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l-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of large size.</p>
s-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of small size.</p>
Benchmark for the evaluation of Named Entity Linking over ancient documents
<p><strong>Benchmark for the evaluation of Named Entity Linking over ancient documents</strong><br> Elvys Linhares Pontes, Ahmed Hamdi, Nicolas Sidere, and Antoine Doucet<br> University of Avignon: elvys.linhares-pontes@univ-avignon.fr; University of La Rochelle: {elvys.linhares_pontes,ahmed.hamdi,nicolas.sidere,antoine.doucet}@univ-lr.fr</p> <p>These are the supplementary materials for the ICADL 2019 paper <strong><em>Impact of OCR Quality on Named Entity Linking</em></strong>. If you end up using whole or parts of this resource, please use the following citation:</p> <ul> <li>Linhares Pontes, E., Hamdi, A., Sidere, N., and Doucet, A. (2019). Impact of OCR Quality on Named Entity Linking. In Proceedings of 21st International Conference on Asia-Pacific Digital Libraries ICADL 2019, Kuala Lumpur, Malaysia.</li> </ul> <p>or alternatively use the following `bib`:</p> <pre><code>@inproceedings{linhares2019icadl, title="Impact of OCR Quality on Named Entity Linking.", author={Linhares Pontes, Elvys, and Hamdi, Ahmed, and Sidere, Nicolas, and Doucet, Antoine}, year={2019}, booktitle={Proceedings of 21st International Conference on Asia-Pacific Digital Libraries ICADL 2019} }</code></pre> <p><strong>Files</strong><br> This archive contains six folders -- one per dataset -- as well as this README. The folders contain the degraded images, the noisy texts extracted by the OCR and their aligned version with clean data. This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).</p> <p><strong>Acknowledgments</strong><br> This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant 770299 [NewsEye](https://www.newseye.eu/).</p>
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation [Models]
<p>This file contains the pretrained models and the evaluation of the pipelines described in the paper <em>Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation</em>.</p> <p>Source code can be downloaded at: <a href="https://github.com/aramis-lab/AD-DL">https://github.com/aramis-lab/AD-DL</a></p> <p>Also, single files can be obtained at: <a href="https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/">https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/</a></p> <p>The structure of the compressed file is as follows:</p> <p>clinicadl_models/<br> ├── 2D_slice<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── AD_CN_dataleakage<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ └── AD_CN<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_patch<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_ROI_based<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_subject<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── autoencoders<br> │ ├── 3D_patch<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ ├── 3D_ROI_based<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ └── 3D_subject<br> │ └── baseline<br> │ ├── extensive<br> │ └── minimal<br> └── svm<br> ├── baseline<br> │ ├── AD_CN<br> │ │ ├── all_subjects.tsv<br> │ │ └── classifier<br> │ └── sMCI_pMCI<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── longitudinal<br> ├── AD_CN<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── sMCI_pMCI<br> ├── all_subjects.tsv<br> └── classifier</p> <p>We provide the pretrained CNN models for the frameworks 3D subject-level, 3D ROI-based, 3D patch-level and 2D slice-level. This models can be found as a <strong><em>.pth.tar</em> </strong>file (<em>Pytorch</em> format) inside the <em>best_model</em> folder for each framework (and for each fold). We also provide the autoencoders that initialize the training stage of the CNN networks. The <em>performances </em>folder contains the computed metrics for the correponding model (ACC, BA, etc). <em> </em></p> <p>For the svn classification, we provide files with the dual coefficients, the support vector indices and the weights. Also, <em>tsv</em> files with the subject list.</p>
VPS: Excavating High-Level C++ Constructs from Low-Level Binaries to Protect Dynamic Dispatching - (Evaluation Data)
<p>Evaluation data of the published paper: "VPS: Excavating High-Level C++ Constructs from Low-Level Binaries to Protect Dynamic Dispatching". The paper was published at the Annual Computer Security Applications Conference (ACSAC) 2019.