Dataset + File Management¶

Supported HTML Fields¶

We currently only support the following HTML tags for any of our textbox meatdata fields (i.e., Description) : <a>, <b>, <blockquote>, <br>, <code>, <del>, <dd>, <dl>, <dt>, <em>, <hr>, <h1>-<h3>, <i>, <img>, <kbd>, <li>, <ol>, <p>, <pre>, <s>, <sup>, <sub>, <strong>, <strike>, <ul>.

Tabular Data Files¶

Files in certain formats - Stata, SPSS, R, Excel(xlsx) and CSV - may be ingested as tabular data (see “Tabular Data Ingest” section of the User Guide for details). Tabular data files can be further explored and manipulated with TwoRavens - a statistical data exploration application integrated with Dataverse, as well as other External Tools if they have been enabled in the installation of Dataverse you are using. TwoRavens allows the user to run statistical models, view summary statistics, download subsets of variable vectors and more. To start, click on the “Explore” button, found next to each relevant tabular file (the application will be opened in a new window). To download subsets of variables click on the “Download” button found next to a relevant tabular file and select “Data Subset” in the dropdown menu. You will then be able to create your subset using the interface opened in a new window (this functionality is also provided by the TwoRavens project). See the TwoRavens documentation section for more information.

Additional download options available for tabular data (found in the same drop-down menu under the “Download” button):

• As tab-delimited data (with the variable names in the first row);
• The original file uploaded by the user;
• Saved as R data (if the original file was not in R format);
• Variable Metadata (as a DDI Codebook XML file);
• Data File Citation (currently in either RIS or EndNote XML format);
• All of the above, as a zipped bundle.

Geospatial¶

Geospatial shapefiles can be further explored and manipulated through our integration with WorldMap, a geospatial data visualization and analysis tool developed by the Center for Geographic Analysis at Harvard University. A shapefile is a set of files, often uploaded/transferred in .zip format. This set may contain up to 15 files. A minimum of 3 specific files (.shp, .shx, .dbf) are needed to be a valid shapefile and a 4th file (.prj) is required for WorldMap–or any type of meaningful visualization.

For ingest into Dataverse and connecting to WorldMap, these 4 files are the minimum required:

• .shp - shape format; the feature geometry itself
• .shx - shape index format; a positional index of the feature geometry to allow seeking forwards and backwards quickly
• .dbf - attribute format; columnar attributes for each shape, in dBase IV format
• .prj - projection format; the coordinate system and projection information, a plain text file describing the projection using well-known text format

For a zipped shapefile, we require 4 files with these extensions. Other files may be included within the zipped shapefile, but they are not required:

• .shp
• .shx
• .prj
• .dbf

For example, if these files were included within a .zip, the “Map Data” button would appear:

• subway_line.shp
• subway_line.shx
• subway_line.prj
• subway_line.dbf

Once you publish your dataset with your shape files, you will be able to use the “Map Data” button using GeoConnect to visualize and manipulate these files for users to Explore this geospatial data using the WorldMap interface. Please note: In order to map your data file, a copy will be sent to Harvard’s WorldMap platform. You have the ability to delete any maps, and associated data, from the Harvard WorldMap platform, at any time.

Astronomy (FITS)¶

Metadata found in the header section of Flexible Image Transport System (FITS) files are automatically extracted by Dataverse, aggregated and displayed in the Astronomy Domain-Specific Metadata of the Dataset that the file belongs to. This FITS file metadata, is therefore searchable and browsable (facets) at the Dataset-level.

Compressed Files¶

Compressed files in zip format are unpacked automatically. If it 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), you will get an error message and the file will uploads as is.

Support for unpacking tar files will be added when this ticket is closed: https://github.com/IQSS/dataverse/issues/2195.

Advanced Options¶

There are several advanced options available for certain file types.

• Image files: .jpg, .png, and .tif files are able to be selected as the default thumbnail for a dataset. The selected thumbnail will appear on the search result card for that dataset.
• SPSS files: SPSS files can be tagged with the language they were originally coded in. This is found by clicking on Advanced Options and selecting the language from the list provided.

