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5,526 results for “information”
FooDrugs database: A database with molecular and text information about food - drug interactions
<p>FooDrugs database is a development done by the Computational Biology Group at IMDEA Food Institute (Madrid, Spain), in the context of the Food Nutrition Security Cloud (FNS-Cloud) project. Food Nutrition Security Cloud (FNS-Cloud) has received funding from the European Union's Horizon 2020 Research and Innovation programme (H2020-EU.3.2.2.3. – A sustainable and competitive agri-food industry) under Grant Agreement No. 863059 – <a href="http://www.fns-cloud.eu">www.fns-cloud.eu</a> (See more details about FNS-Cloud below)</p> <p>FooDrugs stores information extracted from transcriptomics and text documents for foo-drug interactiosn and it is part of a demonstrator to be done in the FNS-Cloud project. The database was built using MySQL, an open source relational database management system. FooDrugs_V2 host information for a total of 161 transcriptomics GEO series with 585 conditions for food or bioactive compounds (see below changes in versions V3 and V4). Each condition is defined as a food/biocomponent per time point, per concentration, per cell line, primary culture or biopsy per study. FooDrugs includes information about a bipartite network with 510 nodes and their similarity scores (tau score; https://clue.io/connectopedia/connectivity_scores) related with possible drug interactions with drugs assayed in conectivity map (https://www.broadinstitute.org/connectivity-map-cmap). The information is stored in eight tables: </p> <ul> <li> <p>Table “study” : This table contains basic information about study identifiers from GEO, pubmed or platform, study type, title and abstract </p> </li> <li> <p>Table “sample”: This table contains basic information about the different experiments in a study, like the identifier of the sample, treatment, origin type, time point or concentration.</p> </li> <li> <p>Table “misc_study”: This table contains additional information about different attributes of the study.</p> </li> <li> <p>Table “misc_sample”: This table contains additional information about different attributes of the sample.</p> </li> <li> <p>Table “cmap”: This table contains information about 70895 nodes, compromising drugs, foods or bioactives, overexpressed and knockdown genes (see section 3.4). The information includes cell line, compound and perturbation type.</p> </li> <li> <p>Table “cmap_foodrugs”: This table contains information about the tau score (see section 3.4) that relates food with drugs or genes and the node identifier in the FooDrugs network.</p> </li> <li> <p>Table “topTable”: This table contains information about 150 over and underexpressed genes from each GEO study condition, used to calculate the tau score (see section 3.4). The information stored is the logarithmic fold change, average expression, t-statistic, p-value, adjusted p-value and if the gene is up or downregulated.</p> </li> <li> <p>Table “nodes”: This table stores the information about the identification of the sample and the node in the bipartite network connecting the tables “sample”, “cmap_foodrugs” and “topTable”.</p> </li> </ul> <p>In addition, FooDrugs_V2 database stores a total of 6422 food/drug interactions from 2849 text documents, obtained from three different sources: 2312 documents from PubMed, 285 from DrugBank, and 252 from drugs.com. These documents describe potential interactions between 1464 food/bioactive compounds and 3009 drugs (see below changes in versions V3 and V4). The information is stored in two tables:</p> <ul> <li> <p>Table “texts”: This table contains all the documents with its identifiers where interactions have been identified with strategy described in section 4. </p> </li> <li> <p>Table “TM_interactions”: This table contains information about interaction identifiers, the food and drug entities, and the start and the end positions of the context for the interaction in the document.</p> </li> </ul> <p> </p> <p>FNS-Cloud will overcome fragmentation problems by integrating existing FNS data, which is essential for high-end, pan-European FNS research, addressing FNS, diet, health, and consumer behaviours as well as on sustainable agriculture and the bio-economy. Current fragmented FNS resources not only result in knowledge gaps that inhibit public health and agricultural policy, and the food industry from developing effective solutions, making production sustainable and consumption healthier, but also do not enable exploitation of FNS knowledge for the benefit of European citizens.