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About 10,000 papers for “Reference database”
Mieke Titulaer · OSF Preprints (OSF Preprints) · 2017
Reference database for "Molecular analysis of stomach contents reveals important grass seeds in the winter diet of Baird's and Grasshopper sparrows, two declining grassland bird species"
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John F. Caratti · 2013
This chapter describes the data compilation process for the Landscape Fire and Resource Management Planning Tools Prototype Project (LANDFIRE Prototype Project) reference database (LFRDB) and explains the reference
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Chaimaa, Tarzi · Zenodo (CERN European Organization for Nuclear Research) · 2024
This repository aims to gather different databases used as reference for the genome-scale metabolic model reconstruction software tools presented in the paper: C. Tarzi, G. Zampieri, N. Sullivan, C. Angione, "Emerging methods for genome-scale metabolic modeling"
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Mieke Titulaer · OSF Preprints (OSF Preprints) · 2017
Reference database for "Molecular analysis of stomach contents reveals important grass seeds in the winter diet of Baird's and Grasshopper sparrows, two declining grassland bird species"
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Marc Navarro, Dennis Koelma · 2003
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Mi, Kai · Figshare · 2024
Genome-function reference database for RFW (Reference based functional profile inference on WMS).--fundb-COG.db--fundb-EC.db--fundb-KO.db--taxonomyInfoDB.db
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I. Chrissochoidis · UCL Discovery (University College London) · 2008
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Gesellschaft für Anlagen- und Reaktorsicherheit · Open MIND · 2011
GRS; 265, engl
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Klaas Ruppel, Toni Suutari · 2014
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Landels-Hill Big Creek Reserve, University Of California Natural Reserve System, Dellavalle, Mary · UC Santa Barbara · 2005
Catalog of data sets held at Point Lobos State Reserve. Each entry includes title, author, location, and topics. Includes data on marine invertebrates and algae (â Point Lobos Subtidal Monitoring - Swath Countsâ ) for 9/3/1998, and other counts for 11/1996.
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Kai Mi · Figshare · 2024
Genome-function reference database for RFW (Reference based functional profile inference on WMS).--fundb-COG.db--fundb-EC.db--fundb-KO.db--taxonomyInfoDB.db
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P Arnaud, Christian Brohet, Sergio Dallavolta et al. · 1985
Toallow anexchange ofmeasurements andcriteria between different electrocardiogra- phic(ECG)computerprograms, an international cooperative project hasbeeninitiated aimedat standardization ofcomputer-derived ECGmeasurements. Tothis endanECGreference library of250 ECGswithselected abnormalities was established andacomprehensive reviewing scheme was devised forthevisual determination oftheonsets andoffsets ofP,QRS,andTwaves.Thistask was performed byagroupofcardiologists onhighly amplified, selected complexes fromthelibrary ofECGs.Withuse ofamodified Delphi approach, individual outlying point estimates wereeliminated infour successive rounds. Inthis way final referee estimates were obtained that proved tobehighly reproducible and precise. Thisreference data base was used tostudy measurement results obtained withnine vectorcar- diographic and10standard 12-lead ECGanalysis programs.Themedians ofprogramdeterminations of P,QRS,andTwave onsetsandoffsets wereclose tothefinal referee estimates. However, animportant variability could bedemonstrated between measurementsfromindividual programs andmean differ- encesfromthereferee estimates amounted to10msec forQRSforcertain programs.Inaddition, the variances ofall programswith respect tothereferee point estimates were variable. Someprograms proved tobemore accurate andstable whenthedata fromhigh- vslow-noise recordings wereanalyzed. Average Q wave durations calculated fromECGsforwhich programsagreed onthe presenceofaQ or QSwave differed bymore than8msec inseveral program-to-program comparisons. Suchdifferences may haveimportant consequenceswith respect todiagnostic performance. Various factors that might explain these differences havebeendetermined. Thepresent study demonstrates that toallow an exchange ofresults anddiagnostic criteria between different ECG computer programs,definitions, minimum wave requirements, andmeasurement procedures urgently needtobestandardized. Circulation 71,No.3,523-534, 1985. DURINGthelast decade rapid growth hasoccurred in computer electrocardiographic
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John Vollmers · Zenodo (CERN European Organization for Nuclear Research) · 2021
MDMcleaner reference database used at time of publication. based on: GTDB release r95 RefSeq release 203 Silva version 138.1
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Americanae (AECID Library) · 2014
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Ben J. Woodcroft, Samuel T. N. Aroney, Zhao Rossen · Zenodo (CERN European Organization for Nuclear Research) · 2021
DEPRECATED: See https://zenodo.org/record/5739612 SingleM reference database. Metapackage containing packages for 59 broad-spectrum single-copy marker genes. Associated sequences and taxonomy based on GTDB r202 release.