Data Provenance¶

Data Provenance is a record of where your data came from and how it reached its current form. It describes the origin of a data file, any transformations that have been made to that file, and any persons or organizations associated with that file. A data file’s provenance can aid in reproducibility and compliance with legal regulations. Dataverse can help you keep track of your data’s provenance. Currently, Dataverse only makes provenance information available to those who have edit permissions on your dataset, but in the near future we plan to expand this feature to make provenance information available to the public. You can track our progress in this issue on the Dataverse GitHub repository.

Dataverse accepts provenance information in two forms: a Provenance File or a free-text Provenance Description. You can attach this provenance information to your data files in Dataverse as part of the file upload process, by clicking Edit -> Provenance:

This will open a window where you can add your Provenance File and/or Provenance Description:

A Provenance File is the preferred way of submitting provenance information to Dataverse because it provides a detailed and trustworthy record. Provenance files are typically generated during the process of data analysis, using provenance capture tools like provR, RDataTracker, NoWorkFlow, recordr, or CamFlow.

Once you upload a provenance file, Dataverse will need some additional information in order to accurately connect it to your data file. Once provenance file upload finishes, an input box labeled “Connect entity” will appear under the file. Provenance files contain a list of “entities”, which include your data file as well as any objects associated with it (e.g. a chart, a spellchecker, etc.). You will need to tell Dataverse which entity within the provenance file represents your data file. You may type the name of the entity into the box, or click the arrow next to the box and select the entity from a list of all entities in the provenance file.

For more information on entities and the contents of provenance files, see the W3C PROV Model Primer.

Once you’ve uploaded your Provenance File and connected the proper entity, you can hit the Preview button to view the raw JSON of the Provenance File. This can help you confirm that you’ve uploaded the right file. Be sure to double-check it, because the Provenance File will made permanent once it’s finalized. At that point you will not be able to replace, remove, or otherwise edit the Provenance File. This ensures that the Provenance File maintains a stable, immutable record of the data file’s history. This finalization of the Provenance File happens at different points depending on the status of your data file. If this is a brand new data file that has never been published before, then its associated Provenance File will be made permanent once you publish the dataset. If this data file has been published in a previous version of your dataset, then its associated Provenance File will be made permanent as soon as you upload the Provenance File and click “Save Changes” on the warning popup.

A Provenance Description allows you to add more provenance information in addition to or in place of a provenance file. This is a free-text field that allows you to enter any information you feel might be relevant to those interested in learning about the provenance of your data. This might be a good place to describe provenance factors like what operating system you used when working with the data file, what functions or libraries you used, how data was merged into the file, what version of the file you used, etc. The Provenance Description is not as useful or trustworthy as a provenance file, but it can still provide value. Unlike the Provenance File, the Provenance Description is never made permanent: you can always edit, remove, or replace it at any time.

You can return to attach provenance to your data file later on by clicking the “Add + Edit Metadata” button on the file page, and then clicking the “Edit -> Provenance” button.

rsync Upload¶

rsync is typically used for synchronizing files and directories between two different systems, using SSH to connect rather than HTTP. Some Dataverse installations allow uploads using rsync, to facilitate large file transfers in a reliable and secure manner.

Cloud Storage + Computing¶

Dataverse installations can be configured to facilitate cloud-based storage and/or computing (this feature is considered experimental at this time, and some of the kinks are still being worked out). While the default configuration for Dataverse uses a local file system for storing data, a cloud-enabled Dataverse installation can use a Swift object storage database for its data. This allows users to perform computations on data using an integrated cloud computing environment.

Edit File Metadata¶

Go to the dataset you would like to edit, where you will see the listing of files. Select the files you would like to edit by using either the Select All checkbox or individually selecting files. Next, click the “Edit Files” button above the file table and from the dropdown menu select if you would like to:

• Delete the selected files
• Edit the file metadata (file name, description) for the selected files
• Restrict the selected files
• Unrestrict the selected files (only if the selected files are restricted)
• Add tags to the selected files

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.

File Tags¶

File tags are comprised of custom, category (i.e. Documentation, Data, Code) and tabular data tags (i.e. Event, Genomics, Geospatial, Network, Panel, Survey, Time Series). Use the dropdown select menus as well as the custom file tag input to apply these tags to the selected files. There is also a Delete Tags feature that, if checked, will allow you to delete unused file tags within that dataset.

