Find research datasets worth reusing

opencc-byNov 2021View details →

Antimicrobial treatment recommendations for common bacterial infections and syndromes from national/standard treatment guidelines in African Union member states

A compilation of antimicrobial treatment recommendations for common bacterial infections and syndromes for adult and pediatric patients from national/standard treatment guidelines in African Union member states

opencc-by-4.0Nov 2021View details →

Language Specific Event Recommendation Ground Truth

This is a multilingual ground truth dataset for training, evaluating and testing the LaSER (Language-Specific Event Recommendation) model. It contains language-specific relevance scores for event-centric click-through pairs according to the publicly available Clickstream dataset in German, French and Russian as well as the user study annotations conducted for evaluating the language-specific recommendations by LaSER. For more details, refer to <a href="https://github.com/saraabdollahi/EventKG-Click">EventKG+Click</a> and <a href="https://github.com/saraabdollahi/LaSER">LaSER</a>. This dataset consists of two sets of files as follows: 1. The ground truth dataset that is used for training the learning to rank (LTR) model in LaSER in three languages. The following files contain the language-specific relevance scores between a source and target entity based on EventKG+Click dataset: <ul> <li>german_ground_truth.txt</li> <li>french_ground_truth.txt</li> <li>russian_ground_truth.txt</li> </ul> In these files source and target represent the label of entities and events in the respective language.  2. The second set contains the user study participants' annotations regarding different relevance criteria of recommended events by LaSER. The following three files contain the annotations of at least three participants per event: <ul> <li>german_user_study_annotations.csv</li> <li>french_user_study_annotations.csv</li> <li>russian_user_study_annotations.csv</li> </ul> In these files, "r1", "r2" and "r3" denote relevance to the topic, language community and general audience respectively. And topic and event represent the wikidata-id of entities and events.

