SLKpnu/sequential dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

Following dataset contain series of information that can be used predicting sequence, which is been collected from different vibrations cases such as micro waves signal etc.

Detail Overview

this set consist of two sequences both are in two different files, which have difference of length, and an target file containing true and false status of the sequence.

sequence are mapped between range of 0 to 10000 & target with 1 = true, and 0=false.

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.

An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

## ## The original dataset has been released in three versions of KuaiRand for different uses:

1. KuaiRand-27K (23GB logs +23GB features): the complete KuaiRand dataset that has over 27K users and 32 million videos. Can be downloaded by : wget https://zenodo.org/records/10439422/files/KuaiRand-27K.tar.gz command

2. KuaiRand-1K (829MB logs + 3.5GB features): randomly sample 1,000 users from KuaiRand-27K, then remove all irrelevant videos. There are 4 million videos rest.Can be downloaded by : wget https://zenodo.org/records/10439422/files/KuaiRand-1K.tar.gz command

3. KuaiRand-Pure (184MB logs + 10MB features): only keeps the logs for the 7583 videos in the candidate pool. (Uploaded in this page data)

There are three log files in each version e.g in KuaiRand-Pure:

- log_random_4_22_to_5_08.csv contains all interactions resulting from random intervention.

- log_standard_4_22_to_5_08.csv contains all interactions of standard recommendation.

- log_standard_4_08_to_4_21.csv contains all interactions of standard recommendation for the same users in the previous two weeks (2022.04.08 ~ 2022.04.21).

Complete files and features description in: https://kuairand.com/

How to Use:

1. Reasons to use KuaiRand-27K or KuaiRand-1K: - Your research needs rigorous sequential logs, such as off-policy evaluation (OPE), Reinforcement learning (RL), or long sequential recommendation.

2. Reasons to use KuaiRand-Pure: - The sequential information is not necessary for your research OR If you are OK with the incomplete sequential logs. For example, if you are studying debiasing in collaborative filtering models or multi-task modeling in recommendation. - If your model can only run with small-size data.

Acknowledgment

Chongming Gao et al, 2022. KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

Advantages

Compared with other datasets with random exposure, KuaiRand has the following advantages:

✅ It is the first sequential recommendation dataset with millions of intervened interactions of randomly exposed items inserted in the standard recommendation feeds.

✅ It has the most comprehensive side information including explicit user IDs, interaction timestamps, and rich features for users and items.

✅ It has 15 policies with each catered for a special recommendation scenario in the Kuaishou App.

✅ introduced by 12 feedback signals (e.g., click, like, and view time) for each interaction to describe the user’s comprehensive feedback.

✅ Each user has thousands of historical interactions on average.

✅ It has three versions to support various research directions in recommendation.

Inspiration

Recommender systems suffer from various biases in the data collection stage . Most existing datasets are very sparse and affected by user-selection bias or exposure bias . It is of critical importance to develop models that can alleviate biases. To evaluate the models, we need reliable unbiased data. KuaiRand is the first dataset that inserts the random items into the normal recommendation feeds with rich side information and all item/user IDs provided. With this authentic unbiased data, we can evaluate and thus improve the recommender policy.

KuaiRand can further support the following promising research directions in recommendation.

- Off-policy Evaluation (OPE)

- Interactive Recommendation

- Long Sequential Behavior Modeling

- Multi-Task Learning

More References

- Bias and Debias in Recommender System: A Survey and Future Directions

- "https://arxiv.org/pdf/2308.01118.pdf">A Survey on Popularity Bias in Recommender Systems

consisting of open-source video frames accompanied by story-like annotations.

MIDI CC & NRPN details for Sequential Take 5 from midi.guide, the open and 'comprehensive' MIDI dataset.

This work discusses drinking water sampling efforts for lead in Flint, MI. This dataset is associated with the following publication: Lytle, D., M. Schock, K. Wait, K. Cahalan, V. Bosscher, A. Porter, and M. Deltoral. SEQUENTIAL DRINKING WATER SAMPLING AS A TOOL FOR EVALUATING LEAD IN FLINT, MICHIGAN. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 157: 40-54, (2019).

Early life environments can have long-lasting effects on adult reproductive performance, but disentangling the influence of early and adult life environments on fitness is challenging, especially for long-lived species. Using a detailed dataset spanning over two centuries, we studied how both early and adult life environments impacted reproductive performance in preindustrial women. Due to a wide geographic range, agricultural production was lower in northern compared to southern parishes, and health conditions were worse in urban than rural parishes. We tested whether reproductive traits and offspring survival varied between early and adult life environments by comparing women who moved between different environments during their lifetime with those who moved parishes but remained in the same environment. Our findings reveal that urban-born women had an earlier age at first reproduction and less offspring surviving to adulthood than rural-born women. Moreover, switching from urban to rural led to increased offspring survival, while switching from rural to urban had the opposite effect. Finally, women who switched from rural to urban and from South to North had their first child at an older age compared to those who stayed in the same environment type. Our study underscores the complex and interactive effects of early and adult life environments on reproductive traits, highlighting the need to consider both when studying environmental effects on reproductive outcomes.

