cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8wp73zxr47.12
title: Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data 

description: With this collection of code and configuration files (contained in "LMIF" = 'Learned Metric Index Framework'), outputs ("output-files") and datasets ("data") we set out to explore whether a learned approach to building a metric index is a viable alternative to the traditional way of constructing metric indexes. Specifically, we build the index as a series of interconnected machine learning models. This collection serves as the basis for the reproducibility paper accompanying our parent paper -- "Learned metric index—proposition of learned indexing for unstructured data" [1].

1. In "data" we make publicly available a collection of 3 individual dataset descriptors -- CoPhIR (1 million objects, 282 columns), Profimedia  (1 million objects, 4096 columns), and MoCap (~350k objects, 4096 columns), "labels" obtained from a template index -- M-tree or M-index, "queries" used to perform an experimental search with and "ground-truths" to evaluate the approximate k-NN performance of the index. Within "test" we include dummy data to ease the integration of any custom dataset (examples in "LMIF/*.ipynb") that a reader may want to integrate into our solution. In CoPhIR [2], each of the vectors is obtained by concatenating five MPEG-7 global visual descriptors extracted from an image downloaded from Flickr. The Profimedia image dataset [3], contains Caffe visual descriptors extracted from Photo-stock images by a convolutional neural network. MoCap (motion capture data) [4] descriptors contain sequences of 3D skeleton poses extracted from 3+ hrs of recordings capturing actors performing more than 70 different motion scenarios.
The dataset's size is 43 GB upon decompression.

[1] Antol, Matej, et al. "Learned metric index—proposition of learned indexing for unstructured data." Information Systems 100 (2021): 101774.
[2] Batko, Michal, et al. "Building a web-scale image similarity search system." Multimedia Tools and Applications 47.3 (2010): 599-629.
[3] Budikova, Petra et al. "Evaluation platform for content-based image retrieval systems." International Conference on Theory and Practice of Digital Libraries. Springer, Berlin, Heidelberg, 2011.
[4] Müller, Meinard, et al. "Documentation mocap database hdm05." (2007).

2. "LMIF" contains a user-friendly environment to reproduce the experiments in [1]. LMIF consists of three components:
- an implementation of the Learned Metric Index (distributed under the MIT license),
- a collection of scripts and configuration setups necessary for re-running the experiments in [1] and
- instructions for creating the reproducibility environment (Docker).
For a thorough description of "LMIF", please refer to our reproducibility paper -- "Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data".

3. "output-files" contain the reproduced outputs for each experiment, with generated figures and a concise ".html" report (as presented in [1])
DOI: 10.6084/m9.figshare.20261475.v1
title: Supplemental Table 3

description: Table S3. All metabolites that had an interaction effect with an FDR &lt; 0.10 for metabolomics analyses.
DOI: 10.17632/8gmt35bpkv.1
title: Feature distance/frequency crime analysis - research sample GIS dataset

description: This is a small GIS dataset, to use as a sample with the Feature distance/frequency crime analysis algorithm.
DOI: 10.17632/8gmt35bpkv.2
title: Feature distance/frequency crime analysis - research sample GIS dataset

description: This is a small GIS dataset, to use as a sample with the Feature distance/frequency crime analysis algorithm.
DOI: 10.17632/fvbr735w28.1
title: Part 5 of data for Elizondo et al "Adipose Tissue Macrophage Transglutaminase 2 and the Inflammation of Diet-Induced Obese Male Mice”

description: Part 5 of data for Elizondo et al "Adipose Tissue Macrophage Transglutaminase 2 and the Inflammation of Diet-Induced Obese Male Mice”
DOI: 10.17632/rf2s8h2xgy.1
title: Association between the skin microbiome and lichen striatus hypopigmentation: Cutibacterium acnes as a potential cause - Supplementary tables

description: Supplementary tables for the research investigating the involvement of Cutibacterium acnes in the development of hypopigmentation in lichen striatus
DOI: 10.17632/nzdb7gftph.1
title: Isotherm data for adsorption of amoxicillin, ampicillin, and doripenem onto bentonite

description: The dataset contains the experimental and fitted adsorption isotherm datasets of amoxicillin, ampicillin, and doripenem onto bentonite obtained from batch adsorption experiments across 30 to 50 C. The dataset showcases adsorption performances of antibiotics on bentonite and allows comparison between various isotherm models to provide further insights onto the adsorption behaviors.
DOI: 10.5281/zenodo.7860242
title: World Padel Tour historical match data

