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Fig. 5 in P e r s p e c t i v e Integrating current methods for the preservation of amphibian genetic resources and viable tissues to achieve best practices for species conservation

Fig. 5. Decision tree used for specimens euthanized to obtain tissue. Blue indicates steps in the decision tree. Green indicates procedures that will lead to preservation of tissues for genetic study. Purple indicates procedures that lead to achieving multiple goals, including cell culture and obtaining gametes for current or future ARTs. NOTE: Breeding and IVF can result in offspring that can be used for genetic purposes, thereby achieving multiple goals.

Fig. 6 in P e r s p e c t i v e Integrating current methods for the preservation of amphibian genetic resources and viable tissues to achieve best practices for species conservation

Fig. 6. Decision tree used to obtain tissue from live animals. Blue indicates steps in the decision tree. Green indicates procedures that will lead to preservation of tissues for genetic study. Purple indicates procedures that lead to achieving multiple goals, including obtaining gametes for current or future ARTs. NOTE: Breeding and IVF can result in offspring that can be used for genetic purposes, thereby achieving multiple goals.

Fig. 4 in P e r s p e c t i v e Integrating current methods for the preservation of amphibian genetic resources and viable tissues to achieve best practices for species conservation

Fig. 4. Length of time from cell culture initiation to freezing for amphibian cell lines in San Diego Zoo's Frozen Zoo®. Low = 19 days; high = 596 days; average = 154 days.

Fig. 3 in P e r s p e c t i v e Integrating current methods for the preservation of amphibian genetic resources and viable tissues to achieve best practices for species conservation

Fig. 3. The "tissue piecing" protocol used to preserve viable cells for establishment of cell lines in the future. A) Tissue is cut into long, thin strips. B) Tissue is diced into 1 mm3 fragments before adding medium containing 10% DMSO as a cryoprotectant. C) Prepared tissue is stored in LN2 until future cell culture is possible; those without cell culture capability can transport samples using a dry shipper to maintain cold-chain.

Fig. 2 in P e r s p e c t i v e Integrating current methods for the preservation of amphibian genetic resources and viable tissues to achieve best practices for species conservation

Fig. 2. Procedures used to obtain amphibian eggs or sperm for use in ARTs. A) Gravid female Leopard Frog (Lithobates sp.) after gonadotropic hormone injection. B) Expressing eggs into container by pressing on abdomen and pushing thumb toward cloaca; eggs can be fertilized (i.e., IVF) by fresh or cryopreserved sperm. Sperm can similarly be released from males by pushing towards the cloaca and releasing sperm naturally (in season) or after injection of gonadotropic hormones (e.g., HIS).

Fig. 1 in P e r s p e c t i v e Integrating current methods for the preservation of amphibian genetic resources and viable tissues to achieve best practices for species conservation

Fig. 1. Role of genetic resource collections in the research and conservation of amphibians. Green indicates the storage of tissues in biobanks. Purple indicates procedures associated with ARTs that lead to achieving multiple goals in amphibian research and conservation. Asterisk (*) denotes tissue or methodologies that are not currently used in ARTs but may be possible in the future. NOTE: For a more complete list of ARTs reference Clulow et al. (2014).

opencc-by-4.0Dec 2014View details →

Figure 2 in A proposed best practice approach to overcome unverified and unverifiable "first records" in ichthyology

Figure 2. - Locations of the occurrences of Lobotes surinamensis in the Adriatic Sea. 1: Dulčić & Dragičević, 2011; 2: Valter Kožul, pers. com.; 3: this paper).

opencc-by-4.0Mar 2021View details →

Dataset: Seeking feedback and taking action: An evidence based evaluation of the impact of using practice tests with inbuilt feedback in a first-year business class

