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54 results for “Apache”
Effect of Habitat Restoration on Native Bee Communities at the Bosque del Apache National Wildlife Refuge, New Mexico (2008-2010)
Native bees interact closely with their host plants and therefore, can be good indicators of habitat diversity and health. Due to the physical aspects of tamarisk removal, reintroduction of native vegetation (through seeding or natural recolonization) is a process that can take years to stabilize. The soil has been greatly disturbed and the recruitment of reproductively mature plants and therefore, the creation of a healthy seed bank takes time. Many types of adult insects, such as bees, wasps, flies, and beetles, use nectar as a source of energy. However bees dont just drink nectar, they also use the pollen as a source of nutrition on which to rear their offspring. Tamarisk is an incredibly good source of nectar, but is a lousy source of pollen. Since it is wind pollinated, the pollen grains are small and not very nutritious. Insect pollinated plants on the other hand provide both nectar and nutritious pollen. Many of these plants also have very tight relationships with their respective bees, in which case one species of bee may only pollinate a single genus of plant. Bees can be used to monitor the recovery of areas where tamarisk has been removed. The diversity of bees can vary greatly temporally and spatially. Bee traps are a low maintenance and low time commitment way of monitoring bees in xeric systems.
HORUS pressure dependent calibrations using the APACHE calibrator
<p>Dataset for the pressure dependent calibrations of HORUS using the newly developed All Pressure Altitude Calibrator for HOx Experimentation (APACHE) chamber.</p>
Scalability Benchmarking of Apache Flink
<p>Results from scalability benchmarking of Apache Flink using the Theodolite benchmarking method.</p> <p>The dataset includes the raw record lag data, numbers of instances and partitions, as well as the minimum required instances that we determined. The resulting scalability graphs are provided as well.</p>
Characterizing Distributed Machine Learning Workloads on Apache Spark
<p>This dataset was used for our submission at Middleware'22 titled: "Characterizing Distributed ML Workloads"</p> <p>It will contains the description and the raw data, its format, as well as a detailed description of the cluster deployments used by these experiments.<br> </p> <p>The full paper is available here:</p> <p>https://dl.acm.org/doi/10.1145/3590140.3629112</p>
Apache Software Foundation Incubator Project Sustainability Dataset
<p>The data set contains ASF incubator projects data (updated in June 2021).</p>
Subspecies and Distribution. M.a.auriculusBR.H.Baker&Stains,1955-EsideofSierraMadreOrientalfromNuevoLeonintoTamaulipasandVeracruz(Mexico). M. a. apache Hoffmeister & Krutzsch, 1955 — from the mountains of SE Arizona and SW New Mexico (USA) S to Jalisco (Mexico); also in N Coahuila (Mexico). There is a record from Guatemala, but geographic discontinuity and absence of new records in almost 40 years indicates need for taxonomic revision ofthis specimen. in Vespertilionidae
Subspecies and Distribution. M.a.auriculusBR.H.Baker&Stains,1955-EsideofSierraMadreOrientalfromNuevoLeonintoTamaulipasandVeracruz(Mexico). M. a. apache Hoffmeister & Krutzsch, 1955 — from the mountains of SE Arizona and SW New Mexico (USA) S to Jalisco (Mexico); also in N Coahuila (Mexico). There is a record from Guatemala, but geographic discontinuity and absence of new records in almost 40 years indicates need for taxonomic revision ofthis specimen.