opencc-by-4.0Dec 2021View details →

Game-Shapley recommender system demonstration

Recommender system using Game-Shapley algorithm

openother-ncFeb 2010View details →

lastfm Music Recommendation Dataset

This is a common Zenodo repository for both<a href="http://ocelma.net/MusicRecommendationDataset/lastfm-360K.html"> lastfm-360K</a> and <a href="http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html">lastfm-1K</a> datasets. See below the details of both datasets, including license, acknowledgements, contact, and instructions to cite.   LASTFM-360K (version 1.2, March 2010). <ul> <li>What is this? This dataset contains <user, artist, plays> tuples (for ~360,000 users) collected from <a href="http://www.last.fm/api">Last.fm API</a>, using the <a href="http://www.last.fm/api/show?service=300">user.getTopArtists()</a> method.</li> <li>Files: <ul> <li>usersha1-artmbid-artname-plays.tsv (MD5: be672526eb7c69495c27ad27803148f1)</li> <li>usersha1-profile.tsv (MD5: 51159d4edf6a92cb96f87768aa2be678)</li> <li>mbox_sha1sum.py (MD5: feb3485eace85f3ba62e324839e6ab39)</li> </ul> </li> <li>Data Statistics: <ul> <li>File usersha1-artmbid-artname-plays.tsv: <ul> <li>Total Lines: 17,559,530</li> <li>Unique Users: 359,347</li> <li>Artists with <a href="http://musicbrainz.org/">MBID</a>: 186,642</li> <li>Artists without <a href="http://musicbrainz.org/">MBID</a>: 107,373</li> </ul> </li> </ul> </li> <li>Data Format: The data is formatted one entry per line as follows (tab separated "\t"): <ul> <li>File usersha1-artmbid-artname-plays.tsv: <pre>user-mboxsha1 \t musicbrainz-artist-id \t artist-name \t plays</pre> </li> <li>File usersha1-profile.tsv: <pre>user-mboxsha1 \t gender (m|f|empty) \t age (int|empty) \t country (str|empty) \t signup (date|empty)</pre> </li> </ul> </li> <li>Example: <ul> <li>File usersha1-artmbid-artname-plays.tsv: <pre>000063d3fe1cf2ba248b9e3c3f0334845a27a6be \t a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432 \t u2 \t 31 ...</pre> </li> <li>File usersha1-profile.tsv: <pre>000063d3fe1cf2ba248b9e3c3f0334845a27a6be \t m \t 19 \t Mexico \t Apr 28, 2008 ...</pre> </li> </ul> </li> </ul>   LASTFM-1K (version 1.0, March 2010). <ul> <li>What is this? This dataset contains <user, timestamp, artist, song> tuples collected from <a href="http://www.last.fm/api">Last.fm API</a>, using the <a href="http://www.last.fm/api/show?service=278">user.getRecentTracks()</a> method. This dataset represents the whole listening habits (till May, 5th 2009) for nearly 1,000 users.</li> <li>Files: <ul> <li>userid-timestamp-artid-artname-traid-traname.tsv (MD5: 64747b21563e3d2aa95751e0ddc46b68)</li> <li>userid-profile.tsv (MD5: c53608b6b445db201098c1489ea497df)</li> </ul> </li> <li>Data Statistics: <ul> <li>File userid-timestamp-artid-artname-traid-traname.tsv: <ul> <li>Total Lines: 19,150,868</li> <li>Unique Users: 992</li> <li>Artists with MBID: 107,528</li> <li>Artists without MBDID: 69,420</li> </ul> </li> </ul> </li> <li>Data Format: The data is formatted one entry per line as follows (tab separated, "\t"): <ul> <li>File userid-timestamp-artid-artname-traid-traname.tsv: <pre>userid \t timestamp \t musicbrainz-artist-id \t artist-name \t musicbrainz-track-id \t track-name</pre> </li> <li>File userid-profile.tsv: <pre>userid \t gender ('m'|'f'|empty) \t age (int|empty) \t country (str|empty) \t signup (date|empty)</pre> </li> </ul> </li> <li>Example: <ul> <li>File userid-timestamp-artid-artname-traid-traname.tsv: <pre>user_000639 \t 2009-04-08T01:57:47Z \t MBID \t The Dogs D'Amour \t MBID \t Fall in Love Again? user_000639 \t 2009-04-08T01:53:56Z \t MBID \t The Dogs D'Amour \t MBID \t Wait Until I'm Dead ...</pre> </li> <li>File userid-profile.tsv: <pre>user_000639 \t m \t Mexico \t Apr 27, 2005 ...</pre> </li> </ul> </li> </ul>   LICENSE OF BOTH DATASETS. The data contained in both datasets is distributed with permission of <a href="http://last.fm">Last.fm</a>. The data is made available for non-commercial use. Those interested in using the data or web services in a commercial context should contact: partners [at] last [dot] fm For more information see Last.fm <a href="http://www.last.fm/api/tos">terms of service</a>   ACKNOWLEDGEMENTS. Thanks to Last.fm for providing the access to this data via their web services. Special thanks to <a href="http://www.last.fm/user/nova77LF">Norman Casagrande</a>.   REFERENCES. When using this dataset you must reference the <a href="http://last.fm">Last.fm</a> webpage. Optionally (not mandatory at all!), you can cite Chapter 3 of <a href="http://ocelma.net/MusicRecommendationBook/index.html">this book</a>: <pre>@book{Celma:Springer2010, author = {Celma, O.}, title = {{Music Recommendation and Discovery in the Long Tail}}, publisher = {Springer}, year = {2010} } </pre>   CONTACT: This data was collected by <a href="http://ocelma.net/">Òscar Celma</a> @ <a href="http://mtg.upf.edu">MTG</a>/<a href="http://upf.edu">UPF</a>

opencc-by-4.0Mar 2022View details →

Developing an in-depth understanding of the prevalence, risk factors and treatment recommendations for phantom limb pain, and patient-generated care priorities for people who have undergone lower limb amputations.

The file holds data collected for a series of four studies on phantom limb pain.

FIGURES 15–17. Aurisetiphora maggiesnowae new species. 15 in Two new genera of phorid flies, Macgrathphora and Aurisetiphora, from Costa Rica (Diptera: Phoridae), with recommendations for naming new genera in the family

FIGURES 15–17. Aurisetiphora maggiesnowae new species. 15. Head and thorax, dorsal. 16. Frons, anterior. 17. Male genitalia, posterior.

FIGURES 5–10. Wing male. 5–6. Macgrathphora caribbea new species. 5. Male. 6. Female. 7–8. Macgrathphora longifurca new species. 7. Male. 8. Female. 9. Macgrathphora pacifica new species, male. 10 in Two new genera of phorid flies, Macgrathphora and Aurisetiphora, from Costa Rica (Diptera: Phoridae), with recommendations for naming new genera in the family

FIGURES 5–10. Wing male. 5–6. Macgrathphora caribbea new species. 5. Male. 6. Female. 7–8. Macgrathphora longifurca new species. 7. Male. 8. Female. 9. Macgrathphora pacifica new species, male. 10. Aurisetiphora maggiesnowae new species, male.

