Resources used to split Super Mario Bros. levels into succcessive "scenes" Each level map was first obtained from NesMaps and added to the mario_scenes_manual_annotations.pdf file. Using the stable-retro GUI, we obtained the X positions corresponding to the start and end of each scene. Then for each scenes, we identified game design patterns as described in Dahlskog & Togelius, 2012. We aggregated all these informations in the scenes_mastersheet.tsv file. Note : Underwater and Castle levels were ignored in our analysis because they have a slightly different gameplay than the regular level, and make use of different game design patterns. Bonus zones and water sections are annotated as such, but their patterns weren't identified. The scenes mastersheet This TSV file contains informations related to all the scenes identified in Super Mario Bros. levels. It contains one row per scene, 3 columns to identify the scene, an Entry and Exit point columns, and one columns per pattern. These columns contain the following information : - World : The world ID, an integer between 1 and 8.- Level : The level ID, an integer between 1 and 3.- Scene : The scene ID, an integer.- Entry point : The X position corresponding to the beginning of the scene. An integer.- Exit point : The X position corresponding to the ending of the scene. An integer. Design patterns (from Dahlskog & Togelius 2012). The values can be 0 (absence of the corresponding pattern) or 1 (presence of the corresponding pattern) : - Enemy : A single enemy- 2-Horde : Two enemies together- 3-Horde : Three enemies together- 4-Horde : Four enemies together- Roof : Enemies underneath a hanging platform making Mario bounce in the ceiling- Gap : Single gap in the ground/platform- Multiple gaps : More than one gap with fixed platforms in between- Variable gaps : Gap and platform width is variable- Gap enemy : Enemies in the air above gaps- Pillar gap : Pillar (pipes or blocks) are placed on platforms between gaps- Valley : A valley created by using vertically stacked blocks or pipes but without Piranha plant(s)- Pipe valley : A valley with pipes and Piranha plant(s)- Empty valley : A valley without enemies- Enemy valley : A valley with enemies- Roof valley : A valley with enemies and a roof making Mario bounce in the ceiling- 2-Path : A hanging platform allowing Mario to choose different paths- 3-Path : 2 hanging platforms allowing Mario to choose different paths- Risk/Reward : A multiple path where one path have a reward and a gap or enemy making it risky to go for the reward- Stair up : A stair going up- Stair down : A stair going down- Empty stair valley : A valley between a stair up and a stair down without enemies- Enemy stair valley : A valley between a stair up and a stair down with enemies- Gap stair valley : A valley between a stair up and a stair down with gap in the middle We added several patterns in order to annotate key sections of the level : - Reward : Rewards without immediate danger- Moving platform : Platform moving vertically or horizontally- Flagpole : End of the level- Beginning : Beginning of the level- Bonus zone : Hidden zone without enemies- Waterworld : A special hidden zone with Waterworld gameplay

The public rights of way information shown on this system has been copied from the Definitive Map of Public Rights Of Way, the legal record of public rights of way - footpaths, bridleways and byways open to all traffic. The digitised rights of way information is updated regularly to record changes to the rights of way network. The map has been produced to assist you in your visits to the Countryside. It is not a legal record. Some public rights of way may exist which are not shown on the Definitive Map.

Credit report of Mario contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Collection description

None

Subject species

homo sapiens

Modality

fMRI-BOLD

Cognitive paradigm (task)

instrumental learning task

Map type

F

Credit report of Mario De La Guardia contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Location of bus stops. This data covers bus stops in Lancashire, Blackburn with Darwen, and Blackpool (including Blackpool tram stops).

Census Lower Super Output Area boundaries as of 2011, with indices of multiple deprivation statistics (2019)

Data Archive to accompany: "Maps of solar wind plasma precipitation onto Mercury’s surface: a geographical perspective." Federico Lavorenti, Elizabeth A. Jensen, Sae Aizawa, Francesco Califano, Mario D’Amore, Deborah Domingue, Pierre Henri, Simon Lindsay, Jim M. Raines, and Daniel Wolf Savin. Submitted 2023 May to Planetary Science Journal.

The files contained in this archive comprise the values shown in Figures 3 & 5.

Assault with Less Serious Injury offences for the period 01/06/2024 to 31/05/2025 by ward, shown as the rate per 1000 population.

Serious Violent Crime offences for the period 01/06/2024 to 31/05/2025 by ward, shown as rate per 1000 population

Locations of hospitals in Lancashire.

These are the first texture maps of Colombia, obtained from national and global digital soil mapping products. The maps were developed at five standard depths (0-5, 5-15, 15-30, 30-60, and 60-100 cm) and standardized with Additive log-ratio (ALR) transformation. The maps were harmonized at 1 square km of spatial resolution. The data packages include the following set maps: texture maps obtained through the Ensemble Machine Learning (EML) algorithms called landmap and MACHISPLIN; texture maps obtained from SoilGrids platform; residual maps of the texture of the algorithms referenced above; and finally texture maps obtained through spatial ensemble technique.

This dataset was developed within an analysis of research data generated and managed within the University of Bologna, with respect to the differences and commonalities between disciplines and potential challenges for institutional data support services and infrastructures. We are primarily mapping the type (e.g., image), content (e.g., scan of a manuscript) and format (e.g., .tiff) of managed data, thus sustaining the value of FAIR data as granular resources.

Credit report of Mario Eimuth contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

A new version has been uploaded by Guillermo Schulz.

The dataset originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).

Road works, both in progress and planned, in Lancashire.

Credit report of Mario Jpireslda contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Data processing in RStudio for soil moisture and Gross Primary Productivity trends across the Avocado “Green Gold” Belt in central Mexico (2001-2018).

Credit report of Mario Silver contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.