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725 results for “recommendation”
Dataset_Dryocosmus_Torymus_Article recommended by PCI Zoology: The open bar is closed: restructuration of a native parasitoid community following successful control of an invasive pest.
<p>This Dataset has been used for the analyses within the paper recommended by PCI Zoology and entitled:</p> <p>"The open bar is closed: restructuration of a native parasitoid community following successful control of an invasive pest."<br> </p> <p> </p>
Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World
<p><strong>Extended Version of The Paper:</strong></p> <pre><code class="language-markdown">Towards_Safety_and_Sustainability_Extended.pdf</code></pre> <p><strong>Dataset Information:</strong></p> <p>List of files</p> <pre><code class="language-markdown">Customer_Choice_Survey.csv NYC_Google.csv NYC_Yelp.csv SF_Google.csv SF_Yelp.csv</code></pre> <p>Field Details in Each File</p> <ol> <li><strong>"Customer_Choice_Survey.csv":</strong> Local recommendations received on Google Local (Google Maps) for different customer locations in New York and San Francisco. <pre><code class="language-markdown">Each respondent was first asked some basic details. Then 7 rounds of ranking questions were asked. In each round, they were given a list of 10 restaurants with random combinations of rating, distance and cuisine. They were asked to rank top 5 one-by-one out of those 10 provided. This becomes evident from the question titles provided the file.</code></pre> <p> </p> </li> <li><strong>"NYC_Google.csv" and "SF_Google.csv":</strong> Local recommendations received on Yelp for different customer locations in New York and San Francisco. <pre><code class="language-markdown">"customer_location": location of the customer where she gets recommendation "rank": rank of the restaurant in the recommended list "id": restaurant's id internal to google "latitude": latitude of restaurant's geographic coordinates "longitude": longitude of restaurant's geographic coordinates "name": name of the resturant "price_level": cheap/costly level "rating": average rating of the restaurant "rating_count": number of ratings collected for the restaurant "address": address of the restaurant</code></pre> <p> </p> </li> <li>"NYC_Yelp.csv" and "SF_Yelp.csv" <pre><code class="language-markdown">"customer_location": location of the customer where she gets recommendation "rank": rank of the restaurant in the recommended list "id": restaurant's id internal to yelp "latitude": latitude of restaurant's geographic coordinates "longitude": longitude of restaurant's geographic coordinates "name": name of the resturant "rating": average rating of the restaurant "rating_count": number of ratings collected for the restaurant "address": address of the restaurant "url": link to the restaurant's yelp page</code></pre> <p> </p> </li> </ol> <p>Link to Code Repository:<br> <a href="https://github.com/gourabkumarpatro/pandemic-aware_local_recommendation">Pandemic-Aware Local Recommendation</a></p> <p><strong>Citation Information:</strong><br> Please cite the following paper if you use this dataset.<br> <br> <strong>"<em>Towards Sustainability and Safety: Designing Local Recommendations for Post-pandemic World</em>"</strong><br> Gourab K Patro, Abhijnan Chakraborty, Ashmi Banerjee, Niloy Ganguly.<br> In proceedings of Fourteenth ACM Conference on Recommender Systems (RecSys-2020), Virtual Event, Brazil.</p> <p>You can also use the following <strong>bibtex</strong>.</p> <pre><code class="language-markdown">@inproceedings{10.1145/3383313.3412251, author = {Patro, Gourab K and Chakraborty, Abhijnan and Banerjee, Ashmi and Ganguly, Niloy}, title = {Towards Safety and Sustainability: Designing Local Recommendations for Post-Pandemic World}, year = {2020}, isbn = {9781450375832}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3383313.3412251}, doi = {10.1145/3383313.3412251}, booktitle = {Fourteenth ACM Conference on Recommender Systems}, pages = {358–367}, numpages = {10}, keywords = {COVID-19, Local Recommendation, Google Local, Yelp, Safety, Social Distancing, Sustainability, Bipartite Matching}, location = {Virtual Event, Brazil}, series = {RecSys '20} }</code></pre> <p> </p>
Challenges and Recommendations in DevOps Education: A Systematic Literature Review
<p>Over the last years, DevOps has gained more importance and attention from the software industry, given its role in enabling continuous software delivery. As a new area, DevOps has brought significant challenges for the academy, both in terms of research topics and teaching strategies. In this paper, we present a systematic literature review that aims to identify challenges and recommendations for teaching DevOps. Our findings show a total of 73 challenges and 85 recommendations organized into different seven categories from a total of 18 papers selected. We also discuss how existing recommendations address the challenges found in the study, thus contributing to the preparation and execution of DevOps courses. Finally, we investigate if challenges and recommendations are specific for teaching DevOps.</p>