<br> FNS-Cloud will, through three Demonstrators; Agri-Food, Nutrition & Lifestyle and NCDs & the Microbiome to facilitate:<br> (1) Analyses of regional and country-specific differences in diet including nutrition, (epi)genetics, microbiota, consumer behaviours, culture and lifestyle and their effects on health (obesity, NCDs, ethnic and traditional foods), which are essential for public health and agri-food and health policies;<br> (2) Improved understanding agricultural differences within Europe and what these means in terms of creating a sustainable, resilient food systems for healthy diets; and<br> (3) Clear definitions of boundaries and how these affect the compositions of foods and consumer choices and, ultimately, personal and public health in the future.<br> Long-term sustainability of the FNS-Cloud will be based on Services that have the capacity to link with new resources and enable cross-talk amongst them; access to FNS-Cloud data will be open access, underpinned by FAIR principles (findable, accessible, interoperable and re-useable). FNS-Cloud will work closely with the proposed Food, Nutrition and Health Research Infrastructure (FNHRI) as well as METROFOOD-RI and other existing ESFRI RIs (e.g. ELIXIR, ECRIN) in which several FNS-Cloud Beneficiaries are involved directly. (https://cordis.europa.eu/project/id/863059)</p> <p><strong>***** changes between version FooDrugs_v2 and FooDrugs_V3 (31st January 2023) are:</strong></p> <ul> <li> <p>Increased the amount of text documents by 85.675 from PubMed and ClinicalTrials.gov, and the amount of Text Mining interactions by 168.826.</p> </li> <li> <p>Increased the amount of transcriptomic studies by 32 GEO series.</p> </li> <li> <p>Removed all rows in table <em>cmap_foodrugs</em> representing interactions with values of <em>tau</em>=0</p> </li> <li> <p>Removed 43 GEO series that after manually checking didn't correspond to food compounds.</p> </li> <li> <p>Added a new column to the table <em>texts</em>: <em>citation</em> to hold the citation of the text. </p> </li> <li> <p>Added these columns to the table <em>study</em>: <em>contributor</em> to contain the authors of the study, <em>publication_date</em> to store the date of publication of the study in GEO and <em>pubmed_id</em> to reference the publication associated with the study if any.</p> </li> <li> <p>Added a new column to <em>topTable </em>to hold the top 150 up-regulated and 150 down-regulated genes</p> </li> </ul> <p><strong>***** changes between version FooDrugs_v3 and FooDrugs_V4 (28th July 2023) are:</strong></p> <ul> <li> <p>Increased the amount of text documents by 439.338 from PubMed, ClinicalTrials.gov and DDI corpus (<a href="https://www.sciencedirect.com/science/article/pii/S1532046413001123">Herrero-Zazo et al.</a>), and the amount of Text Mining interactions by 1108429.</p> </li> </ul> <p> </p>
Supplementary information: How robust is the ligand binding transition state?
<p>We have used the REVO weighted ensemble approach followed by Markov state models to identify the ligand unbinding transition states for five ligands unbinding from the enzyme soluble epoxide hydrolase (sEH). This repo provides the <em><strong>counts matrices, properties and state (cluster) labels</strong></em> of the markov state models. The counts matrices can be converted to conformation space networks using CSNAnalysis software (<a href="https://github.com/ADicksonLab/CSNAnalysis">https://github.com/ADicksonLab/CSNAnalysis</a>). The <em><strong>networks</strong></em> are also provided in the gexf formatted files to be visualized in gephi (<a href="https://github.com/gephi/gephi">https://github.com/gephi/gephi</a>).</p>
Data of the INFORMS Journal on Computing paper: Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses
