cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4683352
title: UIMA ConceptMapper Dictionaries for the Annotation of the 2021 BioASQ Corpus with Drug Names and Terms from Epilepsy Ontologies

description: The dictionary files for the annotation of free text with terms from epilepsy ontologies by the UIMA ConceptMapper are taken from NCBO BioPortal, namely from the ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS: https://bioportal.bioontology.org/ontologies/EPSO https://bioportal.bioontology.org/ontologies/ESSO https://bioportal.bioontology.org/ontologies/EPILONT https://bioportal.bioontology.org/ontologies/EPISEM https://bioportal.bioontology.org/ontologies/FENICS The dictionary for the identification of drug names is derived from the DrugBank vocabulary available online at https://go.drugbank.com/releases/latest#open-data. Further descriptions of making use of the UIMA-based text mining workflow can be found in the following publications: Bernd Müller, Alexandra Hagelstein: Beyond Metadata: Enriching life science publications in Livivo with semantic entities from the linked data cloud. SEMANTiCS (Posters, Demos, SuCCESS) 2016 Bernd Müller, Alexandra Hagelstein, Thomas Gübitz: Life Science Ontologies in Literature Retrieval: A Comparison of Linked Data Sets for Use in Semantic Search on a Heterogeneous Corpus. EKAW (Satellite Events) 2016: 158-161 Bernd Müller, Christoph Poley, Jana Pössel, Alexandra Hagelstein, Thomas Gübitz: LIVIVO - the Vertical Search Engine for Life Sciences. Datenbank-Spektrum 17(1): 29-34 (2017) Bernd Müller, Dietrich Rebholz-Schuhmann: Selected Approaches Ranking Contextual Term for the BioASQ Multi-label Classification (Task6a and 7a). PKDD/ECML Workshops (2) 2019: 569-580 The dictionary files are in particular: Dict_DrugNames.xml - constructed from the DrugBank vocabulary Dict_EpSO.xml - constructed from the EpSO ontology Dict_ESSO.xml - constructed from the ESSO ontology Dict_EPILONT.xml - constructed from the EPILONT ontology Dict_EPISEM.xml - constructed from the EPISEM ontology Dict_FENICS.xml - constructed from the FENICS ontology The dictionaries were used with the UIMA ConceptMapper for the annotation of the 2021 BioASQ corpus resulting in the BioASQ Sub-Corpus for the Pharmacology of Epilepsy (BioPepsy). Please cite this data as: Müller, Bernd. UIMA ConceptMapper Dictionaries for the Annotation of the 2021 BioASQ Corpus with Drug Names and Terms from Epilepsy Ontologies. ZENODO, 10.5281/zenodo.4683353
DOI: 10.5281/zenodo.3668647
title: Doctoral Students' Educational Needs in Research Data Management: Quantitative Data of Perceived Importance and Current Competencies

description: These data sets include numerically coded answers to Likert-like scale questions concerning the importance and perceived current research data management competencies of doctoral students. Interviewees were 35 doctoral students and faculty members. Interview forms are attached. The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684
description: The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684
DOI: 10.5281/zenodo.3668646
title: Doctoral Students' Educational Needs in Research Data Management: Quantitative Data of Perceived Importance and Current Competencies

description: These data sets include numerically coded answers to Likert-like scale questions concerning the importance and perceived current research data management competencies of doctoral students. Interviewees were 35 doctoral students and faculty members. Interview forms are attached. The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684
description: The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684
DOI: 10.5281/zenodo.5428996
title: Verification of ORM-based Controllers by Summary Inference