Apache POI pre-processed data for the first DocGen challenge at DySDoc 3
<p>Apache POI pre-processed data for the first DocGen challenge</p> <p>The pre-processed data for First Software Documentation Generation Challenge (DocGen), hosted at the <a href="https://dysdoc.github.io/">Third International Workshop on Dynamic Software Documentation (DySDoc 3)</a>, includes the following datasets for <a href="https://poi.apache.org/">Apache POI 3.17</a>:</p> <p><strong>Call graph between method and classes.</strong></p> <p>File: call-graph-poi-3.17-all.zip</p> <p>CSV file with the call graph between methods and between classes. Class A calls class B if there exists a call between amethod of class A and a method of class B. The call graph was produced by the tool <a href="https://github.com/gousiosg/java-callgraph/">java-callgraph</a>. </p> <p>The CSV file contains the following columns:</p> <ul> <li>call_type: call between (C)lasses or (M)ethods</li> <li>caller: the Fully Qualified Name (FQN) of the caller</li> <li>method_call_type: the type of method call: <ul> <li>M for invokevirtual calls</li> <li>I for invokeinterface calls</li> <li>O for invokespecial calls</li> <li>S for invokestatic calls</li> <li>D for invokedynamic calls</li> </ul> </li> <li>callee: the FQN of the callee</li> </ul> <p>For more details about the format and each type of method call, check the tool <a href="https://github.com/gousiosg/java-callgraph/">README</a>.</p> <p><strong>Inheritance hierarchy</strong></p> <p>File: poi-3.17-inheritance.zip</p> <p>A CSV file with the inheritance hierarchy of POI, which was extracted using bcel 6.2</p> <p>The CSV file contains the following columns:</p> <ul> <li>record_id: sequential number</li> <li>parent_class: the parent class</li> <li>child_class: the child class</li> <li>relationship_type: the type of relationship between classes, i.e., the child class 'extends' or 'implements' the parent class</li> </ul> <p><strong>Issues</strong></p> <p>File: bugzilla-poi-dump.zip</p> <p>CSV file with the list of issues of <a href="https://bz.apache.org/bugzilla/buglist.cgi?product=POI">Apache POI</a> (timestamp: Tue Feb 27, 2018, 18.41.40 UTC)</p> <p>The CSV file contains the following columns:</p> <ul> <li>record_id: sequential number</li> <li>issue_id: the ID that identifies the issue in the issue tracker</li> <li>issue_url: the URL of the issue in the issue tracker</li> <li>issue_title: the title of the issue</li> <li>xml_path: the path to the XML of the issue, which contains all the issue information provided by the issue tracker</li> </ul> <p>All the issues in XML format can be found in the "poi" folder in the ZIP file</p> <p><strong>Commits</strong></p> <p>File: poi-commits.zip</p> <p>A JSON file with commit information for POI 3.17 (until revision 219dff00e6, on Sept. 8, 2017). The information was extracted using the tools <a href="https://dl.acm.org/citation.cfm?doid=2024445.2024463">Historage</a> and <a href="https://dl.acm.org/citation.cfm?doid=2597073.2597125">Kataribe</a>.</p> <p>For each commit, we provide:</p> <ul> <li>Commit hash</li> <li>Parent commit hash (if exists)</li> <li>Commit message</li> <li>Commit time</li> <li>Committer name</li> <li>Method-level changes (addition/deletion/modification/renaming and method FQN). <ul> <li>The FQN contains information about the class (CN) and method (MT) or constructor (CS)</li> </ul> </li> </ul> <p><strong>StackOverflow posts</strong></p> <p>File: apache-poi-SO.zip</p> <p>JSON file with all 6,299 Stack Overflow threads with the <code>apache-poi</code> tag,</p>
Apache Point Observatory DIS Survey of the Io Plasma Torus
<p>Archive of DIS observations of the Io Plasma Torus 2013-2017, including standard calibration files </p>
FIGURES 21–25. Leptohyphes apache, larval structures. 21 in Revision of the genus Leptohyphes Eaton (Ephemeroptera: Leptohyphidae) in North and Central America
FIGURES 21–25. Leptohyphes apache, larval structures. 21, head and prothorax, [scale bar B]. 22, foreleg [D]. 23, hindleg [E]. 24, hind claw (ventro-lateral) [C]. 25, abdominal tergites 7-10 [A]. Scale bars (mm): A, B = 0.05; C = 0.1; D = 0.5; E = 0.4.
APACHE Cohort (A Psoriatic Arthritis CoHort)
ClinicalTrials.gov study NCT03768271. IPD Sharing: NO. Countries: 1. Publications: 0.
