Intro

Hi 👋,

As a fanatic climber and a junior data engineer, I see that there is a lack of climbing datasets for all the people like us that want to learn and play around with our passion. With that in mind I uploaded this 3 tables.

It is based on the original dataset scrapped from 8a.nu by David Cohen (https://www.kaggle.com/datasets/dcohen21/8anu-climbing-logbook). Please refer to that dataset if you have any questions on this one.

Use of this dataset

Feel free to use this dataset as you please, just dont forget to mention the source.

Contents

Grading table: grades_conversion_table.csv

This table gives you the conversion from numbered to french grading.

Routes table: routes_rated.csv

name_id -> the route id I cleaned the ascensions table and gave some shape so you dont have to worry about problems like having 10 different names for the same route or crag.

grade_mean -> mean of all ascensions I changed a little bit the ascension grading: if someone stated that a route was hard 7a, then I put a 7a/+, same with soft grading. After that I calculated the median for all the grading of each route (more robust with outlayers)

rating_total -> I did this calculation based on 3 features and taking the first component of the PCA: - comment sentiment - rating - recomendations

tall_recommend_sum -> For each rute I am adding up the following: - if the person is tall and consider route easy +1 - if the person is tall and consider route hard -1 - if the person is short and consider route easy -1 - if the person is short and consider route hard +1 (considering tall > 180cm, short < 170cm)

cluster -> I clustered the routes in 9 different clusters that can be more or less identified like: 0 - Soft routes 1 - Routes for some reason preferred by women 2 - Famouse routes 3 - Very hard routes 4 - Very repeated routes 5 - Chipped routes, with soft rate 6 - Traditiona, not chipped routes 7 - Easy to On-sight routes, not very repeated 8 - Very famouse routes but not so repeated and not so traditional

Climbers table: climber_df.csv

date_first -> date of the first ascension date_last -> date of the last ascension grades_first -> grade of the first ascension grades_last -> grade of the last ascension years_cl -> years climbing grades_count -> number of routes done by climber year_first -> year of the first ascension year_last -> year of the last ascension

How to obtain this data

If you want to see how I obtained these 3 tables from the raw data, please check my github repos at:

Climber table -> https://github.com/jordi-zaragoza/Climbing-Data-Analysis/blob/master/src/1.Project_clean.ipynb Routes table -> https://github.com/jordi-zaragoza/Climbing-Route-Recommender/blob/master/src/1.get_routes_table.ipynb

Acknowladgement

Thanks to David Cohen (https://www.kaggle.com/datasets/dcohen21/8anu-climbing-logbook).

43 Global import shipment records of Climb X with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset contains the predicted prices of the asset Climb Race Base over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Try model here: https://genclimb.pages.dev/

  Kilter Board Climbing Dataset

This dataset contains climbing sequences for the Kilter Board, a popular adjustable climbing wall. It includes climbs that meet specific criteria, along with vocabulary mappings.

  Dataset Characteristics

Minimum Ascensionists: 5 Board Layouts: Kilter Board Original and Kilter Board Homewall Quality Rating: Greater than 2.6 Frames Count: 1

  Data Format

Each sample in the dataset is… See the full description on the dataset page: https://huggingface.co/datasets/stfamod/Kilter-Board-Dataset.

As of August 2022, the approximate number of mountaineers to have scaled K2 worldwide was ***. Meanwhile, ** climbers have died while trying to scale the world's second-highest mountain above sea level. Furthermore, the average cost to climb K2 was ** thousand U.S. dollars.

Dataset

This dataset was created by abdelbasset ben kerrouche

Contents

This dataset contains 29,278 gameplay frames from the Countryside level of Hill Climb Racing, captured for training an AI driving model using supervised learning.

Each frame is labeled with one of three possible driving actions:

File Contents

hcr_dataset.npz — a compressed NumPy archive containing:

X → array of shape (29278, 64, 64, 3) — RGB gameplay frames

y → array of shape (29278,) — integer labels representing actions

Purpose

This dataset was collected to train a Convolutional Neural Network (CNN) model capable of predicting the next driving action based on the current visual state of the game.

It can also be useful for:

Computer vision research on gaming environments

Behavioral cloning experiments

Reinforcement learning imitation studies

Example Usage

import numpy as np

data = np.load("hcr_dataset.npz")

X, y = data["X"], data["y"]

print(X.shape, y.shape)
(29278, 64, 64, 3) (29278,)

Notes

1. All frames were captured directly from gameplay.
2. The dataset focuses only on the Countryside level.
3. Labels correspond to player key inputs during recording.
4. UI elements such as pedals, fuel bars, score, and coins were masked (blacked out) to prevent visual bias and improve model generalization.