description: Submission data for ICSE 2022 Verification of ORM-based Controllers by Summary Inference. The folder structure in this repository is as follows. ControllerVerificationData contains results corresponding to Section 5.2 of<br> the paper. The folder hierarchy within this is: benchmark name -&gt; Controller class name -&gt; "Alloy" -&gt; a .als file (i.e., Alloy model/summary generated by our tool)<br> corresponding to each controller method, with assertions added<br> manually in all cases were they were expressible. "Output" -&gt; outputs from Alloy for the assertions. multipage-data contains results corresponding to Section 5.3 of the paper. "properties" -&gt; each .txt file contains a property (basically, a \psi as in Section<br> 3.1 in the paper), written by us, in Alloy form. (For "Unexpressed"<br> properties, we wrote a dummy property.) "toCheck" -&gt; a property number -&gt; Generated Alloy files for this property (one Alloy file for Property<br> (A) and one each for Property (I), see Section 3.1 in the paper) A .txt file for each Alloy file, containing Alloy checker's output. "summary.txt" contains a summary of all the information above Mutation-Analysis contains results corresponding to Section 5.5 of the<br> paper. Each leaf level folder under this contains: Alloy file generated for a mutated version of the benchmark, with<br> assertions added manually. The description of the mutation (in the .desc file) A .out file containing output from the Alloy checker for the assertions<br> in the .als file. JPF contains results corresponding to Section 5.6 of the paper. Each leaf-level folder contains results pertaining to one of the 16<br> assertions. The .java file contains a driver that initializes the tables,<br> calls the controller, and checks the assertion. The .jpf file is required<br> by JPF, and the .out file shows the JPF output. We will positively consider taking steps to make our tool open-source in<br> the near future. As of today there are some licensing issues due to our<br> dependence on DBridge source.<br>
DOI: 10.26180/5e1e801746c3a
title: Human Cathedral

description: <sub>On this page you will find the score materials, previous recordings, reviews, program notes and performance instruction for the music composition entitled;<br><br></sub><b>Human Cathedral</b>For 2 bass guitars and effects.Written in 2019 for French bassist Kasper Toeplitz.<br><br>This work can be performed as a duo and solo work.You will need the Decibel ScorePlayer to read this work (see below).<br><i><sub><b>NOTE FOR PERFORMERS</b></sub></i><br><sub>The majority of my compositions use scores that are read on an iPad tablet computer, using the </sub>Decibel ScorePlayer<sub>, an application available on the </sub>App Store <sub>. Any fixed media is embedded in the score, and some feature automated functions. In the case of ensemble works, multiple iPads can be networked on a LAN or over the Internet so parts can be read in a synchronised way. You should upload the score file (ending with.dsz) to your iPad from your computer via </sub>AirDrop<sub> on an Apple, or cable from a PC. Instructions on how to do this and using the </sub>Decibel ScorePlayer<sub>, more generally are included in the </sub>Application,<sub> which ships with five other scores.</sub><sub> </sub><sub>Thus my works have different versions of the score, as you may see above. A PDF/PNG file of the score ‘image’, a DSZ file to upload onto the iPad for performance, and for some less complex scores, a video version. Hardcopies are also available from my publisher. You can find out more about the Decibel ScorePlayer, and how to make your own scores for it, here.</sub><br><br>
DOI: 10.5281/zenodo.5442281
title: ddlTerm

description: ddlTerm is a data-driven loop termination analysis tool <strong>Install</strong> Download the built docker image directly. Then: <pre><code>docker load -i ddltermartifact.tar</code></pre> <strong>Run Experiments</strong> Start the docker image (replace `<em>/path/to/a/directory/to/save/the/result</em>`) <pre><code>docker run -it -v /path/to/a/directory/to/save/the/result:/log --tmpfs /tmpfs --cpus=1 ddltermartifact:latest</code></pre> Run the experiments (e.g. the main experiment in our paper) <pre><code>cd ddlTerm python3 experiment/scripts/RunTasks.py experiment/scripts/configurations/ExpMain/LeNLeMixed_Standard.xml</code></pre> <strong>For a precise result, we suggest:</strong> using tmpfs to reduce the time of IO. (By default, tmpfs has been used in docker running) setting the CPU limit to one core using cgroup. (By default, CPU limit has been used in docker running) updating the timeout setting in configuration XML files according to the performance of your machine. <strong>The baseline in Our Paper:</strong> If you want to make a comparative experiment, please follow the install instruments for our baseline tools. AProVE Ultimate Automizer MuVal FreqTerm <strong>Remember</strong> to set the cpu limit to one core using cgroup or by the parameter <code>--cpus</code> if using <code>docker</code>. The benchmarks locate in <code>experiment/benchmarks</code>. C-style can be used for <code>AProVE</code>, <code>Ultimate Automizer</code> and <code>MuVal</code>. Horn-style can be used for <code>FreqTerm</code>. We provide some useful scripts in <code>experiment/baseline.scripts</code> to run the experiments for <code>AProVE</code>, <code>Ultimate Automizer</code> and <code>FreqTerm</code>. You can modify the path in the scripts to run these tools. We hope these scripts are useful for you.
DOI: 10.5281/zenodo.5442280
title: ddlTerm

