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65 results for “Research challenges”
Chemical data accompanying the manuscript "Chromium cycling in redox-stratified basins challenges δ53Cr paleoredox proxy applications" in Geophysical research Letters
<p>Water column and sediment chromium concentration and stable isotope data and ancillary metal data from Lake Cadagno, Switzerland. These data accompany a manuscript by the same authors in Geophysical Research Letters (doi: 10.1029/2022GL099154).</p> <p> </p> <p>The associated CTD data are available in the following Zenodo dataset: Sepúlveda Steiner, O., Carlino, C., Haizmann, E., Roman, S., Wüest, A., & Bouffard, D. (2022). Lake Cadagno 2017 CTD and water quality monitoring [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.7127882">http://doi.org/10.5281/zenodo.7127882</a></p>
The evolution and future of research on Nature-based Solutions to address societal challenges
<p>This dataset comprises the bibliographic text files used to analyse the Nature-based Solutions research landscape as presented in:</p> <ul> <li>Dunlop, T., Khojasteh, D., Cohen-Shacham, E., Glamore, W., Haghani, M., van den Bosch, M., Rizzi, D., Greve, P., Felder, S. The Evolution and Future of Research on Nature-based Solutions to Address Societal Challenges. <em>Communications Earth & Environment</em>. 2024.</li> </ul> <p>Excel spreadsheets containing data for the Global Water Security Index (Gain et al., 2016) presented in Figure 2 and the data required to reproduce Figures 1 and 2 in the paper above are also shared.</p>
Numerical data analysed to produce Figure 3a of Nature Climate Change submission "Five challenges for subseasonal to decadal prediction research " by Merryfield et al.
<p>NetCDF4-formatted files containing daily sea ice concentration data from Environment and Climate Change Canada's CanSIPSv2 seasonal forecasting system described in Lin et al. (2020) https://doi.org/10.1175/WAF-D-19-0259.1 </p> <ul> <li>2 models, CanCM4i and GEM-NEMO</li> <li>10 ensemble members for each model, each in separate files as indicated by suffixes _1 to _10</li> <li>initialized May 1, 1980 to 2021</li> <li>840 files total (42 predicted years x 10 ensemble members x 2 models)</li> <li>model outputs interpolated to common 1-degree grid</li> </ul> <p>The calibrated probabilistic forecast map shown in Figure 3a is based on the nonhomogeneous censored Gaussian regression (NCGR) method described in Dirkson et al, (2021) https://doi.org/10.1175/WAF-D-20-0066.1 and produced using scripts available at https://github.com/adirkson/sea-ice-timing </p> <p>The procedure uses as inputs</p> <ul> <li>freeze-up dates calculated from the provided model outputs as described in Sigmond et al. (2016) https://doi.org/10.1002/2016GL071396</li> <li> <p>NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3: https://nsidc.org/data/G02202/versions/3</p> </li> </ul>
The evolution of gender monitoring and its challenges in Research and Innovation in Europe: She Figures reports analysis dataset
<p><span>The article delves into the European Commission's flagship initiative on gender monitoring in science and innovation, offering a responsible metrics perspective and drawing on equality policy literature. Over two decades, the initiative has evolved from competitiveness-related justifications to more transformative objectives related to equality policy evaluation, with the measurement areas and policy focus also undergoing changes. While there has been notable progress, the article points out a logic of invisibility in how dimensions and indicators are conceptualised and their data sources and interpretation. However, it also highlights a significant improvement in the information available. The article suggests that the contextualisation of the process could be enhanced to better integrate it into the policy-making cycle, a crucial area for further research. It concludes with proposals for future gender monitoring science and innovation. The aim is to offer an encouraging vision of monitoring that counts more on who is monitored and in opening up the debates instead of closing them.</span></p>
Meta-analysis reveals challenges and gaps for genome-to-phenome research underpinning plant drought response.