This dataset contains the predicted prices of the asset The Climb over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Context

A year ago i had the opportunity to Climb this beautiful Mountain. Its a 15000 feet majestic beast looking just stands out from Seattle Washington. I was lucky enough to be part of a a great team to be able to successfully reach the summit. The weather was perfect the day we climbed. More often Weather plays a major role in deciding the summit success. Here is an attempt to use the historical weather data and the climbing records to analyse and predict the summit success given the weather details on a given day.

Content

The data contains Date, the various weather parameters averaged daily, the climbing statistics and the target the success percentage.

The weather has been captured from https://www.nwac.us and the climbing statistics from http://www.mountrainierclimbing.us/routes I could match the weather to the climbing records only for the period 2014 and 2015. I coudnt get the weather report for earlier than 2014.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

The one single motive and objective of this dataset is to predict the rate of success of the climb given the route and the weather condition of a day.

Stock Price Time Series for Climb Global Solutions. Climb Global Solutions, Inc. operates as a value-added information technology (IT) distribution and solutions company in the United States, Canada, Europe, and the United Kingdom. It operates through two segments, Distribution and Solutions. The company distributes technical software to corporate and value-added resellers, consultants, and systems integrators under the name Climb Channel Solutions; and provides cloud solutions and resells software, hardware, and services under the name Grey Matter. It also resells computer software and hardware developed by others, as well as provides technical services to end user customers. In addition, the company offers a line of products from various software vendors; and tools for virtualization/cloud computing, security, networking, storage and infrastructure management, application lifecycle management, and other technically sophisticated domains, as well as computer hardware. It markets its products through its own web sites, local seminars, events, webinars, and social media, as well as direct email and printed materials. The company was formerly known as Wayside Technology Group, Inc. and changed its name to Climb Global Solutions, Inc. in October 2022. Climb Global Solutions, Inc. was incorporated in 1982 and is headquartered in Eatontown, New Jersey.

This dataset provides information about the number of properties, residents, and average property values for Hill Climb Road cross streets in Stanley, VA.

Non-traditional data signals from social media and employment platforms for CLYM stock analysis

Climate change is forcing species to shift their distribution ranges. Animal seed dispersers might be particularly important in assisting plants tracking suitable climates to higher elevations. However, this role is still poorly understood due to a lack of comprehensive multi-guild datasets along elevational gradients. We compiled seed dispersal networks for the five altitudinal vegetation belts of the Tenerife Island (0 - 3,718 m a.s.l.) to explore how plant and animal species might facilitate the mutual colonisation of uphill habitats under climate change. The overall network comprised 283 interactions between 73 plant and 27 animal species, with seed dispersers offering viable pathways for plants to colonise upper vegetation belts. A pivotal role is played by a lizard (Gallotia galloti) as island-level hub, while four birds (Turdus merula, Erithacus superbus, Corvus corax, Falco tinnunculus) and one introduced mammal (Oryctolagus cuniculus) are also important connectors between belts. Eleven plant species were found to be actively dispersed to elevations beyond their current known range, with observed vertical dispersal distances largely surpassing those required to escape climate change. Alarmingly, over half of the plants arriving at higher elevations were exotic. Functionally diverse disperser communities are crucial for enabling plants tracking climate change on mountains, but exotic plants might particularly benefit from this upward lift.

After INSPIRE transformed development plan “Behind the slope extension (2nd change)” of the city of Owen based on an XPlanung dataset in version 5.0.

After INSPIRE transformed development plan “Behind the slope (2nd change)” of the city of Owen based on an XPlanung dataset in version 5.0.

Camp And Climb Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The Kilterboard is a standardized training tool for climbers. It is a wall with an adjustable angle and screwed on holds with LEDs which allows users to create their own climb and comes in various sizes.

Note that various holds in each image are circled which represent which holds are allowed / make up a particular climb. These holds are further differentiated by color: green represents the starting holds, yellow represents a foothold, blue represents a handhold and purple represents the finishing hold. A climb begins at the starting hold and ends at the finishing hold(s). A foothold can only be used by a foot while a handhold can be used by both a hand and foot.

Image data of kilterboard climbs ranging from V1 to V10 grades with the angle fixed at 40 degrees. Dimensions of images are 258 x 283. Images were scraped from the climbdex.fly.dev website. Files are labeld with both the font grade (5a, 5b, etc.) as well as the vgrade. The files are numbered in order of appearance though there are gaps due to bugs in the scrapping process as well as manually removing some outlier climbs.

I Climb Safety Co Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This dataset provides information about the number of properties, residents, and average property values for Hawks Climb Lane cross streets in Montpelier, VA.

Building plan “Auf der Steig Cannstatt Plan 3” of the city of Stuttgart based on an XPlanung dataset in version 5.0.