Monthly Archives: March 2013

Minecraft Activities for Younger Kids

So earlier today Thayer asked if I had any suggestions for things to do in Minecraft for younger kids. I thought I might as well write this up as a blog post rather than a long email, then everyone else can add their suggestions too.

Of course, the first thing I did was turn to the experts: my kids. Both my son and daughter have been addicted to Minecraft for some time now. We run our own home server and this gets visits from my son’s friends too. They also play on-line, but having a local server gives us a nice safe environment where we can play with the latest plugins and releases.

So most of the list below was suggested by the kids. I’ve just written it up and added in the links. We’ve assumed that Thayer and Nemi (and you) are playing on the “vanilla” (i.e. out of the box, un-patched) PC version of Minecraft. The browser, XBox, mobile and the Raspberry Pi versions lag behind a little bit so not all of these activities or options might be available.

There’s also a lot more fun to be had exploring many of the amazing mods, maps and mod-packs (bundled packages of mods) that the Minecraft community has shared. The kids are currently obsessed with the Voids Wrath mod pack which has some excellent RPG features. But we decided to focus on the vanilla experience first.

Be Creative

There are lots of ways to play Minecraft. You can opt to build things, undertake survival challenges, explore above and below ground, and push the limits of its sandbox environment in lots of different ways.

But for younger kids we thought it would be best to play on Creative and Peaceful mode. Creative mode removes the survival challenge aspects, allowing you to focus on building and exploration. In Creative mode you have instant access to every block, tool, weapon, etc. in the game. So you can cut to the chase and start building, rather than undertaking a lot of mining and exploring at the start. Peaceful mode disables all of the naturally spawning enemies, meaning you don’t have to worry about being killed by skeletons, exploded by creepers or going hungry whilst travelling.

The Creative mode inventory system can be a little daunting: there are a lot of different blocks in the game. But if you use the compass to activate the search, you can quickly find what you’re looking for. The list of blocks is on the wiki, so that can also be a handy reference.

Build a House

Building a house is obviously the first place to start. Building and decorating a house gives you a base of operations for all your other activities.

There are lots of material to choose from, including varieties of stone, wood, and even glass. Use different coloured wools to make carpets, and then add book shelves, paintings, beds (right click to sleep in it), a jukebox, and lots of other types of furniture.

The minecraft wiki has a good starting reference for building types of shelter

Become a farmer

Once you’ve built a house, you can next turn your hand to a spot of gardening and become a farmer.

Using a hoe, some water, some earth and the right kinds of seeds you can grow all kinds of things, including Wheat, Carrots, Cacti, Melons and even mushrooms. You can even grow giant mushrooms for that fairy tale feel.

You can use a bucket to carry water around to water your garden or to create a pool.

Build a Zoo

You’re not limited to plants. Minecraft has a lot of different types of animals, including pigs, sheep, cows, wolves and ocelots(?!). In Minecraft every animal can be hatched from an egg (unless you breed them).

In Creative mode you have access to Spawn Eggs which you can use to spawn animals. So build you zoo enclosures and then fill them with whatever animals you like.

Tame animals for Pets

Wolves and ocelots can be tamed to make pets. You can do this if you discover them in the wild or just try taming some animals from your zoo. Feed a wolf a bone and it’ll be come a pet dog. If you want a pet cat, then you’ll want to tame an ocelot with some raw fish. Pets will follow you around and you can make your pet dog sit and stay, or follow you on adventures.

Ride a Pig

While we’re talking about animals, why not give Pig riding a try? You’ll be needing a saddle and a carrot on a stick.

Build something amazing

You’re only limited by your imagination when crafting giant castles, houses, hotels, beach resorts, tree houses, or models of your own home and garden. Using creative mode you’ll have full access to all the tools you need. Moving water and lava around using a bucket you can create some really cool landscapes.