FIGURES 1–4 in Two new genera of phorid flies, Macgrathphora and Aurisetiphora, from Costa Rica (Diptera: Phoridae), with recommendations for naming new genera in the family

FIGURES 1–4. Habitus, male, lateral. 1. Macgrathphora caribbea new species. 2. Macgrathphora longifurca new species. 3. Macgrathphora pacifica new species. 4. Aurisetiphora maggiesnowae new species.

FIGURES 11–14. Male genitalia, left lateral. 11. Macgrathphora caribbea new species. 12. Macgrathphora longifurca new species. 13. Macgrathphora pacifica new species. 14 in Two new genera of phorid flies, Macgrathphora and Aurisetiphora, from Costa Rica (Diptera: Phoridae), with recommendations for naming new genera in the family

FIGURES 11–14. Male genitalia, left lateral. 11. Macgrathphora caribbea new species. 12. Macgrathphora longifurca new species. 13. Macgrathphora pacifica new species. 14. Aurisetiphora maggiesnowae new species.

opencc-by-4.0Mar 2022View details →

Appendix of "Revisiting the Effect of Branch Handling Strategies on Change Recommendation"

This is the dataset of the paper "Revisiting the Effect of Branch Handling Strategies on Change Recommendation", presented at ICPC 2022. Files Overview <ul> <li>`path/to/merge_result.db`: SQLite database for the results of Imp_merge.</li> <li>`path/to/no_merge_result.db`: SQLite database for the results of Imp_no_merge.</li> </ul> Database Schema <ul> <li>Table: result <ul> <li>id: ID</li> <li>hexsha: the commit SHA-1</li> <li>algorithm_type: the algorithm used (SINGLE or OTHERS)</li> <li>history_type: the branch handling strategy used (FULL, FIRST_PARENTS, or BOTH). BOTH is used in case two strategies used the same commits.</li> <li>result_type: prediction result (SUCCESS, FAILURE, NO_PREDICTION, or NO_RULE). NO_RULE is used in case of no prediction and no rule.</li> <li>answer_file_name: the oracle filename</li> </ul> </li> <li>Table: prediction <ul> <li>id: ID</li> <li>result_id: refers to result.id</li> <li>file_name: the predicted filename</li> </ul> </li> <li>Table: query <ul> <li>id: ID</li> <li>result_id: refers to result.id</li> <li>file_name: the filename used as the query</li> </ul> </li> <li>Table: used_commit <ul> <li>id: ID</li> <li>result_id: refers to result.id</li> <li>hexsha: the commit SHA-1 to be used for recommendation</li> </ul> </li> <li>Table: rule <ul> <li>id: ID</li> <li>result_id: refers to result.id</li> <li>conclusion: the filename at the conclusion part</li> <li>confidence: the confidence value</li> <li>support: the support value</li> </ul> </li> <li>Table: rule_term (the condition part of a rule) <ul> <li>id: ID</li> <li>rule_id: refers to rule.id</li> <li>file_name: the filename at the condition part</li> </ul> </li> </ul>

