To edit your dataverse, navigate to your dataverse homepage and select the “Edit Dataverse” button, where you will be presented with the following editing options:
The General Information page is how you edit the information you filled in while creating your dataverse. If you need to change or add a contact email address, this is the place to do it. Additionally, you can update the metadata elements used for datasets within the dataverse, change which metadata fields are hidden, required, or optional, and update the facets you would like displayed for browsing the dataverse. If you plan on using templates, you need to select the metadata fields on the General Information page.
Tip: The metadata fields you select as required, will appear on the Create Dataset form when someone goes to add a dataset to the dataverse.
The Theme feature provides you with a way to customize the look of your dataverse. You can decide either to use the customization from the dataverse above yours or upload your own image file. Supported image types are JPEG, TIFF, or PNG and should be no larger than 500 KB. The maximum display size for an image file in a dataverse’s theme is 940 pixels wide by 120 pixels high. Additionally, you can select the colors for the header of your dataverse and the text that appears in your dataverse. You can also add a link to your personal website, the website for your organization or institution, your department, journal, etc.
The Widgets feature provides you with code for you to put on your personal website to have your dataverse displayed there. There are two types of Widgets for a dataverse, a Dataverse Search Box widget and a Dataverse Listing widget. From the Widgets tab on the Theme + Widgets page, you can copy and paste the code snippets for the widget you would like to add to your website. If you need to adjust the height of the widget on your website, you may do so by editing the heightPx=500 parameter in the code snippet.
Dataverse Search Box Widget
The Dataverse Search Box Widget will add a search box to your website that is linked to your dataverse. Users are directed to your dataverse in a new browser window, to display the results for search terms entered in the input field.
Dataverse Listing Widget
The Dataverse Listing Widget provides a listing of all your dataverses and datasets for users to browse, sort, filter and search. When someone clicks on a dataverse or dataset in the widget, it displays the content in the widget on your website. They can download data files directly from the datasets within the widget. If a file is restricted, they will be directed to your dataverse to log in, instead of logging in through the widget on your website.
When you access a dataverse’s permissions page, you will see there are three sections: Permissions, Users/Groups, and Roles.
Clicking on Permissions will bring you to this page:
By clicking on the Edit Access button, you are able to change the settings allowing no one or anyone to add either dataverses or datasets to a dataverse.
The Edit Access pop up allows you to also select if someone adding a dataset to this dataverse should be allowed to publish it (Curator role) or if the dataset will be submitted to the administrator of this dataverse to be reviewed then published (Contributor role). These Access settings can be changed at any time.
You can also give access to a Dataverse user to allow them to access an unpublished dataverse as well as other roles. To do this, click on the Assign Roles to Users/Groups button in the Users/Groups section. You can also give multiple users the same role at one time. This roles can be removed at any time.
* Please note that the ability to choose which metadata fields are hidden, required, or optional is done on the General Information page for the dataverse.
Featured Dataverses is a way to display sub dataverses in your dataverse that you want to feature for people to easily see when they visit your dataverse.
Click on Featured Dataverses and a pop up will appear. Select which sub dataverses you would like to have appear.
Note: Featured Dataverses can only be used with published dataverses.
Dataset linking allows a dataverse owner to “link” their dataverse to a dataset that exists outside of that dataverse, so it appears in the dataverse’s list of contents without actually being in that dataverse. You can link other users’ datasets to your dataverse, but that does not transfer editing or other special permissions to you. The linked dataset will still be under the original user’s control.
For example, researchers working on a collaborative study across institutions can each link their own individual institutional dataverses to the one collaborative dataset, making it easier for interested parties from each institution to find the study.
In order to link a dataset, you will need your account to have the “Add Dataset” permission on the Dataverse that is doing the linking. If you created the dataverse then you should have this permission already, but if not then you will need to ask the admin of that dataverse to assign that permission to your account. You do not need any special permissions on the dataset being linked.