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Maulana Malik Nashrulloh, Ninda Rachmadani, Irfan Mustafa et al. · Zenodo (CERN European Organization for Nuclear Research) · 2026
General InformationThis is the default database of pyTax4Fun2, a Python implementation of the Tax4Fun2 pipeline for prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Originally, this database was the default for Tax4Fun2 v1.1.5, as described in the publication "Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences" [1]. It was later made available by W. Song at https://doi.org/10.5281/zenodo.10035668 following the closure of the AARNet CloudStor service (formerly at cloudstor.aarnet.edu.au). While we have aimed to preserve the original structure as faithfully as possible, we have introduced several modifications to suit our implementation: We replaced the original KEGG KO-to-pathway mapping file (ko2ptw.txt) used in Tax4Fun2 with a customized version better aligned with our implementation. The original Tax4Fun2 ko2ptw.txt is retained as ko2ptw.txt.original_Tax4Fun2 inside KEGG.zip. Just rename ko2ptw.txt.original_Tax4Fun2 to ko2ptw.txt, and currently active ko2ptw.txt to ko2ptw.txt.original_pyTax4Fun2 if you consider to use Tax4Fun2 instead of pyTax4Fun2. fix_ko2ptw.py, a Python script for converting the original Tax4Fun2 ko2ptw.txt to pyTax4Fun2-suitable ko2ptw.txt. For TOOLS.zip, we retained the original binaries shipped with the default database provided by W. Song, including Diamond v0.9.24 [2], Prodigal v2.6.3 [3], and VSEARCH v2.14.1 [4]. In our version of the database, we also included recent versions of Diamond (v2.1.15) [5] and VSEARCH (v2.30.4), which are compatible with and have been tested in our implementation. All binaries are provided in their original, unmodified state. As these tools are licensed under the GNU General Public License v3.0, copies of their respective licenses are included in TOOLS.zip to comply with the terms of the GNU General Public License v3.0. Since this database is derived from the original Tax4Fun2 database, which is distributed under the GNU General Public License v3.0, it remains available under the same license. However, our implementation—pyTax4Fun2—is distributed under the GNU Affero General Public License v3.0. Citation Currently, pyTax4Fun2 do not have paper dedicated to it... For now. However, we at Generasi Biologi Indonesia Foundation indeed have 2 technical reports [6,7] and an undergraduate thesis (or, in Indonesian, a skripsi) [8], which we recommend to cite if you use pyTax4Fun2 in your research. When citing pyTax4Fun2, please additionally cite the original Tax4Fun2 paper, which developed the actual method. Until a paper dedicated to pyTax4Fun2 published online, you can for instance cite all works as: pyTax4Fun2 (Nashrulloh and Rahardi, 2026; Rachmadani et al., 2026; Rachmadani, 2026), a Python implementation to Tax4Fun2 (Wemheuer et al., 2020). Zenodo entry for the database can be cited as well if you use the database only. References Wemheuer, F., Taylor, J.A., Daniel, R., Johnston, E., Meinicke, P., Thomas, T., Wemheuer, B. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environmental Microbiome 15, 11 (2020). doi: 10.1186/s40793-020-00358-7. Buchfink, B., Xie, C., Huson, D.H. Fast and sensitive protein alignment using DIAMOND. Nature Methods 12, 59-60 (2015). doi: 10.1038/nmeth.3176. Hyatt, D., Chen, G.L., LoCascio, P.F., Land, M.L. Larimer, F. W. Hauser, L. J. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010). doi: 10.1186/1471-2105-11-119. Rognes, T., Flouri, T., Nichols, B., Quince, C., Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016). doi: 10.7717/peerj.2584. Buchfink, B., Reuter, K., Drost, H.G., Sensitive protein alignments at tree-of-life scale using DIAMOND, Nature Methods 18, 366–368 (2021). doi: 10.1038/s41592-021-01101-x. Nashrulloh, M.M., Rahardi, B. pyTax4Fun2: A Python Tool for Functional Profiling and Redundancy Analysis of Bacterial Communities via 16S rRNA Gene Sequences, Featuring Polars for Efficient Processing of Large Genomic Datasets—I: Initial Development (Technical Report No. GBR-TR-BIOMIKA-02/Genbinesia/I/2026). Generasi Biologi Indonesia Foundation. Gresik, Indonesia (2026). Rachmawati, N., Nashrulloh, M.M., Mustafa, I., Rahardi, B., Ainiyati, C., Hafazallah, K., Raihandhany, R., Tamam, Mh.B. pyTax4Fun2: A Python Tool for Functional Profiling and Redundancy Analysis of Bacterial Communities via 16S rRNA Gene Sequences, Featuring Polars for Efficient Processing of Large Genomic Datasets—II: Further Development (On Alpha Diversity and Beta Diversity) (Technical Report No. GBR-TR-BIOMIKA-04/Genbinesia/III/2026). Generasi Biologi Indonesia Foundation. Gresik, Indonesia (2026). Rachmawati, N. Analisis Fungsional Komunitas Bakteri dengan pyTax4Fun2 dan Optimasi Pemrosesan Dataset Besar Menggunakan Polars. Skripsi. Departemen Biologi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Brawijaya. Malang, Indonesia (2026). [in Indonesian].
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Willerslev, Eske, Davison, John, Moora, Mari et al. · DRYAD · 2014
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Lindner, Blake · Figshare · 2025
Genomic FASTA file and reference tables for SourceApp.<br><br>For additional info see the following: <br>https://github.com/bglindner/SourceApphttps://pubs.acs.org/doi/full/10.1021/acs.est.5c03603<br>
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Re3data.Org · Registry of Research Repositories · 2013
The Reference Sequence (RefSeq) collection provides a comprehensive, integrated, non-redundant, well-annotated set of sequences, including genomic DNA, transcripts, and proteins. RefSeq sequences form a foundation for medical, functional, and diversity studies. They provide a stable reference for genome annotation, gene identification and characterization, mutation and polymorphism analysis (especially RefSeqGene records), expression studies, and comparative analyses.
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Natalie Wilson · Nature Reviews Molecular Cell Biology · 2004
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