<p>Data used to identify species occurring in hyperarid environments for analyses described in "Meta-analysis reveals challenges and gaps for genome-to-phenome research underpinning plant drought response." The "PlantsLackingHumanUse_PrelimQCd_Data.csv" contains data for all plants queried, while "HyperArid_Occurrences.csv" contains the subset of data corresponding to plants occurring in hyperarid environments.</p>
Challenges and Realities in Asian Elephant Research: A Global Bibliometric Analysis Highlighting Conservation Crises and Collaborative Opportunities
<p><span>The Asian elephant (<em>Elephas maximus</em>), also known as Asiatic elephant is facing a significant decline in its wild populations due to habitat loss, fragmentation and degradation, leading to increased conflicts between humans and elephants. Preserving this iconic species is crucial, prompting researchers to explore various aspects of its biology, behavior, ecology, and conservation efforts. </span><span>In light of the profound importance of this subject</span><span>, this study utilizes a descriptive systematic literature review (SLR) to examine global research trends on Asian elephants. Analyzing a dataset of 1,780 articles spanning from 1914 to 2022, the study reveals a notable increase in publications, particularly since 2000, with 142 articles published in 2022 alone. This research provides a comprehensive overview of advancements in Asian elephant studies, promoting international collaboration and knowledge exchange among researchers. While research from leading scientific countries can aid in conservation efforts, there is a call for more inclusive, participatory and fair approaches. The study demonstrates a commitment to Equity, Inclusion, and Diversity (EID) by adopting a multifaceted approach that considers the needs, perspectives and contributions of diverse stakeholders. By addressing disparities and advocating for social justice within elephant conservation, the study emphasizes the urgent need for collective action in conservation, welfare improvement, conflict resolution, ecosystem balance, and overall enhancement of understanding about Asian elephants. Serving as a valuable resource for policymakers, conservation organizations, and researchers, the study not only synthesizes existing literature but also identifies research gaps, highlights areas requiring immediate attention, and offers collaborative opportunities for global scientists to contribute to the well-being and preservation of this majestic species.</span></p>
Fig. 2. A in Conservation of Peninsular Malaysia's Karst Herpetofauna: A review of herpetological discoveries, research trends, and challenges
Fig. 2. A, Limnonectes blythii from an unnamed cave in Tambun, Perak; B, Limnonectes cf. macrognathus from Gua Wang Burma, Perlis; C, Phrynoidis asper from Gua Wang Burma, Perlis; D, Hylarana malayana from Gua Wang Burma, Perlis; E, Lycodon cavernicolus from Gua Wang Burma, Perlis; F, Elaphe taeniura ridleyi from Gua Wang Burma, Perlis; G, Cyclemys sp. from Gua Katak, Merapoh; H, Dogania subplana from Gua Wang Burma, Perlis. (Photographs by Liz Price [A & G] and Evan Quah [B–F & H]).
Fig. 1 in Conservation of Peninsular Malaysia's Karst Herpetofauna: A review of herpetological discoveries, research trends, and challenges
Fig. 1. Map of the main regions of karst in Peninsular Malaysia and selected areas of known herpetofaunal endemism. (1) Langkawi Archipelago, Kedah. (2) Nakawan Range that encompasses locations like Gua Wang Burma, Wang Kelian and Ulu Wang, as well as karst outcrops in Perlis like Bukit Cabang and Kuala Perlis. (3) Gunung Baling, Kedah. (4) Lenggong Valley, Perak. (5) Kinta Valley, Perak that encompasses locations such as Gua Kanthan, Gua Tempurung, Gunung Lanno, and Tambun. (6) Batu Caves and Dark Cave, Selangor. (7) Gunung Reng, Kelantan. (8) Gua Musang area, Kelantan that encompasses locations like Gua Bayu and Felda Chiku 7. (9) Merapoh, Pahang that encompasses locations like Gua Gunting and Gua Goyang. (10) Gunung Senyum, Pahang. (Map courtesy of Junn Kitt Foon and adapted from Liew et al. (2016)).
