Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,375
datasets available to search
ShareScore release 0.9.0
Dataset results
2,375 results for “Antibiotics”
Lab disease outcomes data evaluating how antibiotic tolerant vs. non-tolerant cell-free supernatant from Pseudomonas aeruginosa affects the interaction between a fungal pathogen (Batrachochytrium dendrobatidis) and amphibian (Rana sylvaticus), 2022.
Microbes living on hosts and in the environment can play a key role in helping hosts to combat pathogens. However, antibiotic-induced alterations to microbial metabolite production could disrupt this dynamic. Here, we investigated whether antibiotic tolerance influences the anti-pathogenic properties of host-associated (living on the host; biofilms) and environmental (living in the soil of water column; planktonic) microbes in vitro and in vivo. For our model host and pathogen, we used the amphibian (Rana sylvatica)-Batrachochytrium dendrobatidis (Bd) system. For our model host-associated (biofilm) and environmental (planktonic) microbes, we used four strains of Pseudomonas aeruginosa that vary in their tolerance to antibiotics and their biofilm-forming capabilities: Planktonic, non-antibiotic tolerant (ΔsagS/VC); Planktonic, antibiotic tolerant (ΔsagS::sagS_L154A); Biofilm, non-antibiotic tolerant (ΔsagS::sagS_D105A); Biofilm, antibiotic tolerant (ΔsagS::sagS). We collected cell-free supernatants (CFS) from each strain to examine the effects of metabolites. We conducted four experiments. In our pathogen-only exposures to test direct effects of metabolites on Bd, we exposed Bd zoospores to each P. aeruginosa CFS at six concentrations. After 11 days of growth, we measured relative abundance of Bd across each treatment. In our host-only exposures to test effects of metabolites on host disease outcomes, we placed R. sylvatica tadpoles in individual units containing each P. aeruginosa CFS. After 48 hours, water was changed into clean well water (no CFS). Bd zoospores were immediately added to each experimental unit following the water change. After 5 days of Bd exposure, we measured snout-vent length (SVL), mass, developmental stage, and Bd quantification in the mouthparts using qPCR for each tadpole. In our host-pathogen exposures to test interactive effects of metabolites on hosts in the presence of the pathogen, we conducted the same experiment as above. However, ins
Global eutrophication and antibiotic resistance genes dataset for "Coupling mechanisms between cyanobacteria and antibiotic resistance genes in freshwater ecosystems"
This dataset compiles global records of cyanobacteria, antibiotic resistance genes (ARGs), and associated water quality parameters to support research on freshwater ecosystem dynamics. It includes 990 metagenomes, 16,648 chlorophyll-a (Chl-a) records, and over 90 documented cases of ARGs–cyanobacteria co-occurrence under comparable spatiotemporal conditions. The dataset covers the years 2000–2024 and provides both raw measurements and harmonized tables for cross-study comparisons. Data were extracted from previously published literature and public repositories, with references to source publications included. This archive is intended to facilitate reproducible analyses, enable large-scale meta-studies, and support further exploration of microbial interactions in freshwater systems.
