• in query form say we use the default but more can be found using the service info and topcat/pyvo.

Introduction

This page desribes describes access to the data in ASTRON's data holdings using the Virtual Observatory standards. Those standards support access to data like catalogs and images. We introduce the most important standards on this page and give some usage exaples taylored examples tailored to the ASTRON data holdings. If you want more advanced information on how to use the VO standards, you can start with the IVOA website for astronomers and the IVOA wiki page on educational resources. For our data holdings we use the DAta Center Helper Suite (DACHS), which offers a web interface of its own. We will link to several places in DACHS in the documentation but in principle all information showed by DACHS can also be accessed using any VO tools.

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The VO consists of several different protocols. In this section, we describe the import ant important protocols, and provide the acronyms that are generally used in the VO tooling. Most data in vo.astron.nl consist of tables, that can be accessed using the Tabular Access Protocol (TAP). Images are offered using the Simple Image Access Protocol (SIA) which in essence is a table with a field containing a link to an image. A few services offer cutout fuctionality functionality (using SODA). A cutout is essentially a service that returns an image (or more generally a data product) that only contains the data inside the cone that has been requested by the user.

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The data published in the VO can also be accessed using a web browser at https://vo.astron.nl. The web interface can be used to perform simple queries. The power of the VO lies in the fact that several applications exist that can interact with the standards we offer. In the architecture, there is a distinction between services and tables. A service is an entity that takes user input and provides a table as output. The tables themselves are the entities that actually hold the data. Tables can also be queried directly, for instance using ADQL. The colomns In the following table, we list the services for accessing data collections in the ASTRON VO. The columns of the table are:

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This web interface provides a page on which all the collections present in the registry are listed.

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The DaCHS system comes with a web view on the services offered. Even though the interface is somewhat rudimentary, it does offer the relevant functionality to query data in the ASTRON VO. The main page, listing all services (as listed in the table above) can be fount found at https://vo.astron.nl . Specific services are linked to that (either by clicking on the name of the collection, or the Q icon next to it.

Selecting a data collection allows the user to perform a cone search through a webform (Fig. 1). The result is either a catalogue (list of sources, Gaussians or visibilities), or a table of data products (images, cubes) of that given class overlapping a given pointing. In some cases, the number of query parameters can differ, in which case the field description explains what the data is that can be submitted to them.

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The result is a table in the requested output format in which every row corresponds to a data product (Fig. 2). In each row there is a column, Product key, which is a link that allows the user to download the fits file of the image of interest. The column titles should generally be self-descriptive. However, the long human-readable description of the content of each column is a tooltip that will appear when hovering over the column name. The result is a table (like Fig. 2) which consists of a link to the data product, a thumbnail image  image that appears when hovering it (both only if querying for a data product) and relevant metadata describing the results. The Quick Plot button on top of the results page can be used to quicikly quickly plot the numerical results (e.g. making an RA/DEC overview of the images or sources). The Send via SAMP button can be used to send the result set to an application using the SAMP protocol. This essentially means that if you have another application open which supports the SAMP protocol (as do both TOPCAT and Aladin), clicking this button will make the table appear in those applications.

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One special service is the ADQL query service, shown on Fig 4.. This contains a single box that can be used to provide custom queries using the Astronomical Data Query Language (ADQL), which in essence is a dialect of SQL. Some more information on, and examples of ADQL can be found further below.

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The VO data collections can easily beThe Apertif DR1 data collection tables can be accessed using TOPCAT, an interactive graphical viewer and editor for tabular data. Since the ASTRON VO is regsitered registered to the so-called registry of registries, TOPCAT can find the relevant services in its query menus. In the VO menu of TOPCAT you can pick the SIA query menu item, which will bring up a form as shown in Fig. 5. When selecting one of the data collections the SIA URL will be automatically filel filled in. One can then either type coordinates, or an object name and click the "Resolve" button which uses Simbad to obtain coordinates for that obiectobject. When clicking OK, a table of the data products found will appeared appear in the main TOPCAT window. Alternatively, the table can be saved in various formats and used locally with other programs (e.g. python scripts etc).

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One of the most popular tools to access data through the VO is Aladin Sky Atlas. There are both a browser and standalone desktop edition. When refering referring to Aladin in the following, this will apply to the desktop edition. Aladin is a tool that makes it possible to visualise tables and images and perform some analysis on them.

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The SIA and TAP services are also available directly from Aladin. First of all, load a HiPS (any HiPS will do;  but it may be useful to use the HiPS from the same data release).  To find the images, you can select Others → SIA → astron.nl → <data collection name> (purple directory icons). When double-clicking the collection (or clicking t he the Load button) the popup screen. On top of the HiPS image, small shapes will appear that each represent the central pointing of each of the images (see Fig. 10; left). Note that the default colour of the shapes is red but the colour in the screenshot has been changed for contrast.  When clicking one of the shapes the meta data of the selected object appears in a table below the figure and clicking on the image URL will then initiate a download of the original image, superimposing it on top of the HiPS (Fig. 10; right). Note that the stack on the right of the Aladin screen shows all loaded layers and that the layer in which the image was loaded is showing the percentage of the image loaded.

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Access to catalogues through TAP is handled in a way comparable to images through SIA. Open ALADIN and on the left panel for SIAP: select Others → CS → astron.nl → <data collection name> (purple directory icons). A pop-up window will open. Click load. Note that "in view" is clicked bu default, meaning that the source in view are being loaded by default. Also note that if the Field of View is too large, the loading may fail because of a overflow on the server side (ie the query resulted in too many results). After loading, each of the catalogue entries is superimposed on top of the HiPS image as, again, a small red circle (and again this coulour colour has been changed in the screen shot screenshot for contrast). You can then click the "select" button and select several sources (by drawing a rectangle around them) and the table of properties will appear beneath the HiPS. See Fig. 11 for a view of those steps.

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In several places, ADQL was mentioned as a way to construct queries to the data and catalogues. ADQL is a query language resembling SQL, so the main concepts are familiar to those acquianted acquainted with SQL. The full  reference documentation (i.e. stnadard standard definition) contains all possible commands. Here we show some simple examples of table queries. You can perform queries using ADQL on the ADQL form (see Fig. 4), or for instance in TOPCAT (see Fig. 6). Also note that TOPCAT has some built in examples implemented. Even though the ADQL commands are case insensitive, it is

In the following examples, we will follow the common practice to write them SQL commands in upper case so that table data and variable values can easily been distinguished from commands, and names of objects (tables, columns, etc) in lower case letters. The SQL commands are however case insensitive so you are totally free to for instance write select instead of SELECT.

Basic examples

The first example is simply selected all items from the lcbsw.main table:

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With everything we learned till now, we can start constructing complex queries. One good example of thjis this is the query to obtain all the data in Apertif DR1. This query is specific to the continuum_images data product but can be adapted to other (beam-based, processed) data products by replacing the table name, e.g., for polarization cubes/images use pol_cubes.

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