description: This dataset contains all World Padel Tour match information available on its webpage. It was obtained via web scraping. This dataset is obtained for educational purposes, it is part of a university course in UOC (Universitat Oberta de Catalunya). The data is in Spanish.
DOI: 10.5281/zenodo.7860241
title: World Padel Tour historical match data

description: This dataset contains all World Padel Tour match information available on its webpage. It was obtained via web scraping. This dataset is obtained for educational purposes, it is part of a university course in UOC (Universitat Oberta de Catalunya). The data is in Spanish.
DOI: 10.17632/8wp73zxr47.11
title: Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data 

description: With this collection of code and configuration files (contained in "LMIF" = 'Learned Metric Index Framework'), outputs ("output-files") and datasets ("data") we set out to explore whether a learned approach to building a metric index is a viable alternative to the traditional way of constructing metric indexes. Specifically, we build the index as a series of interconnected machine learning models. This collection serves as the basis for the reproducibility paper accompanying our parent paper -- "Learned metric index—proposition of learned indexing for unstructured data" [1].

1. In "data" we make publicly available a collection of 3 individual dataset descriptors -- CoPhIR (1 million objects, 282 columns), Profimedia  (1 million objects, 4096 columns), and MoCap (~350k objects, 4096 columns), "labels" obtained from a template index -- M-tree or M-index, "queries" used to perform an experimental search with and "ground-truths" to evaluate the approximate k-NN performance of the index. Within "test" we include dummy data to ease the integration of any custom dataset (examples in "LMIF/*.ipynb") that a reader may want to integrate into our solution. In CoPhIR [2], each of the vectors is obtained by concatenating five MPEG-7 global visual descriptors extracted from an image downloaded from Flickr. The Profimedia image dataset [3], contains Caffe visual descriptors extracted from Photo-stock images by a convolutional neural network. MoCap (motion capture data) [4] descriptors contain sequences of 3D skeleton poses extracted from 3+ hrs of recordings capturing actors performing more than 70 different motion scenarios.
The dataset's size is 43 GB upon decompression.

[1] Antol, Matej, et al. "Learned metric index—proposition of learned indexing for unstructured data." Information Systems 100 (2021): 101774.
[2] Batko, Michal, et al. "Building a web-scale image similarity search system." Multimedia Tools and Applications 47.3 (2010): 599-629.
[3] Budikova, Petra et al. "Evaluation platform for content-based image retrieval systems." International Conference on Theory and Practice of Digital Libraries. Springer, Berlin, Heidelberg, 2011.
[4] Müller, Meinard, et al. "Documentation mocap database hdm05." (2007).

2. "LMIF" contains a user-friendly environment to reproduce the experiments in [1]. LMIF consists of three components:
- an implementation of the Learned Metric Index (distributed under the MIT license),
- a collection of scripts and configuration setups necessary for re-running the experiments in [1] and
- instructions for creating the reproducibility environment (Docker).
For a thorough description of "LMIF", please refer to our reproducibility paper -- "Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data".

3. "output-files" contain the reproduced outputs for each experiment, with generated figures and a concise ".html" report (as presented in [1])
DOI: 10.17632/8wp73zxr47.10
title: Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data 

description: With this collection of code and configuration files (contained in "LMIF" = 'Learned Metric Index Framework'), outputs ("output-files") and datasets ("data") we set out to explore whether a learned approach to building a metric index is a viable alternative to the traditional way of constructing metric indexes. Specifically, we build the index as a series of interconnected machine learning models. This collection serves as the basis for the reproducibility paper accompanying our parent paper -- "Learned metric index—proposition of learned indexing for unstructured data" [1].

1. In "data" we make publicly available a collection of 3 individual dataset descriptors -- CoPhIR (1 million objects, 282 columns), Profimedia  (1 million objects, 4096 columns), and MoCap (~350k objects, 4096 columns), "labels" obtained from a template index -- M-tree or M-index, "queries" used to perform an experimental search with and "ground-truths" to evaluate the approximate k-NN performance of the index. Within "test" we include dummy data to ease the integration of any custom dataset (examples in "LMIF/*.ipynb") that a reader may want to integrate into our solution. In CoPhIR [2], each of the vectors is obtained by concatenating five MPEG-7 global visual descriptors extracted from an image downloaded from Flickr. The Profimedia image dataset [3], contains Caffe visual descriptors extracted from Photo-stock images by a convolutional neural network. MoCap (motion capture data) [4] descriptors contain sequences of 3D skeleton poses extracted from 3+ hrs of recordings capturing actors performing more than 70 different motion scenarios.
The dataset's size is 43 GB upon decompression.