Fig. 3. A in Conservation of Peninsular Malaysia's Karst Herpetofauna: A review of herpetological discoveries, research trends, and challenges
Fig. 3. A, Cnemaspis cf. hangus from Felda Chiku 7, Kelantan; B, Cnemaspis biocellata from Gua Wang Burma, Perlis; C, Cyrtodactylus guakanthanensis from Gua Kanthan, Perak; D, Cyrtodactylus lenggongensis from Lenggong Valley, Perak; E, Cyrtodactylus hidupselamanya from Felda Chiku 7, Kelantan; F, Cyrtodactylus evanquahi from Gunung Baling, Kedah; G, Cyrtodactylus consobrinus from Curtain Cave, Pasoh, Negeri Sembilan; H, Calotes emma from Gua Kelam, Perlis. (Photographs by Ali Shamsul Bahar [G] and Evan Quah [A–F & H])
Fig. 5 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 5. Data sources according to epidemiological level and scale. Representation of the data sources (left column) used for each epidemiological level (central column), and the scale of the corresponding data sources (right). Colors of the left column correspond to general data-sources categories; for example, green corresponds to biological/biodiversity data sources (e.g., Biodiversity repositories and biological general source). Health-related sources are represented in purple (Health gov: governmental, init-program: initiative or programs). Using this broad categorization, most of the sources contribute with data related to the three epidemiological levels, although with an unpaired flow. For example, scientific literature has a lower contribution for hosts/ reservoirs, and biodiversity-biological sources have a minor contribution for pathogens. Most data sources have a global scale meanwhile governmental sources have a relevant contribution to pathogen data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 3. Diseases explored in the studies according to the use of GBIF and the taxa class of the causal pathogen. In the right panel: positive studies (i.e., those studies that used GBIF-mediated data for at least one of the variables explored), negative studies in the left. Bars represent the number of studies exploring each disease, and filling colors represent the corresponding taxa class of the disease agent or causal pathogen (Purple scale, with lighter coloration for fungal diseases, followed by parasites, bacteria, and viruses with the darker purple). Abbreviations: the abbreviation Oth (Fungal Oth, Parasite Oth, Bacteria Oth, Virus Oth) represents a category with multiple species, merged to simplify the figure due to the low number of studies of each disease. Ricket-related: diseases related to Rickettsia species; Paras: parasites; Schistos: Schistosomiases; Leishm: Leishmaniases (both cutaneous and visceral); Dis: disease; Bact: bacteria; Fev: fever; V: virus; CoronaV: Coronavirus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 2. Research areas identified in the studies. Research areas subcategories are represented in the left axis, and general research area groups in the right axis. Orange circles represent the number of positive studies, the blue circles the negatives, and the black lines between them represent the differences in the number of studies, in which larger lines represent larger differences between positive and negatives. Orange icons correspond to research areas with larger number of positive studies, i.e., positive studies were more related to Biology (Bio), Ecology (Ecol) and Other (Hum Soc: Human society; Phy Env Geo: Physical environmental geology; Earth Atm: Earth and atmospheric sciences). Negative studies were more frequent in research areas with blue icons, including Medical (Med) and Veterinary sciences (Vet: Veterinarian and agriculture). In the green icon (Eng Inf Mat: Engineering, informatics, and mathematics) there was no major differences between groups. Subcategories: Bio Zoo: Biology and zoology; Bio Evo Gen: Biology, evolution, and genetics; Bio Bioch: Biology and biochemistry; Env Mang: Environmental management and sciences; Eco App: Ecological applications; Math Stat: Mathematical statistics; Inf Comp: informatics and computing; Eng Geom: Engineer and geometrics; Microb: Microbiology; Pub Heal: Public health; Med Micro: Medical microbiology; Med Clin Heal: Medical clinical and health. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 1 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 1. General framework of analyses at study- and variable-levels. In the upper section (study-level, in grey), studies are divided in those that used GBIF-mediated data (positives, in orange) and those that did not (negatives, in blue). Positive studies were group according if GBIF was used as the only data source for all variables (2 studies), or if the variables were based on GBIF together with other data sources (105 studies). In the variable-level section (bottom, white background) the total 358 variables extracted from the positive and negative studies were categorized according to the specific use of GBIF, resulting in five types of variables, four of them extracted from the positive studies. Note that in those studies based on GBIF, the different variables could be based on GBIF alone (33 variables), GBIF together with other sources (85), or specific variables may not be based on GBIF-mediated data at all (81 variables). Finally, each variable was related to different epidemiological roles, resulting in a larger number hosts/reservoirs variables, mostly based on GBIF-mediated data, and a higher presence of pathogen species-variables not using GBIF. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 4 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 4. Variables according to taxon class (Y- axis) epidemiological level (bar colour) and the use of GBIF- mediated data. Bars represent the number of variables by each taxon class (Y-axis), separated in two panels according to the use of GBIF-mediated data. In the right panel, variables in which GBIF-mediated data was used (Used_GBIF), in the left panel variables in which data was not obtained from GBIF (NonGBIF). Next to the bars, the number of variables by each epidemi- ological level, and percentage in rela- tion to the total number of variables of each group (Used_GBIF: 120 and Non- GBIF: 238). Taxon classes are grouped by taxonomic associations (e.g., birds, primates, ticks, mosquitoes); however, some were merged to simplify the figure. For example, mamm/oth/var includes multiple mammal species which were sparsely mentioned; simi- larly, hosts/res var, vector other and path other grouped several species participating as hosts/reservoirs, vec- tors, and pathogens, respectively. Bar colors represent epidemiological levels (pathogens, vectors, hosts/reservoirs), and Other (in sienna) includes species participating as hosts' regulator, predators, among others. GBIF-mediated data was only used in three pathogen variables (purple), representing only 2.5% of the variables in which GBIF-mediated data was used, resulting in a remarkable difference with other sources (NonGBIF), in which pathogens represented a 59.7%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Dataset for "Tackling global challenges with coherent policies producing more researchers and diaspora engagement"
<p>A list of science policies that were published between 2002 and 2021 by the African Union, Southern African Development Community, and the Government of Zimbabwe.</p>
Dataset for IDRC Project: Exploring the opportunities and challenges of implementing open research strategies within development institutions. International Development Research Center.
<p>Data Package: Exploring the opportunities and challenges of implementing open research strategies within development institutions</p> <p>DOI for this package: https://doi.org/10.5281/zenodo.844394<br> Project Description: https://doi.org/10.3897/rio.2.e8880<br> Data Management Plan: https://doi.org/10.3897/rio.3.e14672<br> Other Related Documents and Reports: https://riojournal.com/collection/18/<br> Funder: International Development Research Centre/Centre de rechereches pour le développement international, https://doi.org/10.13039/501100000193</p> <p><br> Abstract<br> ========</p> <p>This is the Data Package for the project "Exploring the opportunities and challenges of implementing open research strategies within development institutions" the proposal for which was published as https://doi.org/10.3897/rio.2.e8880. The research project conducted open data pilot case studies with seven IDRC grantees to develop and implement open data management and sharing plans. The results of the case studies served to refine guidelines for the implementation of development research funders’ open research data policies.</p> <p>Contents<br> ========</p> <p>The Data Package contains all the public data generated by the project. The package was curated and metadata generated, including an HTML Catalog using the Calcyte Tool (https://codeine.research.uts.edu.au/eresearch/calcyte) developed at University of Technology Sydney.</p> <p>The project had two major phases:</p> <p>1. A review, based on desk work and interviews with data management experts<br> 2. Case studies, based on implementing open data practices within seven IDRC funded research projects</p> <p>Review<br> ------</p> <p>The review, published at https://riojournal.com/article/14673/ was supported by desk work and interviews. The materials related to the interviews can be found in the directory:</p> <p>* Policy and Implementation Review Interviews</p> <p>Case Studies<br> ------------</p> <p>Seven IDRC-funded projects were contributed to the pilot project.