This data set includes both qualitative and quantitative data. Qualitative, survey data sought student opinions on the purpose of feedback and the actions taken following receipt of feedback. 1. Survey data collection began with a 13 question survey used in classes in early February 2020. This initial feedback survey was conducted via MS Forms. The data from this is available in the following files: a. 2020-2-17_feedbacksurvey_1_rawdata.csv b. 2020-2-17_feedbacksurvey_1_how-many-times-got-feedback.csv c. 2020-2-17_feedbacksurvey_1_take-action-after-feedback.csv d. 2020-2-17_feedbacksurvey_1_type-feedback-you-prefer.csv e. 2020-2-17_feedbacksurvey_1_purpose-of-feedback.csv f. 2020-2-17_feedbacksurvey_1_define-feedback.csv g. 2020-2-17_feedbacksurvey_1_what-makes-feedback-useful.csv h. 2020-2-17_feedbacksurvey_1_know-how-feedback-effective.csv i. 2020-2-17_feedbacksurvey_1_what-can-institute-do-to-improve-feedback.csv 2. The first practice test was followed by a short survey of approximately 3 questions (created using MS Forms, a mix of closed and open questions). The data from this is available in the following files: a. 2020-3-2_feedbacksurvey_2_rawdata.csv b. 2020-3-2_action-comments-coded.csv c. 2020-3-2_other-comments-coded.csv d. 2020-3-2_open-question-comments.csv 3. The second practice test followed by a 3 question short survey (created using MS Forms, all open questions) The data from this is available in the following files: a. 2020-5-12_feedbacksurvey_3_rawdata.csv b. 2020-5-12_feedbacksurvey_3_what-learn-by-participating.csv c. 2020-5-12_feedbacksurvey_3_what-action-taking.csv d. 2020-5-12_feedbacksurvey_3_has-module-changed-view-of-feedback.csv Quantitative data was gathered from Learning Management System, Canvas. This data shows the relationship between number of practice tests taken by students and their resultant grades. 4. I gathered the number of times students had taken the practice test before the midterm summative exam and compared with the student grades from the 30% midterm summative exam. The data from this is available in the following files: a. 2020-11-26_Canvasdata_midterm.csv 5. Following the completion of the second summative exam (worth 70%), Canvas data showing the number of times the students had taken the second practice test and the grade outcomes for the students for the summative 70% test was gathered. The data from this is available in the following files: a. 2020-11-26_Canvasdata_endterm.csv 6. A regression model was created to examine the impact of number of times that the practice test was taken on final grades, whilst controlling for midterm grades. The data from this is available in the following files: a. 2024-2-12_Anonymised_mid_and_final_grades_with_number_of_practices_before_final_exam.csv

Data Sharing Practices in the MRC Circadian Mental Health Network.

This dataset supports the research conducted within the MRC Circadian Mental Health Network which assesses data sharing practices among Principal Investigators' publications in 2023. This work aims to identify trends, challenges, and inform future recommendations and policies based on the findings. The dataset includes various files that detail the methodology, data collected, and analyses performed.   Repository Contents: <ol> <li> Methods and Analysis Report - Data Sharing Practices in the MRC CMHN.pdf <ul> <li>This report provides the methodologies used for selecting and assessing research papers within the network, along with detailed results, tables, and discussions from the evaluation.</li> </ul> </li> <li> CMHN_All_Data.xlsx <ul> <li>An Excel workbook containing: <ul> <li>Sheet 1: All data and variables collected and analysed for this project.</li> <li>Sheet 2: A README file that explains each variable and its values. </li> </ul> </li> </ul> </li> <li> CMHN DataType Scoring.xlsx <ul> <li>An Excel workbook detailing: <ul> <li>Sheet 1: All datatypes, both code and datasets, evaluated in this study.</li> <li>Sheet 2: A README explaining the variables evaluated and their specific values. </li> </ul> </li> </ul> </li> <li> CMHN Data Extraction Survey.pdf <ul> <li>A copy of the Microsoft Form used to systematically evaluate data-sharing practices from selected publications, describing the structured data extraction process used. </li> </ul> </li> <li> CMHN DataType Scoring Survey.pdf <ul> <li>A Microsoft Form used to assess the types of data (code and datasets) shared. </li> </ul> </li> <li> Data_CSV_Code.csv <ul> <li>This file is the original, uncleaned dataset directly extracted from the initial response data of the Microsoft Form used in the project. It served as the primary dataset for all subsequent data analysis and code execution within the study. </li> </ul> </li> <li> CMHN Code.Rmd <ul> <li>An R Markdown file containing the code used for data analysis; predominantly descriptive statistics due to the limited number of papers with shared data.</li> </ul> </li> </ol> Recommended Use: For comparative purposes or further analysis, researchers are encouraged to utilise the cleaned datasets available in "CMHN_All_Data.xlsx" and "CMHN DataType Scoring.xlsx." Contact: For further inquiries, please email us at <a href="mailto:bio_rdm@ed.ac.uk" target="_blank" rel="noopener">bio_rdm@ed.ac.uk</a>.