description: ddlTerm is a data-driven loop termination analysis tool <strong>Install</strong> Download the built docker image directly. Then: <pre><code>docker load -i ddltermartifact.tar</code></pre> <strong>Run Experiments</strong> Start the docker image (replace `<em>/path/to/a/directory/to/save/the/result</em>`) <pre><code>docker run -it -v /path/to/a/directory/to/save/the/result:/log --tmpfs /tmpfs --cpus=1 ddltermartifact:latest</code></pre> Run the experiments (e.g. the main experiment in our paper) <pre><code>cd ddlTerm python3 experiment/scripts/RunTasks.py experiment/scripts/configurations/ExpMain/LeNLeMixed_Standard.xml</code></pre> <strong>For a precise result, we suggest:</strong> using tmpfs to reduce the time of IO. (By default, tmpfs has been used in docker running) setting the CPU limit to one core using cgroup. (By default, CPU limit has been used in docker running) updating the timeout setting in configuration XML files according to the performance of your machine. <strong>The baseline in Our Paper:</strong> If you want to make a comparative experiment, please follow the install instruments for our baseline tools. AProVE Ultimate Automizer MuVal FreqTerm <strong>Remember</strong> to set the cpu limit to one core using cgroup or by the parameter <code>--cpus</code> if using <code>docker</code>. The benchmarks locate in <code>experiment/benchmarks</code>. C-style can be used for <code>AProVE</code>, <code>Ultimate Automizer</code> and <code>MuVal</code>. Horn-style can be used for <code>FreqTerm</code>. We provide some useful scripts in <code>experiment/baseline.scripts</code> to run the experiments for <code>AProVE</code>, <code>Ultimate Automizer</code> and <code>FreqTerm</code>. You can modify the path in the scripts to run these tools. We hope these scripts are useful for you.
DOI: 10.6084/m9.figshare.15141078
title: Supplemental Table S1

description: Supplemental material for JDS.2021-2059, entitled Effect of oral administration of timothy hay and psyllium on the growth performance and fecal microbiota of pre-weaned calves
DOI: 10.6084/m9.figshare.15154644
title: Supplemental Table S2

description: Supplemental material for JDS.2021-2059, entitled Effect of oral administration of timothy hay and psyllium on the growth performance and fecal microbiota of pre-weaned calves
DOI: 10.15161/oar.it/74071
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.6084/m9.figshare.15147465.v1
title: Supplemental Table S5

description: Supplemental material for JDS.2021-2059, entitled Effect of oral administration of timothy hay and psyllium on the growth performance and fecal microbiota of pre-weaned calves
DOI: 10.6084/m9.figshare.15147465.v2
title: Supplemental Table S5

description: Supplemental material for JDS.2021-2059, entitled Effect of oral administration of timothy hay and psyllium on the growth performance and fecal microbiota of pre-weaned calves
DOI: 10.5281/zenodo.5442197
title: Data of Black-Box Side-Channel Detection and Mitigation for Internet of Things

description: This repository contains the datasets used in the experiments of the paper "Black-Box Side-Channel Detection and Mitigation for Internet of Things" and the results of the experiments.
DOI: 10.5281/zenodo.5442196
title: Data of Black-Box Side-Channel Detection and Mitigation for Internet of Things

description: This repository contains the datasets used in the experiments of the paper "Black-Box Side-Channel Detection and Mitigation for Internet of Things" and the results of the experiments.
DOI: 10.5281/zenodo.4915758
title: NCoR1 and SMRT fine-tune inflammatory versus tolerogenic balance in dendritic cells by differentially regulating STAT3 signaling

description: Dendritic cell (DC) fine-tunes inflammatory versus tolerogenic responses to protect from immune-pathology. However, the role of co-regulators in maintaining this balance is unexplored. NCoR1-mediated repression of DC immune-tolerance has been recently reported. Here we found that depletion of NCoR1 paralog SMRT enhanced cDC141 activation and expression of IL-6, IL-12 and IL-23 while concomitantly decreasing IL-10 expression/secretion. Consequently, co-cultured CD4+ and CD8+ T-cells depicted enhanced Th1/Th17 frequency and cytotoxicity, respectively. Comparative genomic analysis demonstrated differential regulation of IL-10 by SMRT and NCoR1. SMRT depletion repressed mTOR-STAT3-IL10 signaling in cDC1 by down-regulating NR4A1. Besides, NFkBIA and SOCS3 were down-regulated in SMRT knockdown cDC1, supporting increased production of inflammatory cytokines. Moreover, adoptive transfer of SMRT knockdown cDC1 in OVA-DTH induced footpad inflammation led to increased Th1/Th17 and reduced tumor burden after B16 melanoma injection by enhancing oncolytic CD8+ T-cell frequency, respectively. We also depicted decreased Smrt expression in Rheumatoid Arthritis, a Th1/Th17 disease.
DOI: 10.5281/zenodo.4911207
title: NCoR1 and SMRT fine-tune inflammatory versus tolerogenic balance in dendritic cells by differentially regulating STAT3 signaling

