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3,121 results for “Resources”
Data from: The abundance and distributional (in)equalities of forageable street tree resources in Lagos Metropolis, Nigeria
<p>Foraging for wild resources links urban citizens to nature and biodiversity while providing resources important for local livelihoods and culture. However, the abundance and distributional (in)equity of forageable urban tree resources have rarely been examined. Consequently, this study assessed the abundance of forageable street trees and their distribution in Lagos metropolis, Nigeria. During a survey of 32 randomly selected wards across 16 local government areas (LGAs) in the metropolis, 4,017 street trees from 46 species were enumerated. The LGA with the highest number of street trees was Ikeja, with 818 trees, while Lagos Island had the lowest count, with two trees. This disparity in tree numbers could be attributed to variations in human population density within each LGA. Ninety-four percent of the street trees surveyed had at least one documented use and 76 % had two, and thus were potentially forageable. However, the most common species had relatively low forageability scores. Only 5.6 % of the total street tree population was rated as highly forageable, with a usability score of at least 11 out of 15. The most forageable street trees were fruit trees and non-native species. The forageable street trees in the LGAs showed a significant disparity in their distribution, as evidenced by a Gini coefficient of 0.81. Overall, richer neighbourhoods had a higher street tree abundance, richness, and forageability potential. To meet greening and foraging goals and address the current inequitable distribution, we suggest allocating more funds for greening, particularly in low-income neighbourhoods. Further research should evaluate forageable species from other sites to acquire a detailed understanding of the distribution and abundance of forageable resources in Lagos metropolis.</p>
The Use of Open Educational Resources (OERs) in Teaching and Learning in Higher Education Distance Learning Programmes in Cameroon
<p>A cross sectional descriptive analysis was adopted for this study. The goal was to was find out the use OERs in teaching and learning in higher education distance learning by taking a snapshot from a cross-section of the population. The institution under study is the University of Buea. The research targeted all 25 students at the master level of the Distance Education Program in the Faculty of Education, University of Buea. All the students were purposively sampled due to the small nature of the target population. A carefully designed questionnaire was used for data collection. The questionnaire had both closed and opened-ended questions which required respondents to select from a variety of responses to cover the research questions. Data was calculated and presented using frequencies tables and bar charts. After getting description of student’s responses, an analysis was done to show the situation of the use of OERs and in teaching and learning.</p>
Figure 2. – The18 in Fishers' perceptions of river resources: case study of French Guiana native populations using contextual cognitive mapping
Figure 2. – The18 concepts that fishers mentioned most frequently.
Figure 6 in Fishers' perceptions of river resources: case study of French Guiana native populations using contextual cognitive mapping
Figure 6. – Cognitive maps of threats of fishing practices on the fish resource and environment.
Figure 8 in Fishers' perceptions of river resources: case study of French Guiana native populations using contextual cognitive mapping
Figure 8. – Potential chains of events caused by the Westernization process.
Fig. 1 in Reproductive biology of Cetengraulis edentulus (Cuvier, 1829), the major fishery resource in Guanabara Bay, Brazil
Fig. 1. Geographical location of Guanabara Bay, with the surrounding cities.
Close to the edge: Spatial variation in plant diversity, biomass and floral resources in conventional and agri-environment cereal fields
<p>Non-crop (segetal) plants in arable systems are commonly perceived simply as "weeds", i.e. harmful at worst and undesirable at best. The increase in management intensity in European arable systems has vastly reduced the populations of all but the most disturbance-tolerant plant species, negatively impacting the whole agricultural food web. In recent years, efforts have been made to promote agricultural biodiversity through measures such as flower strips and unsprayed field margins. However, studies of their impacts on the arable flora have rarely considered their spatial variation within the crop field. We investigated the spatial distribution of vascular plant species richness and their contribution to the food web via biomass and flower units in conventional and agri-environment cereal fields in six regions of Germany. We studied two types of in-crop measures (extensive cereals without pesticides or fertiliser, and with or without intercropping with flowering species) and one adjacent measure (neighbouring flower strip), recording at 1 m intervals from the field edge to interior. These results were then extrapolated to illustrate the effects of these measures on resource provision at the field scale. Species richness and plant biomass dropped off sharply after the first metre in the conventional treatments, regardless of the adjacent habitat. The "extensive" treatments maintained a much higher level of diversity and resource provision into the field interior. At the field level, this can mean more than sixty-fold difference in provision of flowering resources between conventional management (1900 flower units/ha) and agri-environment measures (127,000 units/ha for extensive cereals).</p> <p><em>Synthesis and applications:</em></p> <p>The strong edge effects we found in conventional cultivation support the premise that reducing field sizes could play a role in promoting in-crop biodiversity. However, incorporating extensive field margins as an agri-environment measure would be more efficient at maximising diversity of generalists whilst maintaining high yields.</p>
Fig. 2 in Impacts of resource limitations on the reproduction behaviour in the Agile Frog (Rana dalmatina) on the territory of Natura park "Shumensko plato"
Fig. 2. Male and female Rana dalmatina in an artificial basin in amplexus for 12 days.
Fig. 1 in Impacts of resource limitations on the reproduction behaviour in the Agile Frog (Rana dalmatina) on the territory of Natura park "Shumensko plato"
Fig. 1. Registered egg-clutches out of the water near the ponds.