Sequential Data Annotation

## Overview

Sequential Data Annotation is a dataset for object detection tasks - it contains Football annotations for 534 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

MIDI CC & NRPN details for Sequential Pro 3 from midi.guide, the open and 'comprehensive' MIDI dataset.

This data set comprises uncalibrated and calibrated data from the Cassini Composite Infrared Spectrometer (CIRS) instrument. The basic data is comprised of uncalibrated raw spectra, along with along with pointing and geometry information, and housekeeping information. Also included are calibrated power spectra, and documentation.

Dataset Summary

This dataset will help you to develop a machine learning-based model to predict the pathogenic variants (Positive labels) by utilizing their amino acid sequences. Used as an example to benchmark biomerida as part of the Bio-Hakathon Mena region

During the study period

Dataset

This dataset was created by Nagesh Singh Chauhan

Contents

The dataset Arabic Digit Sequential Electromyography (ADSE) is acquired for eight-lead sEMG data targeting sequential signals.

Context

Sequential pattern mining is the discovery of subsequences that are frequent in a set of sequences. The process is similar to the frequent itemset mining1 except that the input database is ordered. As the output of a sequential pattern mining algorithm, it generates a set of frequent sequential patterns, which are sub-sequences that have a frequency in the database greater than or equal to the user-specified minimum support. Let the data set shown in Table 1 where events are accompanied by instants of occurrence in each tuple. https://pasteboard.co/JRNB4rH.png" alt="Image of table">

We can note that, for a fixed threshold equal to 1, the pattern < A, B, C > is considered as frequent because its support (the number of occurrences in the database) is equal to 2.

Content

Problematic and Goal:

Let us assume the example given in Table 1. < A, B, C > is considered a frequent sequential pattern. It shows that events A, B, and C occurred frequently in a sequence manner, but without providing any additional information about the gap between them. For instance, we do not know when B would happen, knowing that A already did. Therefore, we ask you to provide a richer pattern where time constraints are considered. In our data set example, we can deduce that A, B, and C occur sequentially, and that B occurs after A at least after one instant and at most after 5 instants, while C occurs after B in the interval [2, 4] of instants. We represent our pattern as A[1,5]B and B[2,4]C. It is a direct graph where nodes are events and vertices are the instant intervals, denoted by time constraints as shown in Figure 1. https://pasteboard.co/JRNBWWL.png" alt="Image">

Formally, Definition (Event) An event is a couple (e,t) where e ϵ Ε is the type of the event and t ϵ Τ is its time. Definition (Sequence) Let E be a set of event types and T a time domain such that T ⊆ R. E is assumed totally ordered and is denoted #

The graph shows the number of articles published in the discipline of ^.

A building's plumbing system and water service line (pipes) can be made up of different types of materials. Each type of material can affect drinking water differently, so it is useful to conduct what is known as "sequential sampling". Sequential sampling is where all water usage in a building is stopped for several hours, known as "stagnation". Next, water is collected from the faucet in a series of bottles. This is done without wasting any water or running the water before filling the bottles. The first few bottles represent water that was in contact with the faucet or building plumbing during stagnation. The later bottles represent water that was in contact with the water service line. These sample results can help decide whether treatment is working. Learn more at Michigan.gov/FlintWater

This data set comprises uncalibrated and calibrated data from the Cassini Composite Infrared Spectrometer (CIRS) instrument. The basic data is comprised of uncalibrated raw spectra, along with along with pointing and geometry information, and housekeeping information. Also included are calibrated power spectra, and documentation.

Applying sequential mixed-methods to an exploratory research design, with seven interlocking stages and data from Fuzzy Delphi experts and tourist surveys in Taipei City, a smart city in Taiwan, this paper proposes a second-order scale with six dimensions, comprising smart services of attractions, transportation, accommodation, diet, purchase, and payment.

A building's plumbing system and water service line (pipes) can be made up of different types of materials. Each type of material can affect drinking water differently, so it is useful to conduct what is known as "sequential sampling". Sequential sampling is where all water usage in a building is stopped for several hours, known as "stagnation". Next, water is collected from the faucet in a series of bottles. This is done without wasting any water or running the water before filling the bottles. The first few bottles represent water that was in contact with the faucet or building plumbing during stagnation. The later bottles represent water that was in contact with the water service line. These sample results can help decide whether treatment is working.

Learn more at Michigan.gov/FlintWater