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FIG. 6 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 6. — Attractive traps: A, pink LED based automatic light trap (PVP) suspended at 15 m height close to a small canopy gap; B, light trap (LT) with light bulb of 125W and with white sheet, covered with moths at the end of a rainy night; C, colored pan traps (blue [BPT], white [WPT], and yellow [YPT]) at soil surface level to collect Diptera; D, fruit baited Coleoptera traps with banana nectar (BT), suspended in forest canopy; E, Nymphalidae butterfly trap (CHX), suspended in the forest canopy; F, tree equiped with ropes and baits composed of honey and tuna at different heights to attract ants; G, pitfall trap baited with dung (PFC) to collect coprophagous Scarabaeidae; H, Big Shot, a type of slingshot used to shoot ropes and suspend traps high up in the trees. Photos: A, B, G, H, Julien Touroult; C, Marc Pollet; F, Maurice Leponce; D, E, Stéphane Brûlé.
FIG. 11 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 11. — Process flow for Diptera: A, each Diptera coordinator and taxonomic expert signs an agreement prior to receiving samples; B, sampling specimens with an array of methods (Malaise trap, pan traps, sweep net, SLAM trap); C, transporting of partly processed and unprocessed samples to the Belgian lab; D, sorting Diptera from complete samples and splitting the Diptera fraction into workable fractions (mostly on family level) for Diptera coordinators – taxonomic experts; E, processed Diptera fractions (Dolichopodidae, Empidoidea, Mycetophilidae, Phoridae); F, dissemination of workable fractions to Diptera coordinators – taxonomic specialists (10 in Europe, 5 in Canada, 8 in the USA, 10 in Brazil); G, examination and identification of specimens of workable fractions by the taxonomic expert (or further splitting of fractions by Diptera coordinator); H, commitments as part of the signed agreement (see Fig. 11A), with submission of identification file as first.
2DUV Machine Learning Protocol Code
<p>Simulation data and code of ML protocol for 2DUV spectra of proteins.</p> <p>Any researchers who interested in protein spectroscopy can use our ML protocol online service: <a href="http://www.dcaiku.com:13000">http://www.dcaiku.com:13000</a></p> <p>For the machine learning protocol source code written in Python and Bash language which including:</p> <ul> <li>2duv_simulation folder <ul> <li>2DUV folder <ul> <li>0_parse_traj.py: Parse the MD trajectory in pdb format.</li> <li>1_bench_genH.sh: PBS script for generating the excition Hamiltonian,and E/M dipoles.</li> <li>1_cal_Hamil.py: Python script for submitting a large number of PBS script at once.</li> <li>2_extract_struc.py: Extract mode information of secondary structure segments from pdb file.</li> <li>3_extract_Hamil.py: Extract Hamiltonian, E/M dipoles information of secondary structure segments.</li> <li>4_cal_spectra.py: Python script for calculate the 2DUV spectra.</li> <li>4_run_calspectra.py: Python script for submitting a large number of PBS script for calculating 2DUV spectra at once.</li> <li>4_sub_calspectra.pbs: PBS script for runing python script of calculating 2DUV spectra.</li> <li>inputs folder: Spectron main input files.</li> </ul> </li> <li>GramacsFile: Gromacs main input files.</li> </ul> </li> <li>web-api folder: Source code of our ML protocol online service.</li> </ul>
Supplementary underlying data for "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"
<p>This dataset includes additional underlying data for the publication "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"</p> <p>Contents:</p> <p>Tukey Honest Significant Difference (HSD) results for solutes 1-47 across all seven parameter sets, as *.txt. These are pairwise comparisons of results between all possible parameter sets. Significant differences are treated as p < 0.05.</p>
Protocols and data for Heterosigma akashiwo transformation approaches
<p>This dataset provides information about plasmid construction, antibiotic sensitivity and transformation approaches for the algal species, <em>Heterosigma akashiwo</em>.</p> <p><strong>Note that protocols for glass bead-mediated transformation and electroporation of <em>Heterosigma akashiwo</em> have been updated. Please see http://dx.doi.org/10.17504/protocols.io.hjkb4kw and http://dx.doi.org/10.17504/protocols.io.hjmb4k6.</strong></p>
Supplementary Material: Evaluation of Cyanea capillata Sting Management Protocols Using Ex Vivo and In Vitro Envenomation Models