To link a dataset to your dataverse, you must navigate to that dataset and click the white “Link” button in the upper-right corner of the dataset page. This will open up a window where you can type in the name of the dataverse that you would like to link the dataset to. Select your dataverse and click the save button. This will establish the link, and the dataset will now appear under your dataverse.
There is currently no way to remove established links in the UI. If you need to remove a link between a dataverse and a dataset, please contact the support team for the Dataverse installation you are using.
Similarly to dataset linking, dataverse linking allows a dataverse owner to “link” their dataverse to another dataverse, so the dataverse being linked will appear in the linking dataverse’s list of contents without actually being in that dataverse. Currently, data depositor can self-link dataverse/datasets to another dataverse.
If you need further assistance on linking dataverse/dataset in the DR-NTU (Data) Dataverse installation, please contact firstname.lastname@example.org.
Publish Your Dataverse
Once your dataverse is ready to go public, go to your dataverse page, click on the “Publish” button on the right hand side of the page. A pop-up will appear to confirm that you are ready to actually Publish, since once a dataverse is made public, it can no longer be unpublished.
To upload new files to a dataset, go to the dataset you want to update and click on the Upload Files button in the files tab. From there you will be brought to the Upload Files page for the dataset. Once you have uploaded files, you will be able to edit the file metadata, restrict, add tags, or delete them before saving.
See: Supported Metadata and References
* Legal Disclaimer: these Community Norms are not a substitute for the CC-BY-NC or custom terms and licenses applicable to each dataset. Please be advised that the Community Norms are not a binding contractual agreement, and that downloading datasets from Dataverse does not create a legal obligation to follow these policies.
Here is an example of a Data Usage Agreement for datasets that have de-identified human subject data.
Restricted Files + Terms of Access
If you restrict any files in your dataset, you will be prompted by a pop-up to enter Terms of Access for the data. This can also be edited in the Terms tab or selecting Terms in the “Edit” dropdown button in the dataset. You may also allow users to request access for your restricted files by enabling “Request Access”. To add more information about the Terms of Access, we have provided fields like Data Access Place, Availability Status, Contact for Access, etc.
This is where you will enable a particular Guestbook for your dataset, which is setup at the Dataverse-level. For specific instructions please visit the Dataset Guestbooks section of the Dataverse Management page.
Roles & Permissions
Dataverse user accounts can be granted roles that define which actions they are allowed to take on specific dataverses, datasets, and/or files. Each role comes with a set of permissions, which define the specific actions that users may take.
Roles and permissions may also be granted to groups. Groups can be defined as a collection of Dataverse user accounts.
Admins or curators of a dataset can assign roles and permissions to the users of that dataset. If you are an admin or curator of a dataset, then you can get to the dataset permissions page by clicking the “Edit” button, highlighting “Permissions” from the dropdown list, and clicking “Dataset”.
When you access a dataset’s permissions page, you will see two sections:
Users/Groups: Here you can assign roles to specific users or groups, determining which actions they are permitted to take on your dataset. You can also reference a list of all users who have roles assigned to them for your dataset and remove their roles if you please. Some of the users listed may have roles assigned at the dataverse level, in which case those roles can only be removed from the dataverse permissions page.
Roles: Here you can reference a full list of roles that can be assigned to users of your dataset. Each role lists the permissions that it offers.
If specific files in your dataset are restricted access, then you can grant specific users or groups access to those files while still keeping them restricted to the general public. If you are an admin or curator of a dataset, then you can get to the file-level permissions page by clicking the “Edit” button, highlighting “Permissions” from the dropdown list, and clicking “File”.
When you access a dataset’s file-level permissions page, you will see two sections:
Users/Groups: Here you can see which users or groups have been granted access to which files. You can click the “Grant Access to Users/Groups” button to see a box where you can grant access to specific files within your dataset to specific users or groups. If any users have requested access to a file in your dataset, you can grant or reject their access request here.
Restricted Files: In this section, you can see the same information, but broken down by each individual file in your dataset. For each file, you can click the “Assign Access” button to see a box where you can grant access to that file to specific users or groups.
