Mendeley Data Showcase

Appendix: Main Results and Proof of Propositions

Data from Amur tiger footprints

Tesis de grado de doctor sobre la evaluación de los factores de riesgo psicosocial y la validación de la Guía de referencia III de la Normal Oficial Mexicana 035 de la STPS.

methods and findings concerning this article

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This a dataset summarizing data of BAFF level and polymorphisms investigation in Graves' disease patients

Dataset containing the underlying data for the manuscript titled: The mental health benefits of visiting canals and rivers: An ecological momentary assessment study.

We provide the input data and results of six case studies inspired by the operations of OCP Group at the Jorf Lasfar chemical platform in Morocco.

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])

Uncut western blots for publication "SHMT2-mediated mitochondrial serine metabolism drives 5-FU resistance by fueling nucleotide biosynthesis - Pranzini et al."