<p>Supplementary files for Doyle, T.K.; Headlam, J.L.; Wilcox, C.L.; MacLoughlin, E.; Yanagihara, A.A. Evaluation of <em>Cyanea capillata</em>Sting Management Protocols Using Ex Vivo and In Vitro Envenomation Models. <em>Toxins</em> <strong>2017</strong>, <em>9</em>, 215. Video S1: Vinegar Application to Gelatin-Adherent Cnidae</p>
PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) of the research entitled "Development of Competences for the Fashion Designer: a Scope Review
<p>PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol and Check list CAPSI - Critical analysis of the articles related to the specific objective: map the current themes that permeate the competencies of fashion design professionals through a scoping review.</p>
SeaPaCS graphic elaboration of the Protocol for marine micro-plastic collection and monitoring in citizen science and for building a L.A.D.I. trawling tool
<p>This is a graphic elaboration (in Italian) of the protocol "SeaPaCS deliverable - protocol for plastic monitoring in citizen science" in English and Italian is a deliverable of the SeaPaCS project (Participatory Citizen Science Against Marine Pollution), funded by IMPETUS (project ID 101058677). The protocol and the visual elaboration has been freely adapted from "<i>LADI and the Trawl</i>" by Coco Coyle with Melissa Novaceski, Emily Wells and Max Liboiron, as published by the Civic Laboratory for Environmental Action Research, August 2016. The graphic elaboration (as the protocol) in both languages, consists of three parts: 1) how to build a DIY low cost manta trawl device (LADI - Low-Tech Aquatic Detection Debris Instrument) to monitor plastic pollution, adjusted to materials availability and costs in Italy; 2) how to monitor (the sampling itself and towing procedure); and 3) how to categorize plastic debris back on land. </p>
Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies
<p>Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies.</p>
Dataset for publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction protocol implemented in XMapTools"
<p>Datasets for the publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction in XMapTools"</p>
F I G U R E 1 in Staining protocols affect use of otolith to estimate the demography of the damselfish sergeant major (Abudefduf vaigiensis)
F I G U R E 1 Mark quality in different concentrations of stain in the sagittae, lapilli, and asterisci. Error bars represent 95% confidence intervals and letters represent significant differences. Capitalized letters compare across treatments and lower-case letters compare within a treatment.
Data analysis Protocol for a Joint Study into the Impacts of AI on professional Competencies of IT Professionals and Implications for Computing Students. ITiCSE 2024 Working Group 02.
<h1><a name="_Toc169648661"></a><span>Overview</span></h1> <p><strong><span> </span></strong></p> <p><span>The purpose of this protocol is to help us define a common protocol for sharing and analysing data for the ITiCSE 2024 working group: “<em>WG02: A Multi-Institutional-Multi-National Study into the Impacts of AI on Work Practices of IT Professionals and Implications for Computing Students</em>”. <span> </span>Excerpts from the working group plan to place the protocol in context (Clear et al., 2024) are given below.</span></p> <p><strong><em><span> </span></em></strong></p> <p><strong><em><span>Background and Related Work</span></em></strong></p> <p><em><span>As Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world [1,10], and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement [11,13,1] and the implications for tertiary study, assessment and curricula in computing [4, 8, 10, 9].<span> </span></span></em></p> <p><em><span>“Work engagement”, has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability [11, 13]. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI [10, 1]. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement.<span> </span>The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces. </span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>2</span></em></strong><em><span><span> </span><strong>Goals of the Working Group</strong></span></em></p> <p><em><span>By exploring the relationship between work engagement and learning, this study aims to shed light on the dynamics that drive employee engagement and its connection to the professional development of competencies. The previous study has interviewed IT professionals with the following research questions (RQ):</span></em></p> <p><em><span> </span></em></p> <p><em><span>RQ1: How does AI influence work engagement for IT professionals?