The Widgets feature provides you with code for your personal website so your dataset can be displayed. There are two types of Widgets for a dataset: the Dataset Widget and the Dataset Citation Widget. Widgets are found by going to your dataset page, clicking the “Edit” button (the one with the pencil icon) and selecting “Thumbnails + Widgets” from the dropdown menu.
In the Widgets tab, you can copy and paste the code snippets for the widget you would like to add to your website. If you need to adjust the height of the widget on your website, you may do so by editing the heightPx=500 parameter in the code snippet.
The Dataset Widget allows the citation, metadata, files and terms of your dataset to be displayed on your website. When someone downloads a data file in the widget, it will download directly from the datasets on your website. If a file is restricted, they will be directed to your dataverse to log in, instead of logging in through the widget on your site.
To edit your dataset, you will need to return to the Dataverse repository where the dataset is stored. You can easily do this by clicking on the link that says “Data Stored in (Name) Dataverse” found in the bottom of the widget.
Dataset Citation Widget
The Dataset Citation Widget will provide a citation for your dataset on your personal or project website. Users can download the citation in various formats by using the Cite Data button. The persistent URL in the citation will direct users to the dataset in your dataverse.
When you publish a dataset (available to an Admin, Curator, or any custom role which has this level of permission assigned), you make it available to the public so that other users can browse or search for it. Once your dataset is ready to go public, go to your dataset page and click on the “Publish” button on the right hand side of the page. A pop-up will appear to confirm that you are ready to actually Publish since once a dataset is made public it can no longer be unpublished.
Whenever you edit your dataset, you are able to publish a new version of the dataset. The publish dataset button will reappear whenever you edit the metadata of the dataset or add a file.
Note: Prior to publishing your dataset the Data Citation will indicate that this is a draft but the “DRAFT VERSION” text will be removed as soon as you Publish.
Once you edit your published dataset a new draft version of this dataset will be created. To publish this new version of your dataset, select the “Publish Dataset” button on the top right side of the page. If you were at version 1 of your dataset, depending on the types of changes you had made, you would be asked to publish your draft as either version 1.1 or version 2.0.
Important Note: If you add a file, your dataset will automatically be bumped up to a major version (e.g., if you were at 1.0 you will go to 2.0).
On the Versions tab of a dataset page, there is a versions table that displays the version history of the dataset. You can use the version number links in this table to navigate between the different versions of the dataset, including the unpublished draft version, if you have permission to access it.
There is also a Versions tab on the file page. The versions table for a file displays the same information as the dataset, but the summaries are filtered down to only show the actions related to that file. If a new dataset version were created without any changes to an individual file, that file’s version summary for that dataset version would read “No changes associated with this version”.
To view exactly what has changed, starting from the originally published version to any subsequent published versions: click the Versions tab on the dataset page to see all versions and changes made for that particular dataset.
Once you have more than one version (this can simply be version 1 and a draft), you can click the “View Details” link next to each summary to learn more about the metadata fields and files that were either added or edited. You can also click the checkboxes to select any two dataset versions, then click the “View Differences” button to open the Version Differences Details popup and compare the differences between them.
Deaccession Your Dataset [not recommended]
Warning: It is not recommended that you deaccession a dataset or a version of a dataset. This is a very serious action that should only occur if there is a legal or valid reason for the dataset to no longer be accessible to the public. If you absolutely must deaccession, you can deaccession a version of a dataset or an entire dataset.
To deaccession, go to your published dataset (or add a new one and publish it), click the “Edit” button, and from the dropdown menu select “Deaccession Dataset”. If you have multiple versions of a dataset, you can select here which versions you want to deaccession or choose to deaccession the entire dataset.
You must also include a reason as to why this dataset was deaccessioned. Select the most appropriate reason from the dropdown list of options. If you select “Other”, you must also provide additional information.
