cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8wp73zxr47.5
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: 10.17632/8wp73zxr47.6
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: 10.17632/8wp73zxr47.3
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: 10.17632/8wp73zxr47.4
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: 10.17632/8wp73zxr47.2
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: 10.17632/8wp73zxr47.1
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: 10.25346/s6/z36krr/q9lpfs
title: db_blog_skd.zip
DOI: 10.25346/s6/z36krr/i72qew
title: db_acad_rpe.zip
DOI: 10.25346/s6/z36krr/kc7okz
title: db_tvm_ppw.zip
DOI: 10.25346/s6/z36krr/arcct5
title: db_mag_qpj.zip
DOI: 10.25346/s6/z36krr/hhm2sc
title: db_web_wyb.zip
DOI: 10.25346/s6/z36krr/mohspv
title: db_spok_kde.zip
DOI: 10.25346/s6/z36krr/qw3ase
title: db_news_lsp.zip
DOI: 10.25346/s6/z36krr/fxyfan
title: db_fic_awq.zip
DOI: 10.25346/s6/z36krr/0yovz0
title: lexicon.zip
DOI: 10.25346/s6/z36krr/lnjizd
title: text_tvm_nwh.zip
DOI: 10.25346/s6/z36krr/zxxskz
title: sources.zip
DOI: 10.25346/s6/z36krr/fdtqvf
title: text_fic_jjw.zip
DOI: 10.25346/s6/z36krr/yh1dyg
title: text_acad_isi.zip
DOI: 10.25346/s6/z36krr/78bekb
title: text_mag_dhv.zip
DOI: 10.25346/s6/z36krr/hi8ivs
title: text_blog_wih.zip
DOI: 10.25346/s6/z36krr/15gpfx
title: text_news_nne.zip
DOI: 10.25346/s6/z36krr/3jamcb
title: text_spok_yuv.zip
DOI: 10.25346/s6/z36krr/ypbmkp
title: wlp_fic_ine.zip
DOI: 10.25346/s6/z36krr/mx6jdb
title: wlp_tvm_snm.zip
DOI: 10.25346/s6/z36krr/qcrjpu
title: wlp_spok_cud.zip
DOI: 10.25346/s6/z36krr/qpyefm
title: wlp_web_obt.zip
DOI: 10.25346/s6/z36krr/vhczxf
title: wlp_acad_vuw.zip
DOI: 10.25346/s6/z36krr/8tus9x
title: wlp_mag_dhv.zip
DOI: 10.25346/s6/z36krr/e7vnjl
title: wlp_news_znw.zip
DOI: 10.25346/s6/z36krr/efychi
title: wlp_blog_qie.zip
DOI: 10.25346/s6/z36krr/csclsp
title: coca2020_overview.pdf

description: Overview of the Corpus of Contemporary American English (COCA) . Pdf format.
DOI: 10.6084/m9.figshare.20419386
title: Supplemental File 5

description: Supplemental File 5. All data for RNA pathway analyses.
DOI: 10.6084/m9.figshare.20419386.v1
title: Supplemental File 5

description: Supplemental File 5. All data for RNA pathway analyses.
DOI: 10.6084/m9.figshare.20422227
title: Supplemental File 6

description: Supplemental File 6. Pathways and gene membership for the top 3 clusters identified by STRING and MCODE network and pathway analyses.
DOI: 10.6084/m9.figshare.20422227.v1
title: Supplemental File 6

description: Supplemental File 6. Pathways and gene membership for the top 3 clusters identified by STRING and MCODE network and pathway analyses.
DOI: 10.5519/qd.t4hj8ih2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (71422 records)
DOI: 10.35077/act-tax
title: Neuron Morphology data in tiff stack format from Patch-seq experiments in human neocortex, Cell ID 868145983

description: Human neocortical neurons display diverse morphology, physiology and gene expression profiles. We use a method called Patch-seq, where these three data modalities are assayed simultaneously from the same neuron, to understand how the genetic profile of human neocortical neuron types corresponds to their morphological and electrophysiological phenotypes. This collection may contain image stacks (TIFF), reconstruction files in image space (.swc, µm), a transformed file (.swc, µm) oriented so that the y axis extends vertically towards the pia surface, and a marker file (.marker, µm) indicating nodes where dendrites were truncated (10), reconstruction was not continued (20), or surface of tissue was closest to soma (30).
description: https://www.protocols.io/view/patch-seq-recording-and-extraction-detailed-protoc-5qpvoyb2bg4o/v2, https://www.protocols.io/view/3d-reconstruction-of-neurons-in-vaa3d-v-3-36wgq5683gk5/v3
description: DATASET METADATA: title:'Neuron Morphology data in tiff stack format from Patch-seq experiments in human neocortex, Cell ID 868145983', GeneralModality:'cell morphology', Technique:'Patch-seq' SPECIMEN METADATA: LocalID:'H19.03.311', Species:'Human', NCBITaxonomy:'NCBI:txid9606', Age:'41', Ageunit:'years', Sex:'Female', OrganName:'Brain', SampleLocalID:'868145983', Atlas:'Ding, Song-Lin et al. “Comprehensive cellular-resolution atlas of the adult human brain.” The Journal of comparative neurology vol. 524,16 (2016): 3127-481. doi:10.1002/cne.24439', Locations:'TemL' INSTRUMENT METADATA: MicroscopeType:'Brightfield', MicroscopeManufacturerAndModel:'Zeiss AxioImager Z2 microscope', ObjectiveImmersion:'Oil' IMAGE METADATA: xAxis:'NA', yAxis:'NA', zAxis:'NA', Number:'NA', displayColor:'NA', stepSizeX:'0.114', stepSizeY:'0.114', stepSizeZ:'0.44'
DOI: 10.6084/m9.figshare.21898998
title: Supplemental File 6

description: Supplemental File 6. MCODE Results for DEG network analyses.
DOI: 10.6084/m9.figshare.21898998.v1
title: Supplemental File 6

description: Supplemental File 6. MCODE Results for DEG network analyses.
DOI: 10.17632/wnc2k596db.1
title: Awareness of physiotherapy for eye muscle weakness among general population in Haryana

description: Many people suffers from weakness of eye muscle but few people knows that there is also a physiotherapy for strengthen the eye muscle and improve their vision. So, a survey was conducted in Haryana ( 18 years and above) to find out people awareness regarding physiotherapy for eye weakness. It was observed that 75.1% people of Haryana are aware about it and rest don't know about it.
DOI: 10.17632/3f5y89gpw5.1
title: Addiction to Smartphone and its Effect on Forward Head Posture in asymptomatic young adults.

description: Smartphone addiction refers to excessive and compulsive use of smartphones, which can have negative effects on an individual's physical and mental health. One of the physical effects of smartphone addiction is the development of forward head posture (FHP) in asymptomatic young adults. We use smartphone addiction scale - short version to measure addiction level and universal goniometer to measure the CVA.