</span></em></p> <p><em><span>RQ2: How does AI affect the socio-technical work dynamics for IT professionals?</span></em></p> <p><em><span>RQ3: What are the implications of integrating AI on the acquisition and enhancement of professional competencies and the learning processes of IT professionals?</span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>3</span></em></strong><em><span><span> </span><strong>Methodology</strong></span></em></p> <p><em><span>This working group aims to analyse the corpus of interview data collected from multiple countries to better understand the implications for computing students, tertiary computing education curricula and assessment of the new professional competencies emerging from this work. This study informed by the literature on work engagement, automation and motivation for IT professionals [11, 13], will use a combination of multi-vocal literature review [7] and qualitative research methods [2, 5], including thematic analysis of the interviews, to investigate the state of the practice in and challenges IT Professionals face within their local/global work contexts. The literature on professional competencies in computing [4, 3, 6] will be drawn upon to characterise the new needs identified in this analysis.<span> </span>Further implications for computing curricula design and assessment will be developed from this analysis. </span></em></p> <p><span>REFERENCES</span></p> <p><span>[1]<span> </span>ACM Technology Policy Council. 2023. Principles for the development, deployment, and use of generative AI technologies, ACM New York.</span></p> <p><span>[2]<span> </span>Braun, V. and Clarke, V. 2021. One size fits all? What counts as quality practice in (reflexive) thematic analysis? <em>Qualitative research in psychology</em>, <em>18</em> (3). 328-352.</span></p> <p><span>[3]<span> </span>Clear, A., Clear, T., Vichare, A., Charles, T., Frezza, S., Gutica, M., Lunt, B., Maiorana, F., Pears, A. and Pitt, F. 2020. Designing Computer Science<span> </span>Competency Statements: A Process and Curriculum Model for the 21st Century in <em>Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education</em>, ACM, New York.</span></p> <p><span>[4]<span> </span>Clear, A., Parrish, A. and CC2020 Task Force. 2020. Computing Curricula 2020 - CC2020 - Paradigms for Future Computing Curricula ACM and IEEE-CS eds. <em>A Computing Curricula Series Report </em>ACM, New York.</span></p> <p><span>[5]<span> </span>Cruzes, D.S. and Dyba, T. 2011. Recommended steps for thematic synthesis in software engineering. in <em>2011 international symposium on empirical software engineering and measurement</em>, IEEE, 2011, 275-284.</span></p> <p><span>[6]<span> </span>Frezza, S., Clear, T. and Clear, A. 2020. Unpacking Dispositions in the CC2020 Computing Curriculum Overview Report in <em>2020 IEEE Frontiers in Education Conference (FIE)</em>, IEEE, Uppsala, Sweden. </span></p> <p><span>[7]<span> </span>Garousi, V., Felderer, M., & Mäntylä, M. V. 2019. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. <em>Information and Software Technology</em>, <em>106.</em> 101-121</span></p> <p><span>[8]<span> </span>Jacques, L. 2023. Teaching CS-101 at the Dawn of ChatGPT. <em>ACM Inroads</em>, <em>14</em> (2). 40-46.</span></p> <p><span>[9]<span> </span>Liffiton, M., Sheese, B., Savelka, J. and Denny, P. 2023. CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes. <em>arXiv preprint arXiv:2308.06921</em>.</span></p> <p><span>[10]<span> </span>Prather, J., Denny, P., Leinonen, J., Becker, B.A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T. and Luxton-Reilly, A. 2023. The robots are here: Navigating the generative ai revolution in computing education. <em>arXiv preprint arXiv:2310.00658</em>.</span></p> <p><span>[11]<span> </span>Roto, V., Palanque, P. and Karvonen, H., 2019. Engaging automation at work–a literature review. in <em>Human Work Interaction Design. Designing Engaging Automation: 5th IFIP WG 13.6 Working Conference, HWID 2018, Espoo, Finland, August 20-21, 2018, Revised Selected Papers 5</em>, Springer, 158-172.</span></p> <p><span>[12]<span> </span>SFIA Foundation. 2023. SFIA skills aligned to EU ICT Profiles, SFIA Institute, London.</span></p> <p><span>[13]<span> </span>Sharp, H., Baddoo, N., Beecham, S., Hall, T. and Robinson, H. 2009. Models of motivation in software engineering. <em>Information and software technology</em>, <em>51</em> (1). 219-233.</span></p> <p><em><span> </span></em></p>