<p>In what follows, you will find data of the paper:<br> "Routing replenishment workers: The prize collecting traveling salesman problem in scattered storage warehouses" published in INFORMS Journal on Computing</p> <p>List of files:<br> - Computational_results_BB_NN_RW_CPLEX.xlsx: Excel file that gives all results<br> - instance_gen.cc: Instance generator<br> - instances.zip: compressed file of all instances that are sorted by Sections. It additionally includes the generator<br> - Makefile: Makefile for compiling/debugging, i.e., "make all" or "make debug" do the jobs<br> - MersenneTwister.h: needed by schedule_finder.cc<br> - results_Section_5_1.zip: compressed file of all output files of Section 5.1<br> - results_Section_5_2.zip: compressed file of all output files of Section 5.2<br> - results_Section_5_3.zip: compressed file of all output files of Section 5.3<br> - schedule_finder.cc: Main program containing the B&B, the S-shape, and Nearest Neighbor procedure (see details for customizing the parameters at the top of this file)<br> - valgrind_debug.txt: Only contains the used debug command</p> <p>instances/instance_gen.cc generates a problem instance in file problems.txt<br> The structure of the these problem files is the following:<br> /*<br> NE Total number of experiments given by the currently considered file<br> -2 Separator<br> EXPGRP Index of the current experiment group the current experiment belong to<br> N Number of vacant positions in the warehouse<br> M Number of requests to be stored by the tour<br> P Number of pickers to be scheduled in the warehouse<br> A Number of vertical aisles<br> B Number of horizontal (cross) aisles<br> L_A Length of each vertical aisle<br> L_B Length of each cross aisle<br> UF_VA Up-factor of each vertical aisle (A values)<br> DF_VA Down- factor of each vertical aisle (A values)<br> UF_CA Up-factor of each cross aisle (B values)<br> DF_CA Down- factor of each cross aisle (B values)<br> x_pos_vertical_aisle x-position of vertical aisle (A values)<br> y_pos_cross_aisle y-position of cross aisle (B values)<br> warehouse_graph values For each node of the warehouse graph all entries (15 each) are given (total_number_of_warehouse_graph_nodes*15)<br> FS << warehouse_graph[curr_node].free_position << " " << endl;<br> FS << warehouse_graph[curr_node].depot_node << " " << endl;<br> FS << warehouse_graph[curr_node].vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].succ_cross_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].succ_vertical_aisle << " " << endl;<br> FS << warehouse_graph[curr_node].pred_cross_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].succ_cross_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].pred_vertical_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].succ_vertical_aisle_dist << " " << endl;<br> FS << warehouse_graph[curr_node].region << " " << endl;<br> FS << warehouse_graph[curr_node].x_position << " " << endl;<br> FS << warehouse_graph[curr_node].y_position << " " << endl;<br> shortest_path_distance For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the distance<br> shortest_path_length_including_start_and_end For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the number of visited nodes<br> shortest_path_visited_nodes For each combination of nodes (i.e., for total_number_of_warehouse_graph_nodes_square combinations) the detailed path (length is respectively given by shortest_path_length_including_start_and_end)<br> dd_free_position For each free position and the depot (here with index N) the due date is transferred (N+1 values) (only relevant for the extended problem, is ignored here)<br> weight_of_free_position For each free position and the depot (here with index N) the weight is transferred (N+1 values) (only relevant for the extended problem, is ignored here)<br> capacity_of_free_position For each free position the storage capacity transferred (N values)<br> -2 Separator indicating the end of an instances<br> -3 Separator indicating the end of all experiments (i.e., indicating the end of the file)<br> */</p> <p>output files (results_Section_5_1.zip/results_Section_5_2.zip/results_Section_5_3.zip):<br> results_BB_NXXX_MYYY_A10_B05: Output file of applying B&B<br> results_RW_NXXX_MYYY_A10_B05: Output file of applying s-shape random walk<br> results_NN_NXXX_MYYY_A10_B05: Output file of applying nearest neighbor</p> <p>In these files you find all outputs of schedule_finder.cc. <br> Among others, you will find the generated tour schedules (for Experiment with index I) in the output files by searching the phrase: "Experiment I completed with result="<br> or for the next Experiment " completed with result="</p> <p>Example (results_BB_N030_M150_A10_B05.txt, experiment 0, the tardiness values are to be ignored, see comments in schedule_finder.cc)<br> Pos 0 depot node with index 80 Number of stored items 0 CT 0 No tardiness<br> Pos 1 position 27 Number of stored items 4 Current accumulated number of stored items 4 CT 163 DD 5629 No additional tardiness<br> Pos 2 position 28 Number of stored items 5 Current accumulated number of stored items 9 CT 399 DD 2962 No additional tardiness<br> Pos 3 position 29 Number of stored items 4 Current accumulated number of stored items 13 CT 670 DD 12631 No additional tardiness<br> Pos 4 position 26 Number of stored items 5 Current accumulated number of stored items 18 CT 840 DD 10142 No additional tardiness<br> Pos 5 position 23 Number of stored items 7 Current accumulated number of stored items 25 CT 1134 DD 6000 No additional tardiness<br> Pos 6 position 22 Number of stored items 4 Current accumulated number of stored items 29 CT 1170 DD 6396 No additional tardiness<br> Pos 7 position 16 Number of stored items 5 Current accumulated number of stored items 34 CT 1448 DD 8962 No additional tardiness<br> Pos 8 position 11 Number of stored items 10 Current accumulated number of stored items 44 CT 1674 DD 6336 No additional tardiness<br> Pos 9 position 0 Number of stored items 10 Current accumulated number of stored items 54 CT 2062 DD 1141 Additional tardiness 921<br> Pos 10 position 2 Number of stored items 9 Current accumulated number of stored items 63 CT 2201 DD 7742 No additional tardiness<br> Pos 11 position 4 Number of stored items 5 Current accumulated number of stored items 68 CT 2407 DD 4846 No additional tardiness<br> Pos 12 position 3 Number of stored items 5 Current accumulated number of stored items 73 CT 2717 DD 2316 Additional tardiness 401<br> Pos 13 position 1 Number of stored items 8 Current accumulated number of stored items 81 CT 2876 DD 9917 No additional tardiness<br> Pos 14 position 6 Number of stored items 3 Current accumulated number of stored items 84 CT 3073 DD 7299 No additional tardiness<br> Pos 15 position 5 Number of stored items 8 Current accumulated number of stored items 92 CT 3144 DD 6152 No additional tardiness<br> Pos 16 position 8 Number of stored items 6 Current accumulated number of stored items 98 CT 3337 DD 3705 No additional tardiness<br> Pos 17 position 12 Number of stored items 9 Current accumulated number of stored items 107 CT 3452 DD 7622 No additional tardiness<br> Pos 18 position 13 Number of stored items 4 Current accumulated number of stored items 111 CT 3522 DD 7833 No additional tardiness<br> Pos 19 position 14 Number of stored items 5 Current accumulated number of stored items 116 CT 3647 DD 9877 No additional tardiness<br> Pos 20 position 19 Number of stored items 1 Current accumulated number of stored items 117 CT 3922 DD 2905 Additional tardiness 1017<br> Pos 21 position 20 Number of stored items 5 Current accumulated number of stored items 122 CT 3923 DD 2538 Additional tardiness 1385<br> Pos 22 position 21 Number of stored items 7 Current accumulated number of stored items 129 CT 3976 DD 2769 Additional tardiness 1207<br> Pos 23 position 18 Number of stored items 10 Current accumulated number of stored items 139 CT 4173 DD 3552 Additional tardiness 621<br> Pos 24 position 25 Number of stored items 4 Current accumulated number of stored items 143 CT 4482 DD 11710 No additional tardiness<br> Pos 25 position 24 Number of stored items 7 Current accumulated number of stored items 150 CT 4509 DD 10599 No additional tardiness<br> Pos 26 visiting the node with index 80 Number of stored items 0 CT 4710 DD 10893 No additional tardiness<br> opt_makespan=4710 opt_total_tardiness=5552<br> TSP_procedure returned value 4710<br> Experiment 0 completed with result=3<br> BFS Branch&Bound report: Consumed time: 1</p> <p>Copied from schedule_finder.cc:<br> Note that the procedure used as a solution procedure in the paper is int TSP_procedure(struct bb_node *curr_bb_node, int version)</p> <p>It is called by BB_procedure() as a subroutine for computing a lower bound value of an extended problem<br> (for instance, this extended problem additionally covers due dates. Therefore, due dates are also part of the problem instances, but can be ignored)<br> Specifically, TSP_procedure(struct bb_node *curr_bb_node, int version) is called once by lb_computation()</p>
