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+.. _dataset-file-upload:
+
File Upload
===========
@@ -153,6 +157,19 @@ Beginning with Dataverse Software 5.0, the way a Dataverse installation handles
- If a user attempts to replace a file with another file that has the same checksum, an error message will be displayed and the file will not be able to be replaced.
- If a user attempts to replace a file with a file that has the same checksum as a different file in the dataset, a warning will be displayed.
+BagIt Support
+-------------
+
+BagIt is a set of hierarchical file system conventions designed to support disk-based storage and network transfer of arbitrary digital content. It offers several benefits such as integration with digital libraries, easy implementation, and transfer validation. See `the Wikipedia article `__ for more information.
+
+If the Dataverse installation you are using has enabled BagIt file handling, when uploading BagIt files the repository will validate the checksum values listed in each BagIt’s manifest file against the uploaded files and generate errors about any mismatches. The repository will identify a certain number of errors, such as the first five errors in each BagIt file, before reporting the errors.
+
+|bagit-image1|
+
+You can fix the errors and reupload the BagIt files.
+
+More information on how your admin can enable and configure the BagIt file handler can be found in the :ref:`Installation Guide `.
+
.. _file-handling:
File Handling
@@ -211,6 +228,72 @@ Finally, automating your code can be immensely helpful to the code and research
**Note:** Capturing code dependencies and automating your code will create new files in your directory. Make sure to include them when depositing your dataset.
+Computational Workflow
+----------------------
+
+Computational Workflow Definition
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Computational workflows precisely describe a multi-step process to coordinate multiple computational tasks and their data dependencies that lead to data products in a scientific application. The computational tasks take different forms, such as running code (e.g. Python, C++, MATLAB, R, Julia), invoking a service, calling a command-line tool, accessing a database (e.g. SQL, NoSQL), submitting a job to a compute cloud (e.g. on-premises cloud, AWS, GCP, Azure), and execution of data processing scripts or workflow. The following diagram shows an example of a computational workflow with multiple computational tasks.
+
+|cw-image1|
+
+
+FAIR Computational Workflow
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The FAIR Principles (Findable, Accessible, Interoperable, Reusable) apply to computational workflows (https://doi.org/10.1162/dint_a_00033) in two areas: as FAIR data and as FAIR criteria for workflows as digital objects. In the FAIR data area, "*properly designed workflows contribute to FAIR data principles since they provide the metadata and provenance necessary to describe their data products, and they describe the involved data in a formalized, completely traceable way*" (https://doi.org/10.1162/dint_a_00033). Regarding the FAIR criteria for workflows as digital objects, "*workflows are research products in their own right, encapsulating methodological know-how that is to be found and published, accessed and cited, exchanged and combined with others, and reused as well as adapted*" (https://doi.org/10.1162/dint_a_00033).
+
+How to Create a Computational Workflow
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+There are multiple approaches to creating computational workflows. You may consider standard frameworks and tools such as Common Workflow Language (CWL), Snakemake, Galaxy, Nextflow, Ruffus or *ad hoc* methods using different programming languages (e.g. Python, C++, MATLAB, Julia, R), notebooks (e.g. Jupyter Notebook, R Notebook, and MATLAB Live Script) and command-line interpreters (e.g. Bash). Each computational task is defined differently, but all meet the definition of a computational workflow and all result in data products. You can find a few examples of computational workflows in the following GitHub repositories, where each follows several aspects of FAIR principles:
+
+- Common Workflow Language (`GitHub Repository URL `__)
+- R Notebook (`GitHub Repository URL `__)
+- Jupyter Notebook (`GitHub Repository URL `__)
+- MATLAB Script (`GitHub Repository URL `__)
+
+You are encouraged to review these examples when creating a computational workflow and publishing in a Dataverse repository.
+
+At https://workflows.community, the Workflows Community Initiative offers resources for computational workflows, such as a list of workflow systems (https://workflows.community/systems) and other workflow registries (https://workflows.community/registries). The initiative also helps organize working groups related to workflows research, development and application.
+
+How to Upload Your Computational Workflow
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+After you :ref:`upload your files `, you can apply a "Workflow" tag to your workflow files, such as your Snakemake or R Notebooks files, so that you and others can find them more easily among your deposit’s other files.
+
+|cw-image3|
+
+|cw-image4|
+
+How to Describe Your Computational Workflow
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Dataverse installation you are using may have enabled Computational Workflow metadata fields for your use. If so, when :ref:`editing your dataset metadata `, you will see the fields described below.
+
+|cw-image2|
+
+As described in the :ref:`metadata-references` section of the :doc:`/user/appendix`, the three fields are adapted from `Bioschemas Computational Workflow Profile, version 1.0 `__ and `Codemeta