Section 5.2 "Task Area 2: Technical infrastructure and cloud resources" Figure 9
<p>Figure 9. <em><span>In the NFDI4BIOIMAGE Cloud, provisional S3 storage and core SaaS offerings will be provided by platform operators for all participants. Service operators may deploy and manage services in the IaaS cloud or in Kubernetes.</span></em></p> <p>from NFDI Grant Application, "<strong>National Research Data Infrastructure for Microscopy and Bioimage Analysis</strong>" (NFDI4BIOIMAGE)</p>
Fig. 2 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 2. Holotype of Isoetes coreana Y.H. Chung & H.K. Choi.
Fig. 3 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 3. Holotype of Huperzia jejuensis B.Y. Sun & J. Lim.
Example of Resource Abstraction in TeMoto
<p><em>a) An action requires the position operators hand.<br> b) The information about the hand is retrieved by combining two resources – camera feed and an algorithm that uses the feed to detect the hand.<br> c) The request to initiate a specific camera “cam_x” and specific algorithm “alg_y” is directed to the External Resource Manager</em></p>
Data supplementing the article "Boosting DNA metabarcoding for biomonitoring with phylogenetic estimation of OTUs' ecological profiles" F. Keck, V. Vasselon, F. Rimet, A. Bouchez, and M. Kahlert submitted to Molecular Ecology Resources journal
<p>These data supplement the article "Enhancing DNA metabarcoding for biomonitoring with phylogenetic estimation of OTUs' ecological profiles" F. Keck, V. Vasselon, F. Rimet, A. Bouchez, and M. Kahlert submitted to Molecular Ecology Resources journal</p> <p>The directory contains the following files:</p> <p><strong>278 (139 x 2 replicates) samples fastq files.rar </strong>- contains the 278 fastq files provided by the sequencing platform with demultiplexed and contig DNA reads corresponding to the 139 samples with 2 sequencing replicates (A and B).</p> <p><strong>Counts_diatoms.xlsx </strong>- contains the morphological inventories with species list (Omnidia code) and valve abundances for the 139 samples.</p> <p><strong>Sites_list.xlsx </strong>- contains information regarding the 139 samples, including: River name, GPS coordinates, code used for molecular analysis and corresponding to sequencing fastq names.</p>
Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms: Virtual Machine ID Dataset
<p>The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in Section 5 [1] and the file named "vmtable_lifetime_VMoL_55.csv" holds the IDs used in Section 6 [1]. The first column in both files refers to the ID labels in [1], while the second, third, and fourth columns refer to the Virtual Machine IDs, the Subscription IDs, and the Deployment IDs, respectively.</p> <p> </p> <p>[1] Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in<br> Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proc. ACM Meas. Anal. Comput. Syst. 3, 1,<br> Article 179 (March 2019).</p> <p>[2] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. In Proceedings of SOSP’17. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3132747.3132772 Dataset access: https://github.com/Azure/AzurePublicDataset (August 2018)</p>
DroidLeaks: A Large Collection of Resource Leak Bugs in Real-World Android Apps
<p>DroidLeaks features 292 diverse resource leak bugs in popular and large-scale open-source Android apps. For each bug, DroidLeaks provides links to:<br> 1. the code repository of the app subject<br> 2. the concerned resource class<br> 3. the buggy code revision (and buggy file and method names)<br> 4. the bug-fixing code revision (i.e., link to the patch)<br> 5. the bug report or the corresponding pull request for patches (if located)</p>
boones348/Resource-PSA-Project: Resource PSA Project (v.2)
<p>This is a method for tracking data updates for our PSA project.</p>
Tweets informing about resource needs and availabilities in post-disaster situation
<p>This is the dataset for the paper:</p> <p>Moumita Basu, Anurag Shandilya, Prannay Khosla, Kripabandhu Ghosh, Saptarshi Ghosh. Extracting Resource Needs and Availabilities from Microblogs for Aiding Post-Disaster Relief Operations. IEEE Transactions on Computational Social Systems, 2019.</p> <p>The data contains tweetids of tweets (from Twitter) posted during (1) the 2015 Nepal earthquake, and (2) 2016 Italy earthquake. The tweets that inform about need and availability of various types of resources are identified.</p> <p>The dataset can be used for developing algorithms for microblog retrieval / classification, and for understanding social media activity in the aftermath of a disaster event.</p>
OpenIng - Open Access und Open Educational Resources in den Ingenieurwissenschaften : Fragebogen zur bundesweiten Umfrage
<p>Im Rahmen eines vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Verbundprojekts der TU Darmstadt, TU Braunschweig und der Universität Stuttgart startete im Oktober 2018 eine Befragung zum Publikationsverhalten in den Ingenieurwissenschaften im deutschsprachigen Raum. Ziel des Projekts OpenIng ist, nach den Ergebnissen einer Bedarfsermittlung zielgerichtete Services zu etablieren, die das Publizieren in Open Access erleichtern sollen. Forscherinnen und Forscher waren aufgerufen, sich an der Umfrage zu beteiligen und hiermit an ihr Fach angepasste Services mit zu entwickeln und so den Prozess Richtung freie Verfügbarkeit von Wissenschaft mit zu gestalten. Grundlage für eine Entwicklung neuer Angebote sind die Ergebniss aus den Online-Fragebögen.</p>
An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers
<p>Model and data outcomes --> We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.</p>
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.