description: Dendritic cell (DC) fine-tunes inflammatory versus tolerogenic responses to protect from immune-pathology. However, the role of co-regulators in maintaining this balance is unexplored. NCoR1-mediated repression of DC immune-tolerance has been recently reported. Here we found that depletion of NCoR1 paralog SMRT enhanced cDC141 activation and expression of IL-6, IL-12 and IL-23 while concomitantly decreasing IL-10 expression/secretion. Consequently, co-cultured CD4+ and CD8+ T-cells depicted enhanced Th1/Th17 frequency and cytotoxicity, respectively. Comparative genomic analysis demonstrated differential regulation of IL-10 by SMRT and NCoR1. SMRT depletion repressed mTOR-STAT3-IL10 signaling in cDC1 by down-regulating NR4A1. Besides, NFkBIA and SOCS3 were down-regulated in SMRT knockdown cDC1, supporting increased production of inflammatory cytokines. Moreover, adoptive transfer of SMRT knockdown cDC1 in OVA-DTH induced footpad inflammation led to increased Th1/Th17 and reduced tumor burden after B16 melanoma injection by enhancing oncolytic CD8+ T-cell frequency, respectively. We also depicted decreased Smrt expression in Rheumatoid Arthritis, a Th1/Th17 disease.
DOI: 10.5281/zenodo.5443946
title: The list of papers about IntelliJ plugins

description: This spreadheet provides the information about various papers that describe IntelliJ plugins. The spreadsheet has the following columns: 1. <strong>Year —</strong> year of the publication.<br> 2. <strong>Authors </strong>— authors of the publication.<br> 3. <strong>Title </strong>— title of the puplication.<br> 4. <strong>Venue </strong>— venue where the publication was presented.<br> 5. <strong>IDE </strong>— a specific IntelliJ IDE that the plugin supports.<br> 6. <strong>Functionality </strong>— a very brief description of what the plugin does.
DOI: 10.5281/zenodo.5443945
title: The list of papers about IntelliJ plugins

description: This spreadheet provides the information about various papers that describe IntelliJ plugins. The spreadsheet has the following columns: 1. <strong>Year —</strong> year of the publication.<br> 2. <strong>Authors </strong>— authors of the publication.<br> 3. <strong>Title </strong>— title of the puplication.<br> 4. <strong>Venue </strong>— venue where the publication was presented.<br> 5. <strong>IDE </strong>— a specific IntelliJ IDE that the plugin supports.<br> 6. <strong>Functionality </strong>— a very brief description of what the plugin does.
DOI: 10.25625/w072du/uyeqey
title: Azhar et al parasitoid wasps_Braconidae_Matrix_DS.csv

description: :unav
DOI: 10.6084/m9.figshare.16529160.v1
title: Supplementary files: The association between FOXO3a rs4946936 gene polymorphism and the levels of FOXO3a among chronic granulocytic leukemia patients treated with imatinib mesylate

description: Supplementary files: The association between FOXO3a rs4946936 gene polymorphism and the levels of FOXO3a among chronic granulocytic leukemia patients treated with imatinib mesylate.<br>1. Strobe checklist2. Data sheet
DOI: 10.25625/ewehfv/2mo9mw
title: Azhar et al parasitoid wasps_Braconidae_Matrix_RS.csv

description: :unav
DOI: 10.25345/c5654m
title: MassIVE MSV000088059 - GNPS_Trial_Workshop_Metabolomics_Homo_Sapiens
title: trial trail and trial bla lbl al, homo sapiens, metabolomics
DOI: 10.6084/m9.figshare.16570524
title: The bigwig file of HIF1a CUTTag data

description: The supplementary file of APLN paper
DOI: 10.5281/zenodo.5444887
title: Artifacts for Fuzzing SMT Solvers with Diversified Sub-formulas