</p> <p>The paper is available at: https://www.syssec.ruhr-uni-bochum.de/research/publications/VPS/</p> <p> </p> <p>Ab­stract</p> <p>Po­ly­mor­phism and in­heri­t­an­ce make C++ sui­ta­ble for wri­ting com­plex soft­ware, but si­gni­fi­cant­ly in­crea­se the at­tack sur­face be­cau­se the im­ple­men­ta­ti­on re­li­es on vir­tu­al func­tion ta­bles (vta­bles). These vta­bles con­tain func­tion poin­ters that at­ta­ckers can po­ten­ti­al­ly hi­jack and in prac­tice, vta­ble hi­ja­cking is one of the most im­portant at­tack vec­tor for C++ bi­na­ries. In this paper, we pre­sent VTa­ble Poin­ter Se­pa­ra­ti­on (VPS), a prac­tical bi­na­ry-le­vel de­fen­se against vta­ble hi­ja­cking in C++ ap­p­li­ca­ti­ons. Un­li­ke pre­vious bi­na­ry-le­vel de­fen­ses, which rely on un­sound sta­tic ana­ly­ses to match clas­ses to vir­tu­al call­si­tes, VPS achie­ves a more ac­cu­ra­te pro­tec­tion by re­stric­ting vir­tu­al call­si­tes to va­lidly crea­ted ob­jects. More spe­ci­fi­cal­ly, VPS en­su­res that vir­tu­al call­si­tes can only use ob­jects crea­ted at valid ob­ject con­struc­tion sites, and only if those ob­jects can reach the call­si­te. Mo­re­over, VPS ex­pli­cit­ly prevents false po­si­ti­ves (fal­se­ly iden­ti­fied vir­tu­al call­si­tes) from brea­king the bi­na­ry, an issue exis­ting work does not hand­le cor­rect­ly or at all. We eva­lua­te the pro­to­ty­pe im­ple­men­ta­ti­on of VPS on a di­ver­se set of com­plex, re­al-world ap­p­li­ca­ti­ons (Mon­goDB, MySQL ser­ver, Node.js, SPEC CPU2017/CPU2006), show­ing that our ap­proach pro­tects on aver­a­ge 97.8% of all vir­tu­al call­si­tes in SPEC CPU2006 and 97.4% in SPEC CPU2017 (all C++ bench­marks), with a mo­de­ra­te per­for­mance over­head of 11% and 9% geo­me­an, re­spec­tive­ly. Fur­ther­mo­re, our eva­lua­ti­on re­veals 86 false ne­ga­ti­ves in VTV, a po­pu­lar sour­ce-ba­sed de­fen­se which is part of GCC.</p>
Determination and Evaluation of the Non-Additivity in Wetting of Molecularly Heterogeneous Surfaces
<p>All the data relevant to the paper "Determination and Evaluation of the Non-Additivity in Wetting of Molecularly Heterogeneous Surfaces" (PNAS, 2019) are available here.</p> <p>The details about the data collection and analysis are available in the paper.</p> <p>The data are divided into two zipped folders: one of them contains all the experimental data (experiments.zip) and the other one all the files related to the molecular dynamics simulations (MD.zip). Each of the two folders contains a README.txt file, with details about the folder and its structure.</p>
Figure 1 in Blood metabolites as predictors to evaluate the body condition of Neopelma pallescens (Passeriformes: Pipridae) in northeastern Brazil
Figure 1. Spearman's correlation between glucose and ketones
Figure 2 in The use of BMWP and ASPT indices for evaluation of water quality according to macroinvertebrates in Değirmendere Stream (Isparta, Turkey)
Figure 2. Classification of stations based on similarities of macroinvertebrate communities.
Figure 1 in The use of BMWP and ASPT indices for evaluation of water quality according to macroinvertebrates in Değirmendere Stream (Isparta, Turkey)
Figure 1. Study area and stations.
Figure 2. A in Defoliation of the Invasive Tree Falcataria moluccana on Hawaii Island by the Native Koa Looper Moth (Geometridae: Scotorythra paludicola), and Evaluation of Five Fabaceous Trees as Larval Hostplants
Figure 2. A defoliated Falcataria moluccana
Figure 3 in Faunistic and zoogeographical composition and preliminary evaluations of some ecological features of the subfamily Staphylininae (Coleoptera: Staphylinidae) of the Central Anatolian Region of Turkey
Figure 3. Distribution of species according to collection months.
Figure 1 in Faunistic and zoogeographical composition and preliminary evaluations of some ecological features of the subfamily Staphylininae (Coleoptera: Staphylinidae) of the Central Anatolian Region of Turkey
Figure 1. Map of research area.
Figure 1 in Macroinvertebrate-based biotic indices for evaluating the water quality of Kargı Stream (Antalya, Turkey)
Figure 1. Study area and stations.
Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode
<p>Characterisation dataset for “<span>Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode</span>”, DOI:xx. Data for main and supporting figures provided as *.xlsx and *.txt files.</p>
Figure 4 in Evaluation of chitosan and silver nanoparticles Against isolated pathogens from Mulberry Silkworm, Bombyx mori L. (Lepidoptera: Bombycidae) under laboratory conditions
Figure 4. Transmissiоn electron microscоpy micrоgraph of silver nanoparticles.
Figure 3 in Evaluation of chitosan and silver nanoparticles Against isolated pathogens from Mulberry Silkworm, Bombyx mori L. (Lepidoptera: Bombycidae) under laboratory conditions
Figure 3. Final dispersion formed after reduction (A) silver and (B) chitosan.
Figure 1 in Spectral characterization and biological evaluation of biomolecules from the peels of three orange fruits: a comparative study
Figure 1. Schematic representation of experimental protocol.
Figure 9 in Spectral characterization and biological evaluation of biomolecules from the peels of three orange fruits: a comparative study
Figure 9. FT-IR spectroscopy of RM of African Navel orange peel.
Figure 10 in Spectral characterization and biological evaluation of biomolecules from the peels of three orange fruits: a comparative study
Figure 10. The dose-response curve of RM of orange peels against MCF-7 cells.
Figure 7 in Spectral characterization and biological evaluation of biomolecules from the peels of three orange fruits: a comparative study
Figure 7. FT-IR spectroscopy of RM of Valencia orange peel.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.