TrainTicket microservice testbench extracted information for our work: Evaluating ChatGPT's Proficiency in Understanding and Answering Microservice Architecture Queries Using Source Code Insights
<p>It contains the CSV file output of our tool implemented in the paper: "Evaluating ChatGPT’s Proficiency in Understanding and Answering Microservice Architecture Queries Using Source Code Insights." applied to the TrainTicket microservice testbench. The information in this CSV was used for In-Context-Learning for ChatGPT.</p>
Gold standard annotation of epilepsy clinic letters for the development of information extraction tools
<p>This folder contains 200 synthetic letters, based on hospital outpatient epilepsy clinic consultations, written by neurology consultants, specialist trainees, and epilepsy specialist nurses. The letters were double annotated by trained researchers, according to annotation guidelines, uploaded separately (What and How of annotating with Markup). The 200 .ann annotation files are also included.</p> <p>We used Markup (https://getmarkup.com) for annotation (the configuration file is within the uploaded set of annotations) with an epilepsy concept list based on the Unified Medical Language System (UMLS) ontology. All annotations were compared, reviewed, and corrected to form a gold standard annotation set.</p> <p> </p>
Known-fate survival information for radio-tagged snowshoe hares captured in Bonanza Creek Experimental Forest from June 2008 to November 2012
This dataset contains known-fate survival information for radio-tagged snowshoe hares captured in two 200 x 450 m live-trapping grids in Bonanza Creek Experimental Forest from June 2008 to November 2012. The data can be sorted and viewed by year, site, number at risk, and number of mortalities.
Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences X - Research Project Site Information 2014
This data set was collected as a part of Brian Houseman's MS Thesis, Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences (December 2017). Data include research site location information. Data were collected on study plots established across two burn scars (2004 Boundary Fire and 1971 Wickersham Dome Fire) within the Yukon-Tanana Uplands ecoregion of interior Alaska.
Measurements from CalCOFI cruises in the California Current System, including log of station information, weather, sea conditions as well as physical, chemical and biological measurements including including temperature, salinity, oxygen, density, sigma theta, phosphate, silicate, nitrite, nitrate, ammonia, chlorophyll a, integrated chlorophyll a, primary productivity, and integrated primary production. 1949 - January 2020
Since 1949, hydrographic and biological data of the California Current System have been collected on quarterly CalCOFI cruises. The 59+ year hydrographic time-series includes weather, temperature, salinity, oxygen and phosphate observations. In 1961, nutrient analysis expanded to include silicate, nitrate and nitrite; in 1973, chlorophyll was added; in 1984, C14 primary productivity incubations were added. These data are being provided here in collaboration with CalCOFI-SIO in order to provide an additional queriable interface to the data. The data are updated on a regular basis from the CalCOFI hydrographic database.
North Temperate Lakes-LTER Core Research Lakes Information
Lake information for our eleven core NTL-LTER study lakes. These include seven in the Trout Lake area (Allequash, Big Muskellunge, Crystal Lake, Crystal Bog, Sparkling, Trout Bog and Trout Lake), and four lakes in the Madison area (Mendota, Monona, Wingra, and Fish). Data includes lake identifiers for various water-body databases, geographic location, general lake characteristics, watershed and shoreline land cover descriptions, and long-term averages of select water quality measurements.
PIE LTER geographic information regarding vegetation transects set up at the Argilla Rd. Salt marsh restoration site in Ipswich and a reference marsh (Rough Meadows) in Rowley, Massachusetts.
A description of the vegetation transects set up at the Argilla Rd. Salt marsh restoration site in Ipswich, MA and a reference marsh (Rough Meadows) in Rowley, MA.
Descriptive data file for information regarding microbial genetic research in the environs of Plum Island Sound watersheds, PIE LTER, Massachusetts.