[1] Antol, Matej, et al. "Learned metric index—proposition of learned indexing for unstructured data." Information Systems 100 (2021): 101774.
[2] Batko, Michal, et al. "Building a web-scale image similarity search system." Multimedia Tools and Applications 47.3 (2010): 599-629.
[3] Budikova, Petra et al. "Evaluation platform for content-based image retrieval systems." International Conference on Theory and Practice of Digital Libraries. Springer, Berlin, Heidelberg, 2011.
[4] Müller, Meinard, et al. "Documentation mocap database hdm05." (2007).

2. "LMIF" contains a user-friendly environment to reproduce the experiments in [1]. LMIF consists of three components:
- an implementation of the Learned Metric Index (distributed under the MIT license),
- a collection of scripts and configuration setups necessary for re-running the experiments in [1] and
- instructions for creating the reproducibility environment (Docker).
For a thorough description of "LMIF", please refer to our reproducibility paper -- "Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data".

3. "output-files" contain the reproduced outputs for each experiment, with generated figures and a concise ".html" report (as presented in [1])
DOI: 10.17632/8wp73zxr47.9
title: Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data 

description: With this collection of code and configuration files (contained in "LMIF" = 'Learned Metric Index Framework'), outputs ("output-files") and datasets ("data") we set out to explore whether a learned approach to building a metric index is a viable alternative to the traditional way of constructing metric indexes. Specifically, we build the index as a series of interconnected machine learning models. This collection serves as the basis for the reproducibility paper accompanying our parent paper -- "Learned metric index—proposition of learned indexing for unstructured data" [1].

1. In "data" we make publicly available a collection of 3 individual dataset descriptors -- CoPhIR (1 million objects, 282 columns), Profimedia  (1 million objects, 4096 columns), and MoCap (~350k objects, 4096 columns), "labels" obtained from a template index -- M-tree or M-index, "queries" used to perform an experimental search with and "ground-truths" to evaluate the approximate k-NN performance of the index. Within "test" we include dummy data to ease the integration of any custom dataset (examples in "LMIF/*.ipynb") that a reader may want to integrate into our solution. In CoPhIR [2], each of the vectors is obtained by concatenating five MPEG-7 global visual descriptors extracted from an image downloaded from Flickr. The Profimedia image dataset [3], contains Caffe visual descriptors extracted from Photo-stock images by a convolutional neural network. MoCap (motion capture data) [4] descriptors contain sequences of 3D skeleton poses extracted from 3+ hrs of recordings capturing actors performing more than 70 different motion scenarios.
The dataset's size is 43 GB upon decompression.

[1] Antol, Matej, et al. "Learned metric index—proposition of learned indexing for unstructured data." Information Systems 100 (2021): 101774.
[2] Batko, Michal, et al. "Building a web-scale image similarity search system." Multimedia Tools and Applications 47.3 (2010): 599-629.
[3] Budikova, Petra et al. "Evaluation platform for content-based image retrieval systems." International Conference on Theory and Practice of Digital Libraries. Springer, Berlin, Heidelberg, 2011.
[4] Müller, Meinard, et al. "Documentation mocap database hdm05." (2007).

2. "LMIF" contains a user-friendly environment to reproduce the experiments in [1]. LMIF consists of three components:
- an implementation of the Learned Metric Index (distributed under the MIT license),
- a collection of scripts and configuration setups necessary for re-running the experiments in [1] and
- instructions for creating the reproducibility environment (Docker).
For a thorough description of "LMIF", please refer to our reproducibility paper -- "Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data".

3. "output-files" contain the reproduced outputs for each experiment, with generated figures and a concise ".html" report (as presented in [1])
DOI: 10.17632/8wp73zxr47.7
title: Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data 

description: With this collection of code and configuration files (contained in "LMIF" = 'Learned Metric Index Framework'), outputs ("output-files") and datasets ("datasets") we set out to explore whether a learned approach to building a metric index is a viable alternative to the traditional way of constructing metric indexes. Specifically, we build the index as a series of interconnected machine learning models. This collection serves as the basis for the reproducibility paper accompanying our parent paper -- "Learned metric index—proposition of learned indexing for unstructured data" [1].

