Submit the dataset for
review using the "Submit for
Review" button.Make the necessary
correctionsPublishes the dataset and
notifies the researcherReturn the dataset to the
researcher, indicating the
necessary changesChecks whether the dataset
meets the requirements and
completes the review
protocolSends an email to
researchers that the
dataset will be reviewedAdds the Researcher to the
appropriate collection and
informs the researcher
about the deposition
requirementsPrivacy PolicychecklistReadMeData Deposition AgreementTerms of UseData Steward

This section contains the main documents that define the terms of use for the DataverseLV repository, data deposition conditions, and data quality requirements.  

In this section, you will find step-by-step instructions that will help you prepare, describe, and upload datasets to the DataverseLV repository in accordance with dataset quality requirements.  

DataverseLV Repository User Guide 

DataverseLV is Latvia’s research data repository that offers a secure and reliable environment for storing, publishing, and sharing research data in accordance with FAIR principles and open science requirements. It is intended for researchers in Latvia.  
We have prepared a guide that will help you understand the data deposit process step by step, ensure data quality, legal compliance, and appropriate documentation. A summary of the guide is also available in document format, which you can print.  
ResearcherSTARTAtbilstData StewardTerms of UseData Deposition AgreementReadMechecklistPrivacy PolicyAdds the Researcher to the
appropriate collection and
informs the researcher
about the deposition
requirementsSends an email to
researchers that the
dataset will be reviewedChecks whether the dataset
meets the requirements and
completes the review
protocolReturn the dataset to the
researcher, indicating the
necessary changesPublishes the dataset and
notifies the researcherMake the necessary
correctionsSubmit the dataset for
review using the "Submit for
Review" button.Supplement the metadata
as needed, adjust access
levels for files, and specify
the dataset usage license.Following thedocumentation (enter the metadata and
attach accompanying

etc.) and save the dataset
using the "Save" button.(see the tutorials),Start creating a dataset
using the "Add Data"
function (see the tutorials).Get acquainted with the
regulatory documents:Get acquainted with the
regulatory documents:Request access to the
appropriate collection using
the "Contact" button.dv.dataverse.lv, usingCreate account in

institutional e-mail addressCompliesDoes not complyEND

This section contains the main documents that define the terms of use for the DataverseLV repository, data deposition conditions, and data quality requirements.  

In this section, you will find step-by-step instructions that will help you prepare, describe, and upload datasets to the DataverseLV repository in accordance with dataset quality requirements.