This is a descriptive, tabular dataset of publications related to microbial or genomic research conducted within PIE. Assession numbers for genetic sequences generated from PIE samples are provided where available, followed by a very brief description of analysis type and study objectives. Sampling locations within PIE, sampling dates, and habitat type (sea water, fresh water, sediment, marsh) are also given. Environmental data are included in some publications and are listed here (if brief) or availability is described. Links to sequence archives are given in Methods.
Agreeableness personality trait and social information encoding
Open the record for dataset details and reuse information.
Hydrodynamic and morphological information, and the absolute variations of the vulnerability indices for the period 2000-2015 of the Spanish Iberia Peninsula estuaries.
<p>The dataset included in this repository was obtained during the project entitled 'Sensibilidad física y biotic de los estuarios peninsulares al cambio global (SENSES)' funded by 'Fundación Biodiversidad', PRCV00487. The data were used in the research article 'Sensitivity of Iberian estuaries to changes in sea water temperature, salinity, river-flow, mean sea level, and tidal amplitudes' submitted to <em>Estuarine, Coastal and Shelf Science</em>.</p> <p>Brief description of dataset:</p> <p>For each estuary, the following parameters were calculated</p> <ul> <li>Fachade: the location of the estuary</li> <li>Area (km<sup>2</sup>) </li> <li>D (m): water depth at the mouth of the estuary in 2000 and 2015</li> <li>Tidal Prim (m<sup>3</sup>)</li> <li>Q<sub><em>f</em></sub> (m<sup>3</sup>/s): river flow in 2000 and 2015</li> <li><em>a </em>(m): tidal amplitude of the free surface elevation in 2000 and 2015</li> <li>∆<em>U</em> (m/s): absolute variation of the tidal current amplitude between 2000 and 2015</li> <li>∆<em>E</em> (W/m<sup>2</sup>): absolute variation of the tidal energy flux propagation index between 2000 and 2015</li> <li>∆<em>Ri </em>: absolute variation of the bulk Richardson number index between 2000 and 2015</li> <li>∆<em>SI</em>: absolute variation of the salinity intrusion index between 2000 and 2015</li> </ul> <p>A wide description of the parameters can be found in Serrano, M. A. et al (submitted to <em>Estuarine, Coastal and Shelf Science</em>)</p> <p>Contact person: mserranog@ugr.es</p>
Dataset: Knowledge, information needs and behavior regarding HIV and sexually transmitted infections among migrants from sub-Saharan Africa living in Germany: Results of a participatory health research survey.
<p>Dataset for: Koschollek C, Kuehne A, Müllerschön J, Amoah S, Batemona-Abeke H, Dela Bursi T, Mayamba P, Thorlie A, Mputu Tshibadi C, Wangare Greiner V, Bremer V, Santos-Hövener C: Knowledge, information needs and behavior regarding HIV and sexually transmitted infections among migrants from sub-Saharan Africa living in Germany: Results of a participatory health research survey.</p> <p>This dataset has been described in a PLoS One paper and contains all data necessary to replicate the results presented within this paper (10.1371/journal.pone.0227178). Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
Information Needs in Contemporary Code Review - Appendix
<p>Contemporary code review is a widespread practice used by software engineers to maintain high software quality and share project knowledge. However, conducting proper code review takes time and developers often have limited time for review. In this paper, we aim at investigating the information that reviewers need to conduct a proper code review, to better understand this process and how research and tool support can make developers become more effective and efficient reviewers. Previous work has provided evidence that a successful code review process is one in which reviewers and authors actively participate and collaborate. In these cases, the threads of discussions that are saved by code review tools are a precious source of information that can be later exploited for research and practice. In this paper, we focus on this source of information as a way to gather reliable data on the aforementioned reviewers’ needs. We manually analyze 900 code review comments from three large open-source projects and organize them in categories by means of a card sort. Our results highlight the presence of seven high-level information needs, such as knowing the uses of methods and variables declared/modified in the code under review. Based on these results we suggest ways in which future code review tools can better support collaboration and the reviewing task. Appendix material.</p>
Model outputs for validation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar, January 2020 update