Dataset / Code: Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy
<p><strong>Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy</strong></p> <p><strong> </strong></p> <p>Harim I. Won<sup>1,#</sup>, Samuel Zinga<sup>1,#</sup>, Olga Kandror<sup>1</sup>, Tatos Akopian<sup>1</sup>, Ian D. Wolf<sup>1</sup>, Jessica T.P. Schweber<sup>1</sup>, Ernst W. Schmid<sup>2</sup>, Michael C. Chao<sup>1</sup>, Maya Waldor<sup>1</sup>, Eric J. Rubin<sup>1,*</sup>, Junhao Zhu<sup>1,3,*</sup></p> <p><strong> </strong></p> <p><sup>1</sup>Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.</p> <p><sup>2</sup>Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Blavatnik Institute, Boston, Massachusetts 02115, USA.</p> <p><sup>3</sup>CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.</p> <p><sup>#</sup>These authors contributed equally to this work.</p> <p>*Corresponding authors: <a href="mailto:zhujh@im.ac.cn">zhujh@im.ac.cn</a> (J.Z.), <a href="mailto:erubin@hsph.harvard.edu">erubin@hsph.harvard.edu</a> (E. J. R.)</p> <p><strong> </strong></p> <p><strong>Abstract</strong></p> <p>Proteolysis-targeting chimeras (PROTACs) represent a new therapeutic modality involving selectively directing disease-causing proteins for degradation through proteolytic systems. Our ability to exploit targeted protein degradation (TPD) for antibiotic development remains nascent due to our limited understanding of which bacterial proteins are amenable to a TPD strategy. Here, we use a genetic system to model chemically-induced proximity and degradation to screen essential proteins in <em>Mycobacterium smegmatis </em>(<em>Msm</em>)<em>, </em>a model for the human pathogen <em>M. tuberculosis </em>(<em>Mtb</em>). By integrating experimental screening of 72 protein candidates and machine learning, we find that drug-induced proximity to the bacterial ClpC1P1P2 proteolytic complex leads to the degradation of many endogenous proteins, especially those with disordered termini. Additionally, TPD of essential <em>Msm </em>proteins inhibits bacterial growth and potentiates the effects of existing antimicrobial compounds. Together, our results provide biological principles to select and evaluate attractive targets for future <em>Mtb</em> PROTAC development, as both standalone antibiotics and potentiators of existing antibiotic efficacy.</p> <p> </p>
Using the Tea Bag Index to unravel how interactions between an antibiotic (Trimethoprim) and endocrine disruptor (17a-estradiol) affect aquatic microbial activity: Supporting Dataset 1
<p>The constant release of complex mixture of pharmaceuticals, including antimicrobials and endocrine disruptors, into the aquatic environment. These have the potential to affect aquatic microbial metabolism and alter biogeochemical cycling of carbon and nutrients. We used the Tea Bag Index (TBI) for decomposition within a series of contaminant exposure experiments to test how interactions between an antibiotic (trimethoprim) and endocrine disruptor (17a-estradiol) affects microbial activity in an aquatic system. The TBI is a citizen science tool used to test microbial activity by measuring the differential degradation of green and rooibos tea as proxies for labile and recalcitrant organic matter decomposition. Here we present the raw data on pharmaceutical exposures and the mass loss of the Rooibos and Green tea bags within the experiment. From Tea Bag mass loss we then calculated the Stabilisation Factor (S) and Initial Decomposition Rate of the labile organic matter fraction.</p>
S6 | ITNANTIBIOTIC | Antibiotic List: ITN MSCA ANSWER
<p>This is the collection associated with list S6 ITNANTIBIOTIC on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S6 | ITNANTIBIOTIC | <strong>Antibiotic List: ITN MSCA ANSWER</strong></p> <p>A list of antibiotics compiled by Nikiforos Alygizakis (EI/Uni Athens), see e.g. Paulus <em>et al</em> 2019 DOI: <a href="https://doi.org/10.1016/j.ijheh.2019.01.004">10.1016/j.ijheh.2019.01.004</a></p> <p>Suspect list of human phase-I antibiotic metabolites, created with <a href="https://biotransformer.ca/">BioTransformer</a> (v3.0.0) provided by Tim Jonkers (VU, NL). Jonkers <em>et al</em> (in prep.)</p> <p>v0.2.0: added the CYP Metabolites Step 1 files.</p>
Clinically approved small molecule antibiotics since 2010
<p>Properties of clinically approved small molecule antibiotics since 2010 are summarized.</p> <p>Properties include: Name Year approved Origin Mechanism of action Indication Administration Spectrum Protein binding Resistance Smiles Molweight cLogP cLogS H-Acceptors H-Donors Druglikeness DrugScore Total Molweight Monoisotopic Mass Total Surface Area Relative PSA Polar Surface Area LE from Molweight LLE from Molweight LELP from Molweight Shape Index Molecular Flexibility Molecular Complexity Structure of Smiles [idcode]</p> <p> </p> <p>Propeties in rows B to I were retrieved from the references cited in the dataset. <br> Properties in rows K - AC were calculated with DataWarrior. https://openmolecules.org/datawarrior/index.html</p>