opencc-by-4.0Jan 2022View details →

Dataset - Templates Recommendation in the Open Research Knowledge Graph

This dataset has been created for implementing a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts research paper's title and abstracts as input and recommends existing templates in the ORKG semantically relevant to the given paper.   Two approaches have been trained on this dataset in the context of <a href="https://doi.org/10.15488/11834">this master's thesis</a>, namely a Natural Language Inference (NLI) approach based on SciBERT embeddings and an unsupervised approach based on ElasticSearch.   This publication consists therefore of one general dataset, two training sets for each approach, validation set for the supervised approach and a test set for both approaches.   dataset.json The main JSON object consists of a list of templates and a list of neutral papers. Each template object has an ID, label, list of research fields, list of properties and list of papers using that template, whereas each paper object has ID, label, DOI, research field and abstract. Each neutral paper object has the same schema of a paper object using that template. See an example instance below.   <pre><code class="language-json">{ "templates": [ { "id": "R138668", "label": "Psychiatric Disorders AI Overview", "research_fields": [ { "id": "http://orkg.org/orkg/resource/R133", "label": "Artificial Intelligence" } ... ], "properties": [ "Study cohort", ... ], "papers": [ { "id": "R138698", "label": "Application of Autoencoder in Depression Diagnosis", "doi": "10.12783/dtcse/csma2017/17335", "research_field": { "id": "R104", "label": "Bioinformatics" }, "abstract": "Major depressive disorder (MDD) is a mental disorder characterized by at least two weeks of low mood which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. No method can automatically extract discriminative features from the origin time series in fMRI images for MDD diagnosis. In this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge. An autoencoder was used to learn pre-training parameters of a dimensionality reduction process using 3-D convolution network. Through comparison with the other three feature extraction methods, our method achieved the best classification performance. This method can be used not only in MDD diagnosis, but also other similar disorders." }, ... }, ... ] "neutral_papers": [ { "id": "R109377", "label": "Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants", "doi": "10.1016/j.jpha.2020.03.009", "research_field": { "id": "R104", "label": "Bioinformatics" }, "abstract": "Abstract The recent outbreak of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 in December 2019 raised global health concerns. The viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme controls coronavirus replication and is essential for its life cycle. 3CLpro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV). Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV. Therefore, herein, we analysed the 3CLpro sequence, constructed its 3D homology model, and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds. Our analyses revealed that the top nine hits might serve as potential anti- SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19." }, ... ] }</code></pre>   All other files The main JSON object consists of a list of entailments, a list of contradiction and a list of neutrals. Each object of the above mentioned lists has the same schema. An instance_id created by concatenating the template_id (when exists) with the paper_id, a template_id, a paper_id, premise (representing the paper's title), hypthesis (representing the paper's abstract), their concatenation in sequence and the target class. See an example instance below.   <pre><code class="language-json">{ "entailments": [ { "instance_id": "R138668xR138698", "template_id": "R138668", "paper_id": "R138698", "premise": "psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data", "hypothesis": "application of autoencoder in depression diagnosis major depressive disorder (mdd) is a mental disorder characterized by at least two weeks of low mood which is present across most situations diagnosis of mdd using rest state functional magnetic resonance imaging (fmri) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability no method can automatically extract discriminative features from the origin time series in fmri images for mdd diagnosis in this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge an autoencoder was used to learn pre training parameters of a dimensionality reduction process using 3 d convolution network through comparison with the other three feature extraction methods, our method achieved the best classification performance this method can be used not only in mdd diagnosis, but also other similar disorders", "sequence": "[CLS] psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data [SEP] application of autoencoder in depression diagnosis major depressive disorder (mdd) is a mental disorder characterized by at least two weeks of low mood which is present across most situations diagnosis of mdd using rest state functional magnetic resonance imaging (fmri) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability no method can automatically extract discriminative features from the origin time series in fmri images for mdd diagnosis in this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge an autoencoder was used to learn pre training parameters of a dimensionality reduction process using 3 d convolution network through comparison with the other three feature extraction methods, our method achieved the best classification performance this method can be used not only in mdd diagnosis, but also other similar disorders [SEP]", "target": "entailment" }, ... ], "contradictions": [ ... ], "neutrals": [ ... ] } </code></pre>   Statistics <table align="center"> <tbody> <tr> <td>-</td> <td>Training (supervised)</td> <td>Validation (supervised)</td> <td>Training (unsupervised)</td> <td>Test</td> </tr> <tr> <td>Entailment</td> <td>180</td> <td>20</td> <td>200</td> <td>52</td> </tr> <tr> <td>Neutral</td> <td>180</td> <td>20</td> <td>200</td> <td>64</td> </tr> <tr> <td>Contradictrion</td> <td>736</td> <td>84</td> <td>0</td> <td>0</td> </tr> <tr> <td>Total</td> <td>1096</td> <td>124</td> <td>400</td> <td>116</td> </tr> </tbody> </table>

opencc-by-4.0Jun 2022View details →

Replication Package for "Automating Code Review: Using Deep Learning to Assess the Quality of Code Contributions and Recommend Changes"

Replication Package for "Automating Code Review: Using Deep Learning to Assess the Quality of Code Contributions and Recommend Changes"

opencc-by-4.0Oct 2017View details →

Supplementary Material for the Paper "Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development"

Contains the supplementary material for the paper "Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development" submitted to CSCW'18. All contents are explained in the file README.txt. Abstract: One way to improve the productivity of knowledge workers is to increase their self-awareness about productivity at work through self-monitoring. Yet, little is known about expectations of, the experience with and the impact of self-monitoring in the workplace. To address this gap, we studied software developers, as one community of knowledge workers. We used an iterative, feedback-driven development approach (N=20) and a survey (N=413) to infer design elements for workplace self-monitoring, which we then implemented as a technology probe called WorkAnalytics. We field-tested these design elements during a three-week study with software development professionals (N=43). Based on the results of the field study, we present design recommendations for self-monitoring in the workplace, such as using experience sampling to increase the awareness about work and to create richer insights, the need for a large variety of different metrics to retrospect about work, and that actionable insights, enriched with benchmarking data from co-workers, are likely needed to foster productive behavior change at work. Source Code: The source code of WorkAnalytics can be found on <a href="https://github.com/sealuzh/PersonalAnalytics">GitHub</a> (under the original name PersonalAnalytics). WorkAnalytics was built with Microsoft's Dot.Net framework in C# and can be used on the Windows 7, 8 and 10 operating system.