<p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results.</p> <p>Objectives:</p> <p>- Validation of previously produced hunting yield maps and new ones<br> - Downscaling to 10x10 km grid >>> file "January_2020_HY_nut01_10x10.tif"<br> - Downscaling to 2x2 km grid >>> file "January_2020_HY_nut00_2x2.tif"</p> <p><br> Model settings and predictors: <br> - Assuming cells as municipality in 10x10 km grid downscaling<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling </p> <p>Conclusions guiding future methodological steps:<br> - To update wild boar hunting yield data for some specific regions<br> - To increase hunting yield data resolution<br> - To explore model independent parametrization for each bioregion</p> <p>For further details and methodological approach see the paper:</p> <p>ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A. Cohen, R. Soriguer, J. Vicente (2020) Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar. EFSA supporting publication 2020:EN-1787. 23pp. doi:10.2903/sp.efsa.2020.EN-1787.</p> <p>Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.<br> </p>
Searching as Information Literacy Unpacking the ACRL Frame of Searching As Strategic Exploration
<p>Searching as Information Literacy Unpacking the ACRL Frame of Searching As Strategic Exploration is an OER that includes a podcast, blog and associated exercise. Three University of Ottawa librarians are interviewed on search challenges they have encountered and their proposed search strategies as it relates to the ACRL framework: Searching as Strategic Exploration. Although it is possible to present an analysis of search strategies solely in writing or in traditional in-personal presentations, newer media such as podcasts allow for innovative delivery of the material in a more immediate and visceral way. That is, the audience is able to hear and receive the information directly from the experts themselves, in their own words. A podcast is also able to convey the aural nuance of a given speaker in a way that conventional scholarly approaches can’t quite duplicate. We believe that a podcast with real librarians’ voices speaking to and guiding students about exploratory searching, using recent, real-life examples, represents a dynamic, useful, engaging and relevant educational tool for our target audience - MLIS, MIS students. </p> <p> </p> <p>Podcast can be found on Soundcloud: <a href="https://soundcloud.com/somesoundsbylina/searching-as-information">https://soundcloud.com/somesoundsbylina/searching-as-information</a></p> <p>Access to the blog: <a href="https://esisuottawa.wixsite.com/esisinfolit/post/searching-as-information-literacy-unpacking-the-acrl-frame-of-searching-as-strategic-exploration">https://esisuottawa.wixsite.com/esisinfolit/post/searching-as-information-literacy-unpacking-the-acrl-frame-of-searching-as-strategic-exploration</a> </p>
CRISPR locus information of M. parvicella in Martinez Arbas, Narayanasamy et. al. (2020)
<p>Comparative CRISPR locus analyses of <em>Candidatus</em> Microthrix parvicella Bio17-1 isolate genome and the contig containing <em>M. parvicella</em> -like CRISPR locus (D47_L1.43.1_contig_476300). We used the online tools of CRISPRCasFinder and NCBI Nucleotide BLAST.</p> <p>This repository is related to the work published in Martinez Arbas, Narayanasamy et. al. (2020).</p>
An information theory-based machine learning approach to detecting functionally conserved and coordinated protein dynamics
<p>The application of machine learning classification to the molecular dynamics of the functional states of protein allows for application of an information theoretic framework familiar to traditional bioinformatics. The functional states of proteins involving binding interactions with partners comprised of protein, DNA or small molecules can first be defined in a binary fashion (i.e. bound vs unbound), subsequently simulated in molecular dynamics software, and then employed as a comparative training set for a binary machine learning classifier capable of discerning the complex dynamical consequences of binding interaction. This learner can subsequently be deployed on new simulations of the functionally bound state to validate its ability to recognize the molecular motions that are supporting binding function. Regions of proteins with functionally conserved dynamics will induce significant local correlations in learning performance across independent validation runs. Through case studies of Rbp subunit 4/7 interaction in RNA Pol II and DNA-protein interactions of TATA binding protein, we demonstrate this method of detecting functionally conserved protein dynamics. We also demonstrate how Shannon information, relative entropy and mutual information can be applied to these binary classification states of dynamic simulations in order to compare dynamics and identify concerted motions involved in dynamic interactions across sites.</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.