Public dataset for Antibiotic Prophylaxis for Surgical Site Infections as a Risk Factor for Infection with Clostridium difficile
<p>This is the minimal publicly available dataset for the manuscript titled "Antibiotic Prophylaxis for Surgical Site Infections as a Risk Factor for Infection with Clostridium difficile". We have also included the data dictionary. </p>
Taking Antibiotics for a virus is like trying to sweeten lake Victoria with a bag of sugar
<p>Video in English (see also the Swahili version) warns that attempting to treat a virus or common cold by self-medication with antibiotics can lead to the development of Antimicrobial Resistance (AMR). It uses humour and Tanzanian metaphors for foolishness to make its point. This was originally developed as an audio piece, but still images have been added in post-production to create a video. </p><p>Audio and images produced as part of a Participatory Action Research (PAR) Workshop with young professionals in Mwanza, Tanzania to create public health messages on Antimicrobial Resistance in a post-COVID East Africa in June 2022. The project built upon data gathered in two international, interdisciplinary research projects (HATUA – 'Holistic Approaches to Understanding Antimicrobial Resistance in East Africa' and CARE – 'COVID-19 and Antimicrobial Resistance in East Africa – Impact and Response'), seeking to understand the wider medical and societal drivers of AMR in East Africa and identify possible interventions to curb the spread of AMR. The workshop ran for 9 days over a 3 week period and consisted of focus group style discussions with participants to explore issues surrounding AMR, antibiotic use, and public health messaging awareness in local communities (days 1-2), participant-led design of poster, radio, and video messages with feedback from the research team and introduction to filming/recording equipment (days 3-4), filming, shooting and recording materials within local settings in Mwanza with participants serving as actors, directors, and crew with guidance from research team (days 4-8) and a final in-person review and hands-on feedback of preliminary mock-ups of posters and videos (day 9). Participants have continued to collaborate via email and WhatsApp as materials were finalised. Final video production and editing was undertaken by the researchers. </p><p>Correspondence: mgk@st-andrews.ac.uk; kjf4@st-andrews.ac.uk</p><p> </p>
Antibiotic resistant pathogen outbreak investigation: an interdisciplinary module to teach fundamentals of evolutionary biology
<p>The evolution of resistance to antibiotics provides a timely and relevant topic for teaching undergraduate students evolutionary biology. Here, we present a module incorporating modified sequencing data from eight antibiotic resistant pathogen outbreaks in hospital settings with bioinformatics and phylogenetic analyses. This module uses whole genome sequencing data from hospital outbreaks investigated by the Centers for Disease Control and Prevention to provide examples of antibiotic resistance spread. Students work in groups to analyze outbreak data to identify the bacterial species and antibiotic resistance genes, to infer a phylogenetic tree examining relatedness among isolates, and to determine a possible source of the outbreak. Students then compile their results in individual reports and provide recommendations for preventing the further spread of antibiotic resistant organisms. In addition to providing genomic outbreak data, we include a teaching concepts guide discussing three integral components of the module: how evolutionary biology concepts of natural selection and competition impact antibiotic resistance; outbreak investigation information to aid in phylogenetic analysis and creation of recommendations; and instructions for the bioinformatics protocol. Completion of this module provides students an opportunity to think critically about the evolution of resistance, practice bioinformatics techniques, and relate evolutionary biology to current events.</p>
Row sequcenes data for assessing the risks of potential pathogens and antibiotic resistance genes among heterogeneous habitats in a temperate estuary wetland
<p>The study included 118 usable samples within three different habitats (water, soil, and sediment) across the Liaohe River basin to the Red Beach wetland collected from seven papers, and all of the sequence files were uploaded for availability.</p>
AVANT Workshop "Antibiotic-free pork production: realiy or chimera?"