Research Data Management Framework - POC-Study: Guideline and protocol
<p>Dataset for the following paper:<br><br>Proof-Of-Concept-Studie für das FDM in den Ingenieur:innenwissenschaften</p> <p><strong>Ein Forschungsdatenmanagement-Rahmenwerk</strong><br>T. Hamann, C. Florides, A. Abdelrazeq, R. H. Schmitt</p> <p>Forschungsdatenmanagement (FDM) gewinnt seit Jahren an Bedeutung. Das Ziel, Daten wiederverwendbar aufzubereiten und nachzunutzen anstatt sie aufwändig neu zu erheben, wird von Forschenden der deutschen Ingenieur:innenwissenschaften jedoch nur selten verfolgt. Um dem entgegenzuwirken, wurde ein Rahmenwerk für das FDM in den Ingenieur:innenwissenschaften entwickelt. In einer Proof-Of-Concept-Studie soll dieses nun erstmals anhand des Forschungsprojekts KIOptiPack validiert werden.</p> <p><strong>A Research Data Management Framework</strong></p> <p>Research data management (RDM) has been gaining in importance for years. However, the goal of preparing data sustainably and reusing existing data instead of laboriously collecting it from scratch is rarely pursued by researchers in the German engineering sciences. To counteract this, a framework for RDM in the engineering sciences was developed. In a proof-of-concept study, this framework will be validated for the first time using the research project KIOptiPack.<br>Stichwörter: Forschung, Informationsmanagement, Digitalisierung<br><br><br>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.</p>
Testing Protocols for Obtaining Reliable PDFs from Laboratory x-ray Sources Using PDFgetX3
<p>In this work, we explored data acquisition protocols and improved data reduction protocols using PDFgetX3 to obtain reliable data for atomic pair distribution function (PDF) analysis from a laboratory-based Mo x-ray source. A variable counting scheme is described that preferentially counts in the high-angle region of the diffraction pattern. The effects on the resulting PDF are studied by varying the overall count time, the use of Soller slits, and limiting the out-of-plane divergence of the incident beam. The protocols are tested using an amorphous silica and a quartz sample. We also present a modification to the current PDFgetX3 data corrections to take care of sample absorption, which was previously neglected in the use of that program for high-energy synchrotron x-ray data. We show that, despite limitations in the Q-range and flux of laboratory instruments, reasonable data for PDF model fits may be obtained using the best protocols in a few hours of counting. </p>
Standard Rehab Protocol
Open the record for dataset details and reuse information.
Dose coefficients for organ dosimetry in tomosynthesis imaging of adults and pediatrics across diverse protocols
<p>A database of organ dose coefficients using MC simulations for a clinically representative virtual population of adult and pediatric XCAT patient models over an expanded set of 21 exam protocols in digital tomosynthesis. </p>
Protocol for transfection by microinjection into the eggs of the parasite vector snail Biomphalaria glabrata
<p><strong>1. Egg production</strong></p> <p>Place about 30 adult snails (10 mm diameter) into a 5.5-liter water tank. Place a piece of polystyrene of (3 x 3 cm) in each tank. There is the preferred support of <em>Biomphalaria glabrata</em> for laying its eggs. The snails are fed <em>ad libitum</em> with green lettuce leaves, they can also be fed with dry spirulina to boost reproduction. Maintain water at a temperature of 25 degrees Celsius.</p> <p><strong>2. Egg collection</strong></p> <p>Gently pick up several egg layers from the polystyrene with soft holding forceps and place the eggs into a petri dish with natural mineral water (e.g. Volvic) to prevent them from drying out.</p> <p>Start sorting the eggs under the stereoscopic microscope to choose only the gastrula stage and place them into another petri dish with natural mineral water.