description: <pre><code class="language-markdown">## Installation: ``` virtualenv --python=/usr/bin/python3 virenv source virenv/bin/activate cd octopus python3 setup.py install ``` ## Usage: ``` octopus --benchmark=[PATH TO SEED DIRECTORY] --solverbin=[PATH TO SOLVER BIN] --solver=[SOLVER NAME] --theory=[SOLVER THEORY] ``` For example: ``` octopus --benchmark=/home/SMT2021 --solverbin=../z3/build/z3 --solver=z3 --theory=LIA ``` To run `n` parallel instances of octopus on `n` cores, use the `--cores` flag. For example: ``` octopus --benchmark=[PATH TO SEED FILES] --solverbin=[PATH TO SOLVER BIN] --solver=[SOLVER NAME] --theory=LIA --cores=10 ``` ## Refutational soundness bugs detected so far: octopus has detected many new "refutational soundness" bugs in Z3. Here is a list of issues we reported. https://github.com/Z3Prover/z3/issues/5373 `[z3]` `[QF_NRA]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5443 `[z3]` `[QF_BVFP]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5447 `[z3]` `[QF_ABV]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5456 `[z3]` `[QF_IDL]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5457 `[z3]` `[QF_BV]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5460 `[z3]` `[QF_BV]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5468 `[z3]` `[AUFLIRA]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5488 `[z3]` `[QF_BV]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5502 `[z3]` `[QF_NIA]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5508 `[z3]` `[QF_NIA]` &lt;br&gt; https://github.com/Z3Prover/z3/issues/5423 `[z3]` `[QF_BVFP]` `Won't fixed` &lt;br&gt; </code></pre>
DOI: 10.6084/m9.figshare.16570518
title: CRL

description: Fetal ultrasound dataset (CRL)
DOI: 10.25646/8994
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.15034631
title: A Comparison of Primers in 16S rRNA Gene Surveys of Bacteria and Archaea from Volcanic Caves

description: The construction of amplified 16S rRNA gene libraries has been a major methodology for bacterial/archaeal environmental community profiling for decades. Over the years, a variety of alternative primer sets targeting different portions of the rRNA gene have been used. Gradually a widespread collaborative effort, supported by the Earth Microbiome Project (EMP) and other community initiatives, has settled on primers targeting the V4 region (515F/806R) of the 16S rRNA gene, which amplifies both Archaea and Bacteria. Understudied volcanic cave ecosystems possess a high proportion of unclassified bacterial and archaeal lineages, highlighting the selection of optimal primers for community analysis in this environment. Therefore, we investigated the coverage of EMP and alternative archaea-specific (519F/1017R) and bacteria-specific (27F/515R) primers, using microbial and mineral deposits from two volcanic caves. <i>In silico</i> and sequencing analyses showed that the two bacterial primers captured similar amounts and types of Bacteria, while significant differences in the type and abundance of taxa detected between the EMP primers and archaea-specific primers were noted. Our results validate the use of V4 EMP primers for Bacteria in this cave ecosystem, but strongly suggest that Archaea diversity is better captured with the archaea-specific primer in the cave deposits studied.
DOI: 10.6084/m9.figshare.15034631.v1
title: A Comparison of Primers in 16S rRNA Gene Surveys of Bacteria and Archaea from Volcanic Caves

description: The construction of amplified 16S rRNA gene libraries has been a major methodology for bacterial/archaeal environmental community profiling for decades. Over the years, a variety of alternative primer sets targeting different portions of the rRNA gene have been used. Gradually a widespread collaborative effort, supported by the Earth Microbiome Project (EMP) and other community initiatives, has settled on primers targeting the V4 region (515F/806R) of the 16S rRNA gene, which amplifies both Archaea and Bacteria. Understudied volcanic cave ecosystems possess a high proportion of unclassified bacterial and archaeal lineages, highlighting the selection of optimal primers for community analysis in this environment. Therefore, we investigated the coverage of EMP and alternative archaea-specific (519F/1017R) and bacteria-specific (27F/515R) primers, using microbial and mineral deposits from two volcanic caves. <i>In silico</i> and sequencing analyses showed that the two bacterial primers captured similar amounts and types of Bacteria, while significant differences in the type and abundance of taxa detected between the EMP primers and archaea-specific primers were noted. Our results validate the use of V4 EMP primers for Bacteria in this cave ecosystem, but strongly suggest that Archaea diversity is better captured with the archaea-specific primer in the cave deposits studied.