Práctica Metagenómica: Tutorial de microbiomas usando QIIME2 / Practical Metagenomics: Microbiome tutorial with QIIME 2

Data for the e-learning tutorial Práctica Metagenómica: Tutorial de microbiomas usando QIIME2  Datos para el tutorial e-learning <a href="https://f1000research.com/documents/10-798">Practical Metagenomics: Microbiome tutorial with QIIME 2</a>

Práctica de transcriptómica: expresión diferencial de genes aplicado a la producción de alimentos / Practical Transcriptomics: Differential gene expression applied to food production

Data for the eLearning tutorial Practical Transcriptomics: Differential gene expression applied to food production Datos para el tutorial eLearning Práctica de transcriptómica: expresión diferencial de genes aplicado a la producción de alimentos

Pesticide application data and agronomic practices accross Europe and Argentina

A farm survey in 135 farms accross 10 european countries and Argentina was conducted in 2021. Data collected and reported in this database include (i) General information such as the type of farming practice, size of farm and of fields, types of crops and livestock, as well as working capacity (employees). (ii) For a selected one or two fields, a detailed description of the agronomic activities and yield parameters was compiled, which included crop and variety and e.g. position in crop rotation, as well as all crop-specific activities such as tillage, fertilisation, irrigation, pruning, and sowing.  (iii) Activities related to crop protection were requested in detail, including but not limited to the use of PPP formulations and related tools and techniques, and the strategy and motivation of the farm manager for PPP applications. The assessment of the crop protection strategy on the respective fields included variety, dates of PPP applications, product tank mixtures, type of products, product brand name, product distributor, target organisms, mode of action, area treated, quantity of product applied, total volume applied (total tank volume: product and water volume), formulation, active substances and concentrations, technique of application, speed of application, type of nozzles, and costs of products applied. (iv) Soil fertility management assessment included pre-crop, dates of application, types of product and nutrient specifications (NPK, micronutrients) as well as quantities applied. The information on agronomic practices was compiled in a ‘log book’, which listed all agronomic management practices on the respective fields during the whole season from planting/sowing to harvest as well as the underlying assumptions and motivations. (vi) Finally, econometric data included costs of products and yield Raw data was thoroughly screened for completeness, plausibility and data integrity, and revised/supplemented where necessary. Missing/unknown data was tagged and complemented where possible with proxies from e.g. product technical leaflets to allow for subsequent analysis. The data set was further complemented with related publicly available data, such as phenological codes (BSV, 2021.; Schweizerische Eidgenossenschaft, 2021), standard codes for crops and pests (EPPO secretariat, 2022.), mode of action of active substances and resistance risks (FRAC Committee, 2022.; HRAC Committee, 2022.; IRAC Committee, 2022.; Sparks & Nauen, 2015), and application recommendations of PPPs and fertilizers (collection of regional crop-specific or manufacturer-specific technical leaflets). If expenditures for PPPs were not available, costs for PPPs were assessed based on publicly available catalogues issued by manufacturers or regional PPP distributors. Data was also amended by hazard statements of active substances (Burtscher-Schaden et al.,)

Relax and repeat: The role of relaxation and practice on effects of mindfulness and mind wandering on time perception.

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2021View details →

Datasets for Earth Observation Using Python: A Practical Programming Guide

These are the datasets used in the <a href="https://agupubs.onlinelibrary.wiley.com/doi/book/10.1002/9781119606925">2021 Edition</a> of "Earth Observation Using Python: A Practical Programming Guide." These contain example text, csv, netCDF, and GRIB files that are used in code exercises within the book. Datasets are original files and modified public domain files from large Earth satellite data providers, such as NASA, NOAA, EUMETSAT, and ESA. These files may be used freely, but if not heavilly modified, please attribute credit to the original author for collecting the examples.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: The genera of the Neotropical armored catfish subfamily Loricariinae (Siluriformes: Loricariidae): a practical key and synopsis..

