Omkar2118/datasets huggingface dataset:
BA-2motifs

It's a synthetic dataset, which contains 1000 graphs divided into two classes according to the motif they contain: either a “house” or a five-node cycle.

Introducing paper: Parameterized Explainer for Graph Neural Network
File S1
Full name: Carboxylase properties table

-Tab 1 (Carboxylase table):
This expanded table contains additional information for carboxylase classes and splits them into individual examples.

Notes:
Primary direction is carboxylating if we could find a single natural or even artificial example that runs primarily in that direction.
Thermodynamic data is missing for many classes, equilibrator has a hard time with reducing agents. Values for TPP-containing enzymes may not be extremely reliable.

-Tab 2 (Enolate table):
This is a subset of carboxylases that act on substrates with the general structure in the table

This table specifies the R and X groups for each of the “enolate” enzymes.

Introducing paper: Rubisco function, evolution, and engineering
File S2
Full name: Kinda of Rubisco Table

-Tab 1 Rubisco forms:
This excel sheet contains one row for every rubisco form considered in this review (some forms like IAq and IAc from [31] are not considered separately because they are only phylogenetically separated in the small subunits). For each form we include the pathway in which it functions, the chemical reaction catalyzed - for known types of Form IV RLPs (rubisco-like proteins), an example sequence of the protein and a citation. We also include an example structure if available.


-Tab 2 Reference sequences:
This list of rubisco sequences is the list of references used in this paper to annotate rubisco forms. These sequences were taken from [2,6].

Introducing paper: Rubisco function, evolution, and engineering
File S4
Full name: tree file of all rubiscos at 65% dereplication

This tree was generated as indicated above in the methods. The model chosen by the algorithm was LG_F_R10.

Introducing paper: Rubisco function, evolution, and engineering
File S5
Full name: tree file of Form IV rubiscos (RLPs) at 70% dereplication

This tree was generated as indicated above in the methods. The model chosen by the algorithm was LG+F+R8.

Introducing paper: Rubisco function, evolution, and engineering
File S6
Full name: tree file of Form I rubiscos at 85% dereplication

This tree was generated as indicated above in the text for file S3 using model LG+F+G.

Introducing paper: Rubisco function, evolution, and engineering
File S7
Full name: tree file of Form I rubiscos at 85% dereplication LG+R8

This tree was generated as indicated above in the methods. The model chosen by the algorithm was LG+R8.

Introducing paper: Rubisco function, evolution, and engineering
EGFxSet
Full name: Electric Guitar Effects Dataset, ISMIR 2022 LBD

EGFxSet (Electric Guitar Effects dataset) features recordings for all clean tones in a 22-fret Stratocaster, recorded with 5 different pickup configurations, also processed through 12 popular guitar effects.  Our dataset was recorded in real hardware, making it relevant for music information retrieval tasks on real music.  We also include annotations for parameter settings of the effects we used.

EGFxSet is a dataset of 8,970 audio files with a 5-second duration each, summing a total time of  12 hours and 28 minutes.

All possible 138 notes of a standard tuning 22 frets guitar were recorded in each one of the 5 pickup configurations, giving a total of 690 clean tone audio files ( 58 min ).

The 690 clean audio (58 min) files were processed through 12 different audio effects employing actual guitar gear (no VST emulations were used), summing a total of 8,280 processed audio files (11 hours 30 min).

The effects employed were divided into four categories, and each category comprised three different effects. Some gear used supported the generation of more than one effect setting (but only one was recorded at a time).

Categories, Models and Effects: Distortion (Blues Driver, Tube Screamer, RAT2  Distortion), Modulation (Chorus, Phaser, Flanger), Delays (Digital Delay, Tape Echo, Sweep Echo), Reverb (Plate Reverb, Hall Reverb, Spring Reverb).

Annotations are labeled by a trained electric guitar musician. For each tone, we provide: Guitar string number, Fret number, Guitar pickup configuration, Effect name, Effect type, Hardware modes, Knob names, Knob types, Knob settings).

The dataset website is: https://egfxset.github.io/

The data can be accessed here: https://zenodo.org/record/7044411#.YxKdSWzMKEI

An ISMIR extended abstract was presented in 2022: https://ismir2022.ismir.net/program/lbd/

This dataset was conceived during Iran Roman's "Deep Learning for Music Information Retrieval" course imparted in the postgraduate studies in music technology at the UNAM (Universidad Nacional Autónoma de México). The result is a combined effort between two UNAM postgraduate students (Hegel Pedroza and Gerardo Meza) and Iran Roman (NYU).
SymphonyNet
Full name: SymphonyNet

First large-scale symphony generation dataset.

Introducing paper: Symphony Generation with Permutation Invariant Language Model
ValNov Subtask A

Binary labels for Validity and Novelty respectively are given for each Conclusion.

Introducing paper: Overview of the 2022 Validity and Novelty Prediction Shared Task
ValNov Subtask B

Validity and Novelty are determined in a comparative setting between two conclusions at a time. For Validity and Novelty possible labels are "Conclusion 1 is better", "tie" and "Conclusion 2 is better", for Validity and Novelty respectively.

Introducing paper: Overview of the 2022 Validity and Novelty Prediction Shared Task