</p> <p> </p> <p><strong>3. Preparation of the transfection solution</strong></p> <p><strong>Material:</strong></p> <p>a. <em>in vivo</em> JetPEI transfection reagent</p> <p>b. 10% glucose solution</p> <p>c. 5% glucose solution</p> <p>d. Plasmids (dCas9-SunTag-BFP and scFv-DNMT3A-GFP)</p> <p>e. 0.2 ml microtubes</p> <p>f. P10 and P200 pipettes</p> <p>g. P10 and P200 pipette tips</p> <p>h. Permanent marker</p> <p>The glucose solution and the <em>in vivo</em> jetPEI transfection reagent are equilibrated at room temperature. </p> <p>Prepare 21 µl of each plasmid at a concentration of 78 and 88 ng / µl respectively (for a total volume of 42 µl =equals 3.5 µg of DNA) add the plasmid DNA to a 0.2 ml tube (labeled as Tube A) and mix with 21 µl of 10% glucose solution. </p> <p>In another microtube (labeled as Tube B), add 21 μl of 5% glucose solution and 1 μl of <em>in vivo</em> jetPEI. </p> <p> Prepare a third tube (labeled as Tube C) with 21 µl of 5% glucose solution and 0.5 µl of <em>in vivo</em> jetPEI to inject into embryos that will serve as controls. </p> <p>Leave the solutions at room temperature while you prepare the microinjection station. </p> <p> </p> <p> </p> <p><strong>4. Preparation of the micro-injection station</strong></p> <p><strong>Material:</strong></p> <p>a. Pre-pulled glass micropipettes (1mm diameter)</p> <p>b. Watch glass</p> <p>c. Modeling clay</p> <p>d. 35 mm and 90 mm petri dishes</p> <p>e. Mineral oil (M5904, SIGMA)</p> <p>f. Wash bottle with natural mineral water (Volvic)</p> <p>g. 0.2 ml microtubes</p> <p>h. 12-well cell culture plate</p> <p>i. Fine brush</p> <p>j. Phenol red solution</p> <p>k. Pasteur pipette or dropper</p> <p>l. Dissection forceps</p> <p>m. Soft holding forceps</p> <p>n. Snail eggs in the gastrula stage</p> <p>o. Drummond Scientific Nanoject III Programmable Nanoliter Injector</p> <p> </p> <p>Take a pre-pulled glass micropipette and cut it with a scalpel to have a ~ 0.2 mm tip slightly beveled if possible.</p> <p>Before attaching the micropipette to the programmable nanoliter injector, fill it with mineral oil. If this step is not done, the injector will not work properly. This can be done with a filling needle </p> <p>attached to a hamilton syringe of 10 microliters.</p> <p>When the micropipette is filled with oil, it must be fixed on the injector. For this it is necessary to: </p> <p>Slide the chuck and collet onto the glass micropipette, then slide the black O-ring with the seal onto the wire plunger </p> <p>With the micropipette attached to the injector, press the [EMPTY] icon until the plunger is fully extended. This step can be done with the footswitch by pressing once [EMPTY] then [STOP] and then proceeding [EMPTY] with the foot switch. A single beep is emitted when the plunger is fully extended.</p> <p>Fill the micropipette with 3 µl of the control solution or the transfection solution by placing the glass micropipette tip in a 0.2 ml tube with the solution to be injected and pressing the [FILL] icon. It is desirable to fill it at a slow rate, by pressing the [FILL] icon for a few seconds, then the [STOP] icon to allow the sample to equilibrate before pressing again the '[FILL] icon.</p> <p>Note: The piston continues to extend or retract until the [STOP] icon is pressed, or until the fully extended or fully retracted position is reached.</p> <p> </p> <p>5. Microinjection</p> <p>Place a watch glass into a 35mm petri dish and secure it on one side with modeling clay to form a slope. Use soft handling forceps to transfer an egg mass and lay it on the slope side of the watch glass so that the egg mass is in a sloping position.</p> <p>Remove excess water from the eggs with absorbent paper. Rehydrate if necessary with a fine brush to improve the visibility of the embryos. To inject the sample, return to the operating mode screen by pressing the [EXIT] icon, then select the injection mode by pressing the [INJECT] icon. Set the injection volume to 30nL and the flow rate to 20nL per second using the icons [+] and [-] respectively. Press the [INJECT] icon to inject the sample. </p> <p>Inject 30nL of the microinjection solution into each egg. Place the microinjected egg masses in a 12-well cell culture plate and note with a marker whether they were microinjected with the control solution or with the solution containing the plasmids.</p> <p>We colored the injection solution with red phenol to facilitate the visibility in this video.</p> <p><strong>Monitor the expression of the plasmids</strong></p> <p>Monitor the plasmids expression 72 h after microinjection in a contrast / fluorescent microscope or in a fluorescent stereo microscope. Then sort the fluorescent snails and perform a second micro-injection with a solution containing 10 µl of single guide RNA (at a concentration of 2ng / µl), add 0.5 µl of <em>in vivo</em> jetPEI reagent and 10 µl of 5% glucose solution. 