Work Data Extraction and Recommendations
<p>Work Data Extraction and Recommendations</p>
Extended Cookbook Data Extraction and Recommendations
<p>Extended Cookbook Data Extraction and Recommendations</p>
Data from: A review of riverine ecosystem service quantification: research gaps and recommendations
1.Increasing demand for benefits provided by riverine ecosystems threatens their sustainable provision. The ecosystem service concept is a promising avenue to inform riverine ecosystem management, but several challenges have prevented the application of this concept. 2.We quantitatively assess the field of riverine ecosystem services' progress in meeting these challenges. We highlight conceptual and methodological gaps, which have impeded integration of the ecosystem service concept into management. 3.Across 89 relevant studies, 33 unique riverine ecosystem services were evaluated, for a total of 404 ecosystem service quantifications. Studies quantified between one and 23 ecosystem services, although the majority (55%) evaluated three or less. Among studies that quantified more than one service, 58% assessed interactions between services. Most studies (71%) did not include stakeholders in their quantification protocols, and 34% developed future scenarios of ecosystem service provision. Almost half (45%) conducted monetary valuation, using 16 methods. Only 9% did not quantify or discuss uncertainties associated with service quantification. The indicators and methods used to quantify the same type of ecosystem service varied. Only 3% of services used indicators of capacity, flow, and demand in concert. 4.Our results suggest indicators, data sources, and methods for quantifying riverine ecosystem services should be more clearly defined and accurately represent the service they intend to quantify. Furthermore, more assessments of multiple services across diverse spatial extents and of riverine service interactions are needed, with better inclusion of stakeholders. Addressing these challenges will help riverine ecosystem service science inform river management. 5.Synthesis and applications. The ecosystem service concept has great potential to inform riverine ecosystem management and decision making processes. However, this review of riverine ecosystem service quantification uncovers several remaining research gaps, impeding effective use of this tool to manage riverine ecosystems. We highlight these gaps and point to studies showcasing methods that can be used to address them.
Data from: Whole-genome sequencing approaches for conservation biology: advantages, limitations, and practical recommendations
Whole-genome resequencing (WGR) is a powerful method for addressing fundamental evolutionary biology questions that have not been fully resolved using traditional methods. WGR includes four approaches: the sequencing of individuals to a high depth of coverage with either unresolved (huWGR) or resolved haplotypes (hrWGR), the sequencing of population genomes to a high depth by mixing equimolar amounts of unlabelled-individual DNA (Pool-seq), and the sequencing of multiple individuals from a population to a low depth (lcWGR). These techniques require the availability of a reference genome. This, along with the still high cost of shotgun sequencing and the large demand for computing resources and storage, has limited their implementation in non-model species with scarce genomic resources and in fields such as conservation biology. Our goal here is to describe the various WGR methods, their pros and cons, and potential applications in conservation biology. WGR offers an unprecedented marker density and surveys a wide diversity of genetic variations not limited to single nucleotide polymorphisms (e.g. structural variants and mutations in regulatory elements), increasing their power for the detection of signatures of selection and local adaptation as well as for the identification of the genetic basis of phenotypic traits and diseases. Currently though, no single WGR approach fulfills all requirements of conservation genetics, and each method has its own limitations and sources of potential bias. We discuss proposed ways to minimize such biases. We envision a not distant future where the analysis of whole genomes becomes a routine task in many non-model species and fields including conservation biology.