If you need some space then you can create a “Superflat” world, using the advanced options in the game menu. This creates a basic, flat world that gives you plenty of space to build, but you won’t get any of those amazing Minecraft landscapes. Speaking of which…

Fly around the world

Roaming around on foot is great for exploring, but you can’t beat flying. Flying is another bonus of creative mode and it’ll let you quickly explore huge areas to find the best place for your next awesome build. Don’t forget to pull out a map and a compass from your inventory. The map will give you a birds-eye view, while the compass will always point to home (e.g. where you last slept).

Other Ideas

Some other ideas we had:

  • Once you’ve mastered flying, then you could try and create some Minecraft pixel art
  • You might also want to change the skin in your minecraft account. There’s lots of sites and apps that provide tools for building and changing skins to your favourite characters.
  • You could also try out an alternative look-and-feel for Minecraft by installing a texture pack. While installing them will probably need some adult help, once installed its easy to switch between them. Then you make Minecraft look more like Pokemon or maybe just give it a more child friendly feel.

Those were just our ideas. If you’ve got other suggestions for younger kids, or starting player, then feel free to leave a comment below.

What Does Your Dataset Contain?

Having explored some ways that we might find related data and services, as well as different definitions of “dataset”, I wanted to look at the topic of dataset description and analysis. Specifically, how can we answer the following questions:

  • what kinds of information does this dataset contain?
  • what types of entity are described in this dataset?
  • how can I determine if this dataset will fulfil my requirements?

There’s been plenty of work done around trying to capture dataset metadata, e.g. VoiD and DCAT; there’s also the upcoming working on Open Data on the Web. Much of that work has focused on capturing the core metadata about a dataset, e.g. who published it, when was it last updated, where can I find the data files, etc. But there’s still plenty of work to be done here, to encourage broader adoption of best practices, and also to explore ways to expose more information about the internals of a dataset.

This is a topic I’ve touched on before, and which we experimented with in Kasabi. I wanted to move “beyond the triple count” and provide a “report card” that gave a little more insight into a dataset. A report card could usefully complement an ODI Open Data Certificate, for example. Understanding the composition of a dataset can also help support new ways of manipulating and combining datasets.

In this post I want to propose a conceptual framework for capturing metadata about datasets. Its intended as a discussion point, so I’m interested in getting feedback. (I would have submitted this to the ODW workshop but ran out of time before the deadline).

At the top level I think there are five broad categories of dataset information: Descriptive Data; Access Information; Indicators; Compositional Data; and Relationships. Compositional data can be broken down into smaller categories — this is what I described as an “information spectrum” in the Beyond the Triple Count post.

While I’ve thought about this largely from the perspective of Linked Data, I think its applicable to any format/technology.

Descriptive Data

This kind of information helps us understand a dataset as a “work”: its name, a human-readable description or summary, its license, and pointers to other relevant documentation such as quality control or feedback processes. This information is typically created and maintained directly by the data publisher, whereas the other categories of data I describe here can potentially be derived automatically by data analysis

Examples:

  • Title
  • Description
  • License
  • Publisher
  • Subject Categories

Access Information

Basically, where do I get the data?

  • Where do I download the latest data?
  • Where can I download archived or previous versions of the data?
  • Are there mirrors for the dataset?
  • Are there APIs that use this data?
  • How do I obtain access to the data or API?

Indicators

This is statistical information that can help provide some insight into the data set, for example its size. But indicators can also build confidence in re-users by highlighting useful statistics such as the timeliness of releases, speed of responding to data fixes, etc.

While a data publisher might publish some of these indicators as targets that they are aiming to achieve, many of these figures could be derived automatically from an underlying publishing platform or service.