FIGURE 27. A in Collection management and study of microscope slides: Storage, profiling, deterioration, restoration procedures, and general recommendations

FIGURE 27. A. Absorbent purified cotton wool wrapped on skewer. B. Coverslips 24 mm x 24 mm with paraffin rings ready for mounting each specimen in a drop of glycerol on a Cobb aluminum slide. C, D. Slide mounting device for Cobb aluminum slides. E. Storage of labels in 4-flap paper envelopes filed in cardboard boxes provided by Klug Conservation. F, G. Storage of labels in Secol polyester slide and negative film preserver sheets housed in a custom-made cardboard file system made by Klug Conservation. Note certificate of deacidification with Bookkeeper™ in front of the labels (F) and sheet of paper behind labels in polyester envelope (G). H, I. Restoration of a microscope slide by soaking the slide in distilled water on a hot plate in the lower part of a Petri dish (Pd) covered with a watch glass (wg) before (H) and after removal of labels and their storage under the top of the Petri dish (I, toPd). Note drops of water dripping back into lower Petri dish (I).

FIGURE 26 in Collection management and study of microscope slides: Storage, profiling, deterioration, restoration procedures, and general recommendations

FIGURE 26. Chemical structure of ingredients of shellac from the hemipteran Kerria lacca and related species.

FIGURE 23 in Collection management and study of microscope slides: Storage, profiling, deterioration, restoration procedures, and general recommendations

FIGURE 23. Chemical structure of monoterpenes, seaquiterpenes, and more volatile ingredients of gum mastic from Pistacia lentiscus.

FIGURE 21 in Collection management and study of microscope slides: Storage, profiling, deterioration, restoration procedures, and general recommendations

FIGURE 21. Chemical structure of the ingredients of gum colophony (= rosin, Greek pitch) from various species of Pinus.

FIGURE 20. A-F in Collection management and study of microscope slides: Storage, profiling, deterioration, restoration procedures, and general recommendations

FIGURE 20. A-F. Kinorhyncha mounted in Fluoromount G™ between 2002 and 2008 (A, B), 2003 and 2007 (C, D: same slide), and 2006 and 2010 (E) by Sørensen (A-E) and 1999 and 2012 by Herranz (F). Currently not affected mounting medium (mo) with severe deterioration like cavities (ca), platelet-like (A), rectangularly growing (B-D), radially growing (B-E, arrowheads), small chaotic (D) crystals (cy), and segregation of ingredients of mounting medium resulting in formation of bubbles (F). Specimens marked by asterisks. A, DF: DIC; B, C: bright field illumination. Scalebars: A, E, 300 µm; B, C, 2 mm; D, 500 µm; F, 200 µm.

FIGURE 18. A in Collection management and study of microscope slides: Storage, profiling, deterioration, restoration procedures, and general recommendations

FIGURE 18. A. Polychaete in glycerol-gelatin with unharmed medium (mo), formation of cavities (ca), and coverslip cracked above specimen (arrowheads). B-H. Kinorhyncha mounted in polyvinyl lactophenol between 1985 and 1990 by Neuhaus, unringed; currently not affected mounting medium (mo) and areas at different stages of deterioration with extensive cavities (ca), drops of fluid (fl; B, F), and formation of different kinds of crystals (cy). B, C. Overview of damage on two slides. Arrowheads mark specimens. D-F. Details of radially growing (D, F), rectangularly growing (F, H), leaf-like (D, E, G), and tree-like (H) crystals. A-C: bright field illumination; D-H: DIC. Scalebars: A-C, 5 mm; D-H, 500 µm.

FIGURE 19. A-D in Collection management and study of microscope slides: Storage, profiling, deterioration, restoration procedures, and general recommendations

FIGURE 19. A-D. Kinorhyncha mounted in polyvinyl lactophenol between 1985 and 1990 by Neuhaus, unringed; currently not affected mounting medium (mo) and areas at different stages of deterioration with extensive cavities (ca) and formation of different kinds of crystals (cy). Specimens marked by asterisk. E, F. Aphidina mounted in Polyviol; now deteriorating with cavities and crystals. A-F: DIC. Scalebars: A-D, 500 µm.