Natural history specimen data linked to collectors and determiners held within, "The genera of the Neotropical armored catfish subfamily Loricariinae (Siluriformes: Loricariidae): a practical key and synopsis.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4cb112e9-a4d9-4bff-8a2b-3104bf2db199">https://bionomia.net/dataset/4cb112e9-a4d9-4bff-8a2b-3104bf2db199</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4cb112e9-a4d9-4bff-8a2b-3104bf2db199">https://gbif.org/dataset/4cb112e9-a4d9-4bff-8a2b-3104bf2db199</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Nov 2024View details →

Research integrity practices and environmental impact of research: Questionnaire of the survey conducted at two French institutions

This dataset is a questionnaire designed to evaluate researchers' understanding, application, and perception of responsible research principles, and to identify strengths and weaknesses in their implementation. It contains about thirty questions and covers various and sometimes complex topics: responsible and inclusive research practices, public trust in science, climate crisis, reduction of GHG emissions caused by research activities, research integrity, researchers' engagement in the public sphere. It is divided into 6 sections: - Relations between science and society  - Conducting research in a changing world  - Ethics and research integrity  - Engaged research  - Science communication  To enable comparative analysis, it incorporates questions from other surveys which are cited as references.

opencc-by-4.0Apr 2024View details →

Fig. 2 in Fossil or non-fossil? A best-practice guide for archaeobotanical taxa

Fig. 2. Images of the original materials mentioned in the invalid publication of "Hordeum lagunculiforme" Bachteev. A, Charred barley grains from the archaeological excavations of the Bosporan expedition of the Leningrad Region Institute of Mineral Resources of the U.S.S.R. Academy of Sciences (3rd–2nd centuries B.C.): a, the largest grains; b, middle grains; c, small and very small grains; h, hulled; n, naked (Bachteev, 1956: fig. 1); B, Two samples (a and b) of charred barley grains from the Karmir-Blur archaeological sites: (7th–6th centuries B.C.) (Bachteev, 1956: fig. 2).

opencc-by-4.0Apr 2024View details →

Fig. 1 in Fossil or non-fossil? A best-practice guide for archaeobotanical taxa

Fig. 1. Image of the original materials for Heer's different archaeobotanical taxa. A, Faba vulgaris var. celtica-nana Heer (Heer, 1865: fig. 44–47); B, Faba vulgaris var. celtica-nana Heer (Heer, 1866: fig. 44–47); C, Triticum vulgare var. antiquorum Heer (Heer, 1865: fig. 14–18); D, Triticum vulgare var. antiquorum Heer (Heer, 1865: "1" in text-fig. on p. 5); E, "Die dichte sechszeilige Gerste Hordeum hexastichum, densum" (Heer, 1865: fig. 9); F, "Kleine Pfahlbaugerste (Hordeum hexastichum, sanctum)" (Heer, 1865: fig. 1–7); G, "Kleine Pfahlbaugerste (Hordeum hexastichum, sanctum)" (Heer, 1865: fig. 8).

opencc-by-4.0Jun 2021View details →

Agriculture and food system scenarios with particular focus on organic and agro-ecological farming practices in the EU

This is a comprehensive dataset of the agriculture and food system scenarios co-developed with stakeholders with the agricultural land use model BioBaM-GHG 2.0 and presented in Deliverable 4.2 of the H2020 project UNISECO. It includes sub-national (NUTS1/2-level) data on agricultural production and consumption, land use, greenhouse gas emissions from livestock and agricultural activities, etc. for the base year 2012 and the scenario years 2030 and 2050. The scenarios include a Business as usual case and four scenarios with focus on organic and agro-ecological farming practices in the EU, based on different storylines. Further information is available from the above-mentioned deliverable. A detailed model description is provided in the paper "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0", in which these scenarios are also presented as an exemplary application of the model BioBaM-GHG 2.0. This work was funded by the ERA-NET SusAn project 101243 AnimalFuture, as well as by the European Union’s Horizon 2020 research and innovation programme and its funding of the H2020 UNISECO project under grant agreement N°773901.