Add more information as to why this was deaccessioned in the free-text box. If the dataset has moved to a different repository or site you are encouraged to include a URL (preferably persistent) for users to continue to be able to access this dataset in the future.
If you deaccession the most recently published version of the dataset but not all versions of the dataset, you may then revisit an earlier version and create a new non-deaccessioned draft for the dataset. For example, imagine you have a version 1 and version 2 of a dataset, both published, and you deaccession version 2. You may then edit version 1 of the dataset and a new draft version will be created.
Important Note: A tombstone landing page with the basic citation metadata will always be accessible to the public if they use the persistent URL (Handle or DOI) provided in the citation for that dataset. Users will not be able to see any of the files or additional metadata that were previously available prior to deaccession.
You will not have to leave the dataset page to complete these action, except for editing file metadata, which will bring you to the Edit Files page. There you will have to click the “Save Changes” button to apply your edits and return to the dataset page.
If you restrict files, you will also prompted with a popup asking you to fill out the Terms of Access for the files. If Terms of Access already exist, you will be asked to confirm them. Note that some Dataverse installations do not allow for file restrictions.
The File Path metadata field is Dataverse’s way of representing a file’s location in a folder structure. When a user uploads a .zip file containing a folder structure, Dataverse automatically fills in the File Path information for each file contained in the .zip. If a user downloads the full dataset or a selection of files from it, they will receive a folder structure with each file positioned according to its File Path.
A file’s File Path can be manually added or edited on the Edit Files page. Changing a file’s File Path will change its location in the folder structure that is created when a user downloads the full dataset or a selection of files from it.
If there is more than one file in the dataset, and once at least one of them has a non-empty directory path, the Dataset Page will present an option for switching between the traditional table view, and the tree-like view of the files showing the folder structure, as in the example below:
In cases where you would like to revise an existing file rather than add a new one, you can do so using our Replace File feature. This will allow you to track the history of this file across versions of your dataset, both before and after replacing it. This could be useful for updating your data or fixing mistakes in your data. Because replacing a file creates an explicit link between the previous dataset version and the current version, the file replace feature is not available for unpublished dataset drafts. Also note that replacing a file will not automatically carry over that file’s metadata, but once the file is replaced then its original metadata can still be found by referencing the previous version of the file under the “Versions” tab of the file page.
To replace a file, go to the file page for that file, click on the “Edit” button, and from the dropdown list select “Replace”. This will bring you to the Replace File page, where you can see the metadata for the most recently published version of the file and you can upload your replacement file. Once you have uploaded the replacement file, you can edit its name, description, and tags. When you’re finished, click the “Save Changes” button.
After successfully replacing a file, a new dataset draft version will be created. A summary of your actions will be recorded in the “Versions” tab on on both the dataset page and file page. The Versions tab allows you to access all previous versions of the file across all previous versions of your dataset, including the old version of the file before you replaced it.
Compressed files in .zip format are unpacked automatically. If a .zip file fails to unpack for whatever reason, it will upload as is. If the number of files inside are more than a set limit (1,000 by default, configurable by the Administrator), you will get an error message and the .zip file will upload as is.
If the uploaded .zip file contains a folder structure, Dataverse will keep track of this structure. A file’s location within this folder structure is displayed in the file metadata as the File Path. When you download the contents of the dataset, this folder structure will be preserved and files will appear in their original locations.
These folder names are subject to strict validation rules. Only the following characters are allowed: the alphanumerics, ‘_’, ‘-‘, ‘.’ and ‘ ‘ (white space). When a zip archive is uploaded, the folder names are automatically sanitized, with any invalid characters replaced by the ‘.’ character. Any sequences of dots are further replaced with a single dot. For example, the folder name data&info/code=@137 will be converted to data.info/code.137. When uploading through the Web UI, the user can change the values further on the edit form presented, before clicking the ‘Save’ button.
Note: If you upload multiple .zip files to one dataset, any subdirectories that are identical across multiple .zips will be merged together when the user downloads the full dataset.