<p>The aim of the workshop organized by the EU Innovation Action AVANT is to facilitate dialogue and consensus on specific themes pertinent to the future of antibiotic-free pork production. The involvement of significant partners such as SEGES, COOPERL and FVE, alongside other relevant national and international stakeholders, will offer a diverse array of perspectives and expertise crucial for addressing the multifaceted challenges and opportunities of this type of production in the years to come. One significant outcome could be the development of a position paper encompassing the collective insights, agreements, and recommendations arising from the discussions held during the workshop.</p>
Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"
<p>City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics.</p> <p>To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: <strong><em><span>Escherichia coli</span></em></strong>, the emerging pathogen <strong><em><span>Streptococcus suis</span></em></strong>, and the sewer-inhabiting <strong><em><span>Arcobacter cryaerophilus</span></em></strong>. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin).<span> Using</span> these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.</p> <p> </p> <p><strong>Usage</strong></p> <p>Users can perform the analysis by running the R script (TC3D.R) after the installation of all</p> <p>package mentioned in the preamble,<span> </span></p> <p>This folder contains:</p> <p>- 3 datasets with OD measures for the 3 species:</p> <p><span> </span>* data_acrya.xlsx</p> <p><span> </span>* data_ecoli.xlsx</p> <p><span> </span>* data_ssuis.xlsx</p> <p>- 1 excel files with metadata (plate, well, species, environmental conditions)</p> <p><span> </span>* map_plate_all.xlsx</p> <p>- 4 datasets giving time series of the flow and several measures including <span> </span>conductivity and <span> </span>temperaturefor 2 sewers of Barcelona obtained from sample cabines <span> </span>set during the implementation of SCOREWATER (ID:820751)</p> <p><span> </span>* carmel_flow.csv</p> <p><span> </span>* carmel_quality.csv</p> <p><span> </span>* poblenou_flow.csv</p> <p><span> </span>* poblenou_quality.csv</p> <p>- 1 C++ script compiled and run with the R TMB package:</p> <p><span> </span>* fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.</p> <p><br><br></p>
Higher temperatures exacerbate effects of antibiotics on methanogenesis in freshwater sediment
<p>This dataset features data and code of the associated publication in Communications Earth & Environment (<a href="https://doi.org/10.1038/s43247-024-01828-3" target="_blank" rel="noopener">https://doi.org/10.1038/s43247-024-01828-3</a>). To reproducibly run the code you should download the entire <code>.zip</code> archive and open the <code>*.RProj</code> file ideally with the same R version (<code>4.3.1</code>). To achieve maximum reproducibility of the code you should run <code>renv::restore()</code> to restore the packages recorded in <code>renv.lock</code>. Scripts within the folder <code>code</code> are named in the order in which they should be run. Note that running the Bayesian models requires a C++ toolchain. A web search of "RStan Getting Started" will yield up to date information about how to install this on your device. The models ran for the publication are provided as <code>.rds</code> file in case you don't want to run it yourself. Some figures required post-export editing in the paid program "Affinity photo". The edited and unedited versions are included in this repository to allow for comparisons. Also the <code>.afphoto</code> files are included for users that own a license.</p>
Epistasis decreases with increasing antibiotic pressure