3 days after the second microinjection, collect the hatched snails in a 1.5 ml tube containing 25 µl of lysis buffer for DNA and RNA purification.</p> <p>In this photo produced under a confocal microscope we washed a veliger larva in PBS solution, then we fixed it with 4% paraformaldehyde solution and then we placed it in a slide with two drops of the Dako fluorescence mounting medium. </p> <p>96 after the transfection we can observe the expression of the green fluorescent protein, the blue fluorescent protein and the co-localization of both proteins. </p> <p>This protocol is used to perform DNA methylation changes in a target gene. This transfection protocol can be used with other plasmids, with small interfering RNAs, or with messenger RNAs.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr)</p>
Comparative analysis of surface sanitization protocols on the bacterial community structures in the hospital environment
<p>In this study, we used 16S rRNA gene sequencing approaches to characterize the bacterial microbiota on different surfaces of the hospital environment. The longitudinal data was then subjected to comprehensive comparisons between different sanitation strategies (disinfectants, detergents and probiotics) to measure their potential effect on the microbial community structures in the hospital environment.</p> <p>This archive contains results and data of the 16S rRNA amplicon sequencing performed on 1019 environmental and 271 patient DNA samples collected over the time course of 40 weeks in a newly opened ward in the neurological station at the Charité Hospital (Berlin). The files include a study information and sample metadata sheets, BIOM-tables and information about the taxonomy results and diversity metrics.</p>
Data and code for publication: A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging
<p>Data & Code release for publication:</p> <p>Rebecca Buchholz, Sebastian Krossa, Maria K Andersen, Michael Holtkamp, Michael Sperling, Uwe Karst, May-Britt Tessem, A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging, <em>Metallomics</em>, Volume 14, Issue 3, March 2022, mfac013, <a href="https://doi.org/10.1093/mtomcs/mfac013">https://doi.org/10.1093/mtomcs/mfac013</a></p> <p>Python code for LA ICP MS imaging data segmentation</p> <p>Code & Data also on <a href="https://github.com/sekro/la-icp-msi_segmentation">github</a></p> <p>Thresholding based segmentation of LA-ICP-MS imaging data</p> <p>Description</p> <p><a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/main.py">src/main.py</a> - run this to process LA ICP MS data in data folder - generates matplotlib.figures - project specific setup <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/laicpms_data_handler.py">src/laicpms_data_handler.py</a> - contains object to import, handle and segment (shimadzu) raw data</p> <p>Dependencies</p> <p>Python 3.8.1 or newer</p> <p>For packages see <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/requirements.txt">requirements.txt</a></p> <p>Data</p> <p>LA-ICP-MS imaging data of human prostate tissue of the elements Zn, Fe & P. Details on data generation & collection in <a href="https://doi.org/10.1093/mtomcs/mfac013">publication</a>. LA-ICP-MS imaging data as plain text files (comma-separated values)</p> <ul> <li>Condition 1 = fresh frozen (FF)</li> <li>Condition 2 = room temperature vacuum dried and sealed (RTV)</li> <li>Condition 3 = formalin fixed (FFix)</li> <li>Condition 4 = formalin fixed, paraffin sealed (FFPS)</li> </ul> <p>3 replicate sectioning sets named A, B, C</p> <p>File-naming: LA_Data_CISN1.csv, where I = [1, 2, 3, 4] is indicating the condition used and N = [A, B, C] is indicating the replicate set</p> <p>License</p> <p>Data</p> <p>CC-BY 4.0 - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/data/LICENSE">LICENSE</a> file in data folder</p> <p>Source code</p> <p>MIT - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/LICENSE">LICENSE</a> file in src folder</p>
Example data for #GliaMorph Protocol
<p>Example data for the #GliaMorph Protocol by Kugler et al.</p> <p><strong>Link to GitHub</strong>: https://github.com/ElisabethKugler/GliaMorph</p> <p><strong>Contact</strong>: kugler.elisabeth[at]gmail.com</p> <p>Acquired by Dr Ryan MacDonald at the Institute of Ophthalmology, University College London (http://zebrafishucl.org/macdonald-lab).<br> Processed by Dr Elisabeth Kugler at the Institute of Ophthalmology, University College London (https://www.elisabethkugler.com/).</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.