</p> <p>The materials generated by the case studies and used to support the final report (to be published with the collection at https://riojournal.com/collection/18/) are found in the following directories.</p> <p>* Introductory_Data_Workshop_Materials<br> * Introductory_Workshop_Presentations<br> * Data Management Planning<br> * SciDataCon Presentations<br> * Final_Project_Workshop_Materials<br> * Final_Project_Workshop_Presentations</p> <p>The files are encoded with a three letter code that identifies the relevant contributing project in each case. The contributing projects were:</p> <p>* Crowd Sourcing Data to fight Social Crimes: Harassmap, Egypt (HMP)<br> * The Brazilian Virtual Herbarium: CRIA, Brazil(BVH)<br> * Strengthening the Economic Committee of the National Assembly in Vietnam: Centre for Analysis and Forecasting, Vietnam (ECV)<br> * The Impact of Copyright User Rights: Derechos Digitales, Columbia (DED)<br> * Establishing a clearinghouse for tobacco economic data in Africa: DataFirst, South Africa (TED)<br> * Les problèmes négligés des systèmes de santé en Afrique : une incitation aux réformes: LASDEL, Niger (NDF)<br> * Indigenous Knowledge in Climate Change: Natural Justice, South Africa (IKC)</p> <p>More details will be found in the Case Studies and in the Final Report (forthcoming at https://riojournal.com/collection/18/)</p> <p>References<br> ==========</p> <p>* Neylon C, Chan L (2016) Exploring the opportunities and challenges of implementing open research strategies within development institutions. Research Ideas and Outcomes 2: e8880. https://doi.org/10.3897/rio.2.e8880<br> * Neylon C (2017) Data Management Plan: IDRC Data Sharing Pilot Project. Research Ideas and Outcomes 3: e14672. https://doi.org/10.3897/rio.3.e14672<br> * Neylon C, Chan L (2016-17) Exploring the opportunities and challenges of implementing open research strategies within development institutions: A project of the International Development Research Center, Research Ideas and Outcomes Collection, https://riojournal.com/collection/18/</p> <p> </p>
Tendencies and challenges in worldwide research on PROBIOTICS
<p>We include several tables on research in probiotics, including the number of funded and unfunded published articles and the number of citations during the period under study (2009-2018); 1829 different journals; thematic areas of the journals in which the works were published; countries of publication and key words.</p>
Data of "Poor repeatability of cortisol responses to adrenocorticotropic hormone (ACTH) in beef heifers: is the ACTH challenge a suitable measure for stress research in cattle?"
<p>Data for article "Poor repeatability of cortisol responses to adrenocorticotropic hormone (ACTH) in beef heifers: is the ACTH challenge a suitable measure for stress research in cattle?" Dataset of 64 crossbred beef heifers which were subjected to three ACTH challenges. Both experimental independent variables (animal id, horn status, replicate, time of day of the ACTH challenge, ACTH challenge number) and post-ACTH salivary cortisol concentrations (at the seven sampling timepoints and area under the curve values) are presented.</p>
Results of the ISMRM 2020 joint Reproducible Research & Quantitative MR study groups reproducibility challenge on phantom and human brain T<sub>1</sub> mapping
<p>This dataset includes both raw data and processed T1 maps obtained from the 2020 challenge on inversion recovery T1 mapping organized by the International Society in Magnetic Resonance in Medicine (ISMRM) Reproducible Research Study Group (RRSG). For a comprehensive overview of the data distribution submitted for this challenge, please visit <a href="https://rrsg2020.db.neurolibre.org/">https://rrsg2020.db.neurolibre.org</a>. It's important to note that this dataset exclusively comprises ISMRM-NIST system phantom data.</p> <p>Dataset provided for NeuroLibre preprint. Author repo: https://github.com/rrsg2020/paper NeuroLibre fork:https://github.com/roboneurolibre/paper</p> <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/14">NeuroLibre technical screening.</a></p> <p><strong><a href="https://neurolibre.org">https://neurolibre.org</a></strong></p>
Supplementary material 3 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Praxisbericht: Entwicklung eines Maßnahmenkatalogs zur Verbesserung des Forschungsdatenmanagements am Herder-Institut für historische Ostmitteleuropaforschung
ScienceDex guides
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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.