<p><strong>Data and code for 'Epistasis decreases with increasing antibiotic pressure but not temperature'</strong></p> <p>All of the competition data and code used to run the analysis presented in the manuscript "Epistasis decreases with increasing antibiotic pressure but not temperature" is available here. The bioRxiv pre-print is found here: <a href="https://doi.org/10.1101/2022.09.01.506172">https://doi.org/10.1101/2022.09.01.506172</a>. The publication will appear in <em>Philosophical Transactions of the Royal Society B: Biological Sciences</em> on 03 April 2023 at the following permanent link: <a href="https://dx.doi.org/10.1098/rstb.2022.0058">https://dx.doi.org/10.1098/rstb.2022.0058</a>.</p> <p>All flow cytometry and estimated competitive fitness data is found in the 'data' folder. The data files include raw data files as well as intermediate data files that were created along the course of the analysis. Running the `.Rmd` files locally may over-write these intermediate data files (if you're trying to re-create these intermediate data files but it's not working, remember to check whether `eval=FALSE` in the header of the chunk).</p> <p>Analyses are found in the 'analysis' folder. For R notebook files, the code is available in the `.Rmd` files and is best run using RStudio (but the `.Rmd` files can also be opened for viewing with any text editor). Intermediate data files in the 'analysis' folder have the extension `.RData` or `.dat`; running R and python files locally may over-write these intermediate data files. The output of each R notebook file has also been saved to `.html` files. It is therefore easiest to start by viewing the `.html` file for each R notebook and then opening `.Rmd` files only if you wish to alter the code.</p> <p>The python code calculates gamma epistasis and parametric bootstrapping of the gamma epistasis values.</p>
Multipad Agarose Plate (MAP): A Rapid and High-Throughput Approach for Antibiotic Susceptibility Testing
<p>The datasets used for the Multipad Agarose Plate (MAP) paper. Each experiment was labelled with BE followed by a number. </p> <p>The file <em>BE_condition_map.json</em> describes what was placed on each pad for the experiments. Attached here are JSON files with Pandas data frames that contain all segmentation information, along with debug videos showing how the segmentation aligns with the images. Contact us for access to the raw data.</p> <p>Datasets used for validation experiments:</p> <ul> <li>Leakage test: BE100, BE102</li> <li>Agarose concentration: BE103</li> <li>Illumination wavelength verification: BE138</li> <li>Seeding density verification: BE162</li> </ul> <p>Datasets used for AST:</p> <ul> <li>Chloramphenicol and Rifampicin: BE140, BE141, BE142, BE144, BE145</li> <li>Vancomycin, Ampicillin, Kanamycin: BE148, BE149</li> <li>Ciprofloxacin, Tetracycline, Carbenicillin, Mecillinam: BE150, BE151</li> </ul> <p>Broth microdilution data used for AST validation:</p> <ul> <li>BE139, BE143, BE160</li> </ul> <p>Some datasets also include data that was discarded. </p>
Dataset for "Phenolic Substitution in Fidaxomicin: A Semisynthetic Approach to Antibiotic Activity Across Species"
<p>ZIP File:</p> <p>Characterisation data (such as e.g. NMR, IR, MS spectra)</p> <p>NMR raw data, .mnova files</p> <p>ChemDraw drawings (.cdx files)</p> <p>PDF file:</p> <p>Supporting information for</p> <p><strong>Phenolic Substitution in Fidaxomicin: A Semisynthetic Approach to Antibiotic Activity Across Species</strong></p> <p><br> Erik Jung,[a] Anastassia Kraimps,[a] Silvia Dittmann,[b] Tizian Griesser,[c] Jordan Costafrolaz,[d] Yves Mattenberger,[d] Simon Jurt,[a] Patrick H. Viollier,[d] Peter Sander,[c] Susanne Sievers,[b] and Karl Gademann*[a]</p> <p>[a] E. Jung, A. Kraimps, S. Jurt, Prof. Dr. K. Gademann Department of Chemistry, University of Zurich 8057 Zürich (Switzerland)<br> E-mail: karl.gademann@uzh.ch<br> [b] S. Dittmann, Dr. S. Sievers<br> Department of Microbial Physiology and Molecular Biology Institute of Microbiology<br> Center for Functional Genomics of Microbes<br> University of Greifswald<br> Greifswald (Germany)<br> [c] T. Griesser, Prof. Dr. P. Sander Institute of Medical Microbiology University of Zurich<br> Zurich (Switzerland)<br> [d] J. Costafrolaz, Dr. Y. Mattenberger, Prof. Dr. P. H. Viollier Department of Microbiology