1. In "datasets" we make publicly available a collection of 3 individual dataset descriptors -- CoPhIR (1 million objects, 282 columns), Profimedia  (1 million objects, 4096 columns), and MoCap (~350k objects, 4096 columns), "labels" obtained from a template index -- M-tree or M-index, "queries" used to perform an experimental search with and "ground-truths" to evaluate the approximate k-NN performance of the index. Within "test" we include dummy data to ease the integration of any custom dataset (examples in "LMIF/*.ipynb") that a reader may want to integrate into our solution. In CoPhIR [2], each of the vectors is obtained by concatenating five MPEG-7 global visual descriptors extracted from an image downloaded from Flickr. The Profimedia image dataset [3], contains Caffe visual descriptors extracted from Photo-stock images by a convolutional neural network. MoCap (motion capture data) [4] descriptors contain sequences of 3D skeleton poses extracted from 3+ hrs of recordings capturing actors performing more than 70 different motion scenarios.
The dataset's size is 43 GB upon decompression.

[1] Antol, Matej, et al. "Learned metric index—proposition of learned indexing for unstructured data." Information Systems 100 (2021): 101774.
[2] Batko, Michal, et al. "Building a web-scale image similarity search system." Multimedia Tools and Applications 47.3 (2010): 599-629.
[3] Budikova, Petra et al. "Evaluation platform for content-based image retrieval systems." International Conference on Theory and Practice of Digital Libraries. Springer, Berlin, Heidelberg, 2011.
[4] Müller, Meinard, et al. "Documentation mocap database hdm05." (2007).

2. "LMIF" contains a user-friendly environment to reproduce the experiments in [1]. LMIF consists of three components:
- an implementation of the Learned Metric Index (distributed under the MIT license),
- a collection of scripts and configuration setups necessary for re-running the experiments in [1] and
- instructions for creating the reproducibility environment (Docker).
For a thorough description of "LMIF", please refer to our reproducibility paper -- "Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data".

3. "output-files" contain the reproduced outputs for each experiment, with generated figures and a concise ".html" report (as presented in [1])
DOI: 10.17632/8wp73zxr47.8
title: Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data 

description: With this collection of code and configuration files (contained in "LMIF" = 'Learned Metric Index Framework'), outputs ("output-files") and datasets ("datasets") we set out to explore whether a learned approach to building a metric index is a viable alternative to the traditional way of constructing metric indexes. Specifically, we build the index as a series of interconnected machine learning models. This collection serves as the basis for the reproducibility paper accompanying our parent paper -- "Learned metric index—proposition of learned indexing for unstructured data" [1].

1. In "datasets" we make publicly available a collection of 3 individual dataset descriptors -- CoPhIR (1 million objects, 282 columns), Profimedia  (1 million objects, 4096 columns), and MoCap (~350k objects, 4096 columns), "labels" obtained from a template index -- M-tree or M-index, "queries" used to perform an experimental search with and "ground-truths" to evaluate the approximate k-NN performance of the index. Within "test" we include dummy data to ease the integration of any custom dataset (examples in "LMIF/*.ipynb") that a reader may want to integrate into our solution. In CoPhIR [2], each of the vectors is obtained by concatenating five MPEG-7 global visual descriptors extracted from an image downloaded from Flickr. The Profimedia image dataset [3], contains Caffe visual descriptors extracted from Photo-stock images by a convolutional neural network. MoCap (motion capture data) [4] descriptors contain sequences of 3D skeleton poses extracted from 3+ hrs of recordings capturing actors performing more than 70 different motion scenarios.
The dataset's size is 43 GB upon decompression.

[1] Antol, Matej, et al. "Learned metric index—proposition of learned indexing for unstructured data." Information Systems 100 (2021): 101774.
[2] Batko, Michal, et al. "Building a web-scale image similarity search system." Multimedia Tools and Applications 47.3 (2010): 599-629.
[3] Budikova, Petra et al. "Evaluation platform for content-based image retrieval systems." International Conference on Theory and Practice of Digital Libraries. Springer, Berlin, Heidelberg, 2011.
[4] Müller, Meinard, et al. "Documentation mocap database hdm05." (2007).

2. "LMIF" contains a user-friendly environment to reproduce the experiments in [1]. LMIF consists of three components:
- an implementation of the Learned Metric Index (distributed under the MIT license),
- a collection of scripts and configuration setups necessary for re-running the experiments in [1] and
- instructions for creating the reproducibility environment (Docker).
For a thorough description of "LMIF", please refer to our reproducibility paper -- "Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data".

3. "output-files" contain the reproduced outputs for each experiment, with generated figures and a concise ".html" report (as presented in [1])
DOI: 10.6084/m9.figshare.20263674
title: Supplemental Table 6

description: Table S6. Differentially methylated FDRs.
DOI: 10.6084/m9.figshare.20263674.v1
title: Supplemental Table 6

description: Table S6. Differentially methylated FDRs.