CC0 Public Domain Dedication¶

By default, all new datasets created through Dataverse’s web UI are given a Creative Commons CC0 Public Domain Dedication.

The Creative Commons organization defines a number of licenses that allow copyright holders to release their intellectual property more openly, with fewer legal restrictions than standard copyright enforces. Each Creative Commons license typically specifies simple terms for how the IP must be used, reused, shared, and attributed. In addition to these licenses, Creative Commons also provides public domain tools that make it easy to dedicate IP to the public domain.

In the context of Dataverse, their CC0 Public Domain Dedication allows you to unambiguously waive all copyright control over your data in all jurisdictions worldwide. Data released with CC0 can be freely copied, modified, and distributed (even for commercial purposes) without violating copyright. In most parts of the world, factual data is exempt from copyright anyway, but applying CC0 removes all ambiguity and makes the legal copyright status of the data as clear as possible. Dataverse applies CC0 to datasets by default because it facilitates reuse, extensibility, and long-term preservation of research data by assuring that the data can be safely handled by anyone without fear of potential copyright pitfalls.

Though CC0 waives a dataset owner’s legal copyright controls over the data, it does not exempt Dataverse users from following ethical and professional norms in scholarly communications. The Dataverse Community Norms * as well as scientific best practices assert that proper credit should be given via citation. Regardless of whether CC0 has been applied or not, Dataverse users are expected to cite the data they use, giving credit to the data’s authors. This expectation applies to both the Dataverse Community and the entire wider scholarly community.

Additionally, users are still expected to respect access restrictions and other terms applied to CC0 files in Dataverse. Additional restrictions, conditions, and terms can still be compatible with CC0, as CC0 only operates in the realm of copyright, which is rather limited when it comes to data.

If a data owner feels that CC0 is not suitable for their data, they are able to enter custom Terms of Use, as detailed in the following section.

* Legal Disclaimer: these Community Norms are not a substitute for the CC0 waiver or custom terms and licenses applicable to each dataset. 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.

Custom Terms of Use for Datasets¶

If you are unable to use the CC0 Public Domain Dedication for your datasets, you may specify your own custom Terms of Use. To do so, select “No, do not apply CC0 - “Public Domain Dedication”, and a Terms of Use text box will show up allowing you to enter your own custom terms of use for your dataset. To add more information about the Terms of Use, we have provided fields like Special Permissions, Restrictions, Citation Requirements, etc.

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.

Note: Some Dataverse installations do not allow for file restriction.

Guestbook¶

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.

Dataset-Level¶

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.

File-Level¶

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.

Thumbnails¶

Thumbnail images can be assigned to a dataset manually or automatically. The thumbnail for a dataset appears on the search result card for that dataset and on the dataset page itself. If a dataset contains one or more data files that Dataverse recognizes as an image, then one of those images is automatically selected as the dataset thumbnail.

If you would like to manually select your dataset’s thumbnail, you can do so by clicking the “Edit” button on your dataset, and selecting “Thumbnails + Widgets” from the dropdown menu.

On this page, under the Thumbnail tab you will see three possible actions.

Select Available File: Click the “Select Thumbnail” button to choose an image from your dataset to use as the dataset thumbnail.

Upload New File: Upload an image file from your computer to use as the dataset thumbnail. While by default your thumbnail image is drawn from a file in your dataset, this will allow you to upload a separate image file to use as your dataset thumbnail. This uploaded image file will only be used as the dataset thumbnail; it will not be stored as a data file in your dataset.

Remove Thumbnail: If you click the “Remove” button under the thumbnail image, you will remove the dataset’s current thumbnail. The Dataset will then revert to displaying a basic default icon as the dataset thumbnail.

When you’re finished on this page, be sure to click “Save Changes” to save what you’ve done.

Note: If you prefer, it is also possible to set an image file in your dataset as your thumbnail by selecting the file, going to Edit Files -> Metadata, and using the “Set Thumbnail” button.

Widgets¶

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.

Version Details¶

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.