> For the complete documentation index, see [llms.txt](https://dots.gitbook.io/dots-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dots.gitbook.io/dots-docs/project-prep/data-import.md).

# Data Import

**Why Use Direct Import?**\
When starting a new project on Dots, importing a pre-prepared Excel sheet lets you populate your database fast, enabling you to import hundreds and thousands of rows in one go and start analysis in no time.&#x20;

**Start Importing Data - Steps to Follow**\
In the Datasets tab, click on the 'Import New Dataset' button and upload CSV sheet from your device.

<figure><img src="/files/3GrG1rSCI7P8fZhB90AH" alt=""><figcaption></figcaption></figure>

**Verify the Data Mapping**\
After uploading data, the system will automatically map it to the relevant fields. You’ll then be able to preview how the data will appear once published, and you can change the field type as needed.

<figure><img src="/files/Z8TvYnohCNhYNjFu73Nl" alt=""><figcaption></figcaption></figure>

### **Different field types**

<table data-search="false"><thead><tr><th valign="top">Field type name</th><th valign="top">Description</th></tr></thead><tbody><tr><td valign="top">Document Title</td><td valign="top">Use this field type to uniquely identify each document in your dataset.</td></tr><tr><td valign="top">Date</td><td valign="top">Use this field to add date values. Dates can be analyzed over time and used in time-series visualizations.</td></tr><tr><td valign="top">Text</td><td valign="top">Use this field type for short pieces of text, such as titles, labels, or brief notes. Best suited for concise content of around 10 to 20 words which does not require highlighting.</td></tr><tr><td valign="top">Rich Text Field</td><td valign="top">Use this field type for longer qualitative content like transcripts, summaries, or long responses. Rich Text supports highlighting and is ideal for deeper analysis.</td></tr><tr><td valign="top">Numbers</td><td valign="top">Use this field type for numeric values. Numbers are analyzed within relevant ranges for calculations and comparisons.</td></tr><tr><td valign="top">URL</td><td valign="top">Use this field type to store external or reference links related to your data.</td></tr><tr><td valign="top">Single Select</td><td valign="top">Use this field type when one option applies for each entry.</td></tr><tr><td valign="top">Multiple Select</td><td valign="top">Use this field when more than one option applies for each entry using a chosen separator.</td></tr><tr><td valign="top">Single Tag</td><td valign="top">Use this field type to assign one tag to each entry. Tags help organize, filter, and connect data across datasets and form the basis of data connectors.<br><br>This field type requires data connection to be set up with an existing tag category, if present.</td></tr><tr><td valign="top">Multiple Tag</td><td valign="top">Use this field type to assign multiple tags to each entry using a chosen separator. Tags help organize, filter, and connect data across datasets and form the basis of data connectors.<br><br>This field type requires data connection to be set up with an existing category, if present.</td></tr></tbody></table>

### **How to set up Data Connectors with Single / Multi Tag field types**

The **Data Connectors** feature enables users to connect different datasets using consistent tags. Allowing **cross-dataset analysis** and filter data from multiple datasets simultaneously to ease your search.&#x20;

**Step 1: Upload Dataset 1**

1. Upload your first dataset to the platform.
2. For any column you plan to use for connections, set its **field type** to **Single Tag** or **Multi Tag**.
3. Import the data. This creates a tag-category based on the values in that column.

<figure><img src="/files/DOhVkYgQVrg8iaKD0zM8" alt=""><figcaption></figcaption></figure>

**Step 2: Upload Dataset 2**

1. Upload your second dataset.
2. For the header/column you want to link to the first dataset, again set its field type to **Single Tag** or **Multi Tag**.
3. In the "Connect with Existing Tag Category" dropdown, choose the entire dataset if you want all fields or select an individual field for a specific filter.
4. Import the data.

<figure><img src="/files/HHiCDawr5k5G1xSUHMCA" alt=""><figcaption></figcaption></figure>

Once complete, the values in that column from both datasets will be unified under a common tag category, enabling cross-dataset filtering and analysis.

### **Using Separators for Multi-Tag / Multi-Select Field**

A separator between tags or options helps you separate two or more options added in a single cell. Using any special character, you can split the two-option added in one cell and register on the platform as two data points.&#x20;

**Example:** Let's say you have a dataset with a column named **Safe Sanitation Awareness Campaigns**. In that column, you may want to indicate more than one campaign name. Using a special separator (e.g. $) allows the system to record multiple values from a single cell.

| User ID | Campaigns                                                                  |
| ------- | -------------------------------------------------------------------------- |
| 1       | Behavior Change Communication (BCC) via Mass, Mobile **&** Community Media |

<figure><img src="/files/w9i1VehT5WaTkED3t2bg" alt=""><figcaption></figcaption></figure>

**Upload data on the platform**

Once you've reviewed the data you click on 'Confirm Import' to have your data on the platform.

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