Data from: Recommendations for using msBayes to incorporate uncertainty in selecting an ABC model prior: a response to Oaks et al.
Prior specification is an essential component of parameter estimation and model comparison in Approximate Bayesian computation (ABC). Oaks et al. present a simulation-based power analysis of msBayes and conclude that msBayes has low power to detect genuinely random divergence times across taxa, and suggest the cause is Lindley's paradox. Although the predictions are similar, we show that their findings are more fundamentally explained by insufficient prior sampling that arises with poorly chosen wide priors that critically undersample nonsimultaneous divergence histories of high likelihood. In a reanalysis of their data on Philippine Island vertebrates, we show how this problem can be circumvented by expanding upon a previously developed procedure that accommodates uncertainty in prior selection using Bayesian model averaging. When these procedures are used, msBayes supports recent divergences without support for synchronous divergence in the Oaks et al. data and we further present a simulation analysis that demonstrates that msBayes can have high power to detect asynchronous divergence under narrower priors for divergence time. Our findings highlight the need for exploration of plausible parameter space and prior sampling efficiency for ABC samplers in high dimensions. We discus potential improvements to msBayes and conclude that when used appropriately with model averaging, msBayes remains an effective and powerful tool.
Data from: Workshop on reconstruction schemes for magnetic resonance data: summary of findings and recommendations
The high-fidelity reconstruction of compressed and low-resolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the Royal Society through the Newton Mobility Grant Scheme, we held a half-day workshop on reconstruction schemes for MR data on 17 August 2016 to discuss new ideas from related research fields that could be useful to overcome the shortcomings of the conventional reconstruction methods that have been evaluated to date. Participants were 21 university students, computer scientists, image analysts, engineers and physicists from institutions from six different countries. The discussion evolved around exploring new avenues to achieve high resolution, high quality and fast acquisition of MR imaging. In this article, we summarize the topics covered throughout the workshop and make recommendations for ongoing and future works.
FIGURE 42. Range map for Hippocampus zosterae. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 42. Range map for Hippocampus zosterae. See Figure 2 caption for further details.
FIGURE 40. Range map for Hippocampus whitei. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 40. Range map for Hippocampus whitei. See Figure 2 caption for further details.
FIGURE 41. Range map for Hippocampus zebra. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 41. Range map for Hippocampus zebra. See Figure 2 caption for further details.
FIGURE 36. Range map for Hippocampus spinosissimus. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 36. Range map for Hippocampus spinosissimus. See Figure 2 caption for further details.
FIGURE 37. Range map for Hippocampus subelongatus. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 37. Range map for Hippocampus subelongatus. See Figure 2 caption for further details.
FIGURE 38. Range map for Hippocampus trimaculatus. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 38. Range map for Hippocampus trimaculatus. See Figure 2 caption for further details.
FIGURE 35. Range map for Hippocampus sindonis. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 35. Range map for Hippocampus sindonis. See Figure 2 caption for further details.
FIGURE 34. Range map for Hippocampus satomiae. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 34. Range map for Hippocampus satomiae. See Figure 2 caption for further details.
FIGURE 32. Range map for Hippocampus pusillus. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 32. Range map for Hippocampus pusillus. See Figure 2 caption for further details.
FIGURE 31. Range map for Hippocampus pontohi. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 31. Range map for Hippocampus pontohi. See Figure 2 caption for further details.
FIGURE 30. Range map for Hippocampus planifrons. See Figure 2 in A global revision of the Seahorses Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and biogeography with recommendations for further research
FIGURE 30. Range map for Hippocampus planifrons. See Figure 2 caption for further details.
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.