Examples of indicators:

  • Size
  • Rate of Growth
  • Date of Last Update
  • Frequency of Updates
  • Number of Re-users (e.g. size of user community, or number of apps that use it)
  • Number of Contributors
  • Frequency of Use
  • Turn-around time for data fixes
  • Number of known errors
  • Availability (for API based access)

Relationships

Relationship data primarily drives discovery use cases: to which other datasets does this dataset relate? For example the dataset might re-use identifiers or directly link to resources in other datasets. Knowing the source of that information can help us build trust in the reliability of the combined data, as well as give us sign-posts to other useful context. This is where Linked Data excels.

Annotation Datasets provide context to, and enrich other reference datasets. Annotations might be limited to linking information (“Link Sets”) or they may add new facts/properties about existing resources. Independently sourced quality control information could be published as annotations.

Provenance is also a form of relationship information. Derived datasets, e.g. created through analysis or data conversions, should refer to their original input datasets, and ideally also the algorithms and/or code that were applied.

Again, much of this information can be derived from data analysis. Recommendations for relevant related datasets might be created based on existing links between datasets or by analysing usage patterns. Set algebra on URIs in datasets can be used to do analysis on their overlap, to discover linkages and to determine whether one dataset contains annotations of another.

Examples:

  • List of dataset(s) that this dataset draws on (e.g. re-uses identifiers, controlled vocabulary, etc)
  • List of datasets that this datasets references, e.g. via links
  • List of source datasets used to compile or create this dataset
  • List of datasets that link to this dataset (“back links”)
  • Which datasets are often used in conjunction with this dataset?

Compositional Data

This is information about the internals of a dataset: e.g. what kind of data does it contain, how is that data organized, and what kinds of things are being described?

This is the most complex area as there are potentially a number of different audiences and abilities to cater for. At one end of the spectrum we want to provide high level summaries of the contents of a dataset, while at the other end we want to provide detailed schema information to support developers. I’ve previously advocated a “progressive disclosure” approach to allow re-users to quickly find the data they need; a product manager looking for data to support a new feature will be looking for different information to a developer constructing queries over a dataset.

I think there are three broad ways that we can decompose Compositional Data further. There are particular questions and types of information that relate to each of them:

  • Scope or Coverage 
    • What kinds of things does this dataset describe? Is it people, places, or other objects?
    • How many of these things are in the dataset?
    • Is there a geographical focus to the dataset, e.g. a county, region, country or is it global?
    • Is the data confined to a particular data period (archival data) or does it contain recent information?
  • Structure
    • What are some typical example records from the dataset?
    • What schema does it conform to?
    • What graph patterns (e.g. combinations of vocabularies) are commonly found in the data?
    • How are various types of resource related to one another?
    • What is the logical data model for the data?
  • Internals
    • What RDF terms and vocabularies that are used in the data?
    • What formats are used for capturing dates, times, or other structured values?
    • Are there custom validation rules for particular fields or properties?
    • Are there caveats or qualifiers to individual schema elements or data items?
    • What is the physical data model
    • How is the dataset laid out in a particular database schema, across a collection of files, or named graphs?

The experiments we did in Kasabi around the report card (see the last slides for examples) were exploring ways to help visualise the scope of a dataset. It was based on identifying broad categories of entity in a dataset. I’m not sure we got the implementation quite right, but I think it was a useful visual indicator to help understand a dataset.

This is a project I plan to revive when I get some free time. Related to this is the work I did to map the Schema.org Types to the Noun Project Icons.

Summary

I’ve tried to present a framework that captures most, if not all of the kinds of questions that I’ve seen people ask when trying to get to grips with a new dataset. If we can understand the types of information people need and the questions they want to answer, then we can create a better set of data publishing and analysis tools.

To date, I think there’s been a tendency to focus on the Descriptive Data and Access Information — because we want to be able to discover data — and its Internals — so we know how to use it.

But for data to become more accessible to a non-technical audience we need to think about a broader range of information and how this might be surfaced by data publishing platforms.

If you have feedback on the framework, particularly if you think I’ve missed a category of information, then please leave a comment. The next step is to explore ways to automatically derive and surface some of this information.

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