and Molecular Medicine Faculty of Medicine, University of Geneva<br> Geneva (Switzerland)<br> </p> <p>Abstract of the corresponding publication:</p> <p>Fidaxomicin (Fdx) is a natural product antibiotic with potent activity against Clostridioides difficile and other Gram-positive bacteria such as Mycobacterium tuberculosis. Only a few Fdx derivatives have been synthesized and examined for their biological activity in the 50 years since its discovery. Fdx has a well-studied mechanism of action, namely inhibition of the bacterial RNA polymerase. Yet, the targeted organisms harbor different target protein sequences, which poses a challenge for the rational development of new semisynthetic Fdx derivatives. We introduced substituents on the two phenolic hydroxy<br> groups of Fdx and evaluated the resulting trends in antibiotic activity against M. tuberculosis, C. difficile, and the Gram-neg- ative model organism Caulobacter crescentus. As suggested by the target protein structures, we identified the preferable derivatisation site for each organism. The derivative ortho- methyl Fdx also exhibited activity against the Gram-negative C. crescentus wild type, a first for fidaxomicin antibiotics. These insights will guide the synthesis of next-generation fidaxomicin antibiotics.</p>
Confocal microscopy images of Escherichia coli cells treated with various antibiotics (LB, exponential phase)
<p>This dataset and CARE model is part of the publication "<strong>Transertion and cell geometry organize the </strong><i><strong>Escherichia coli</strong></i><strong> nucleoid during rapid growth</strong>".</p><p>It contains all CLSM images that were used for the publication, as well as the single-cell regions of interest for analyses.</p><p>Cells were grown to exponential phase in LB Lennox and antibiotics were added for 0-60 min. Cultures were then chemically fixed, immobilized and stained for DNA (DAPI) and membrane (Nile Red). The strain (NO34) expresses a MreBsw-sfGFP fusion protein from the native chromosomal locus. It was a kind gift from Zemer Gitai (<a href="http://doi:10.1016/j.bpj.2016.07.017">Ouzounov et al., 2016</a>).</p><p>More information can be found in the publication.</p>
Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome
<p><strong>The dataset from the article </strong><strong>Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome</strong></p>
Supplementary material: Prophylactic antibiotics for adults with chronic obstructive pulmonary disease: a network meta-analysis
<p>Supplementary material for Cochrane review: Janjua S , Mathioudakis AG , Fortescue R , Walker RAE , Sharif S , Threapleton CJD , Dias S . Prophylactic antibiotics for adults with chronic obstructive pulmonary disease: a network meta-analysis. Cochrane Database of Systematic Reviews 2021, Issue 1. Art. No.: <a href="https://archie.cochrane.org/sections/documents/CD013198">CD013198</a>. DOI: <a href="https://archie.cochrane.org/sections/documents/10.1002/14651858.CD013198.pub2">10.1002/14651858.CD013198.pub2</a>.</p>
Macrophage phagocytosis assay of Acinetobacter baumannii AB074, passaged 15 days in antibiotics or transferrin.
<p>Macrophage phagocytosis assay of Acinetobacter baumannii AB074, passaged 15 days in antibiotics or transferrin. Experiment #1, replica #1-2, from March 7th, 2017 and Experiment #2, replica #1-2 from March 16th, 2017</p> <p>MICs for AB074: transferrin = 4 mcg/ml; ciprofloxacin = 1 mcg/ml; meropenem = 0,5 mcg/ml</p> <p>6 groups of treatment:</p> <p>1. No drug</p> <p>2. Transferrin only, last passage dose = 16 mcg/ml </p> <p>3. Ciprofloxacin only, last passage dose = 5 mcg/ml </p> <p>4. Ciprofloxacin + transferrin, last passage dose = 0,5 mcg/ml of cipro + 16 mcg/ml of transferrin</p> <p>5. Meropenem only, last passage dose = 2,5 mcg/ml </p> <p>6. Meropenem + transferrin, last passage dose = 2,5 mcg/ml + 16 mcg/ml of transferrin</p> <p>The file name structure: group_picture#_strain_timeOfPassage_ experiment#_replica#</p>
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.