Predictive Value of SOFA and APACHE Scores for In-hospital Mortality in COVID-19 ICU Patients
ClinicalTrials.gov study NCT04713852. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Apache Point Observatory ARCES Spectra of Io-in-eclipse (2018-2019)
<p>APO/ARCES R~31,500 optical and near-IR spectra of Io while eclipsed by Jupiter on UT180320 and UT190812. This includes guider images (ecam directory), raw spectra, and spectra reduced using two independent pipelines with IRAF/PyRAF scripts included. Reduction via PyRAF script, echelleReduction_Aug2019.py, is courtesy of Jean McKeever (Yale). The ReduceARCES_CS.cl IRAF script comes courtesy of Brett Morris (University of Bern) and produces the initial files for his own Python package AESOP (DOI:10.21105/joss.00854). </p>
Models, Simulations, Measurements, and Analysis for Modeling and Simulating Apache Spark Streaming Applications
<p>Palladio component models, simulations results, measurement data, and R analysis script for the publication: Modeling and Simulating Apache Spark Streaming Applications.</p> <p>Symposium on Software Performance (SSP16)</p>
Computational Artifacts for Performance Feedback Autoscaling Experiments with Workloads of Workflows in Apache Airflow
<p>These computational artifacts are related to the software artifacts DOI:10.5281/zenodo.2635571</p> <p><strong>The content of the computational artifacts:</strong></p> <ul> <li><strong>experiments.pdf</strong> contains the list of all the conducted experiments with the Airflow system. Experiment IDs are not sequential since some experiments required rerunning, etc., we report only successful results. The file lists different experiment configurations, e.g., the number of processed workflows, the name of the used workload, the user budgets, and PFA settings.</li> <li><strong>db.tar.gz</strong> contains directories with Airflow database snapshots and autoscaler logs. The names of the directories correspond to those listed in `experiments.pdf`. Each experiment directory contains an autoscaler log and a full copy of a PostgreSQL database directory just after each experiment finished. The database name is `airflow`, the user name is `ailyushk`. Within each database, most of the paper-related data are stored in the `stat_log` table. The scripts for extracting data from these databases are available as software artifacts in `tools/analysis`.</li> <li><strong>gurobi.tar.gz</strong> contains the results obtained from the Gurobi solver when solving the MIP model.</li> <li><strong>pdf.tar.gz</strong> contains all the figures in pdf format, also those that were not included neither in the paper nor in the technical report. The scripts for creating this plots are delivered as software artifacts.</li> <li><strong>csv.tar.gz</strong> contains the analysis results extracted from Airflow database snapshots. These files are used to create the plots in the `pdf` directory. The scripts for doing this are delivered as software artifacts.</li> <li><strong>wl1.tar.gz</strong> is the first synthetic realistic workload (WL I) with three subsets of 200 workflows each (`1_0`, `1_1`, `1_2`). Each directory contains the file with interarrivals `interarrivals.txt`, and the file with workflow IDs `workload.txt` in the subset. The `dags` directory contains Python-based Airflow descriptors and CSV files that summarise the same descriptors in CSV format for simpler analysis. The scripts for extracting workload statistics from these CSV files are available in the software artifacts in `tools/analysis`. The `inputs` directory contains initial input files for each worfklow. The `dax` contains original DAX files obtained from the generator: <a href="https://github.com/pegasus-isi/WorkflowGenerator/tree/master/bharathi/src/simulation/generator">https://github.com/pegasus-isi/WorkflowGenerator/tree/master/bharathi/src/simulation/generator</a></li> <li><strong>wl2.tar.gz</strong> is the second synthetic realistic workload (WL II) with three subsets of 200 workflows each (`4_0`, `4_1`, `4_2`). Has similar structure as `wl1.tar.gz`, except that `dax` directory is omitted, as WL II uses the same DAX structures as WL I.</li> <li><strong>wl3.tar.gz</strong> is the small synthetic workload based on WL I for the experiment with the MIP solver, contains three subsets with 5 workflows in each, all in the `3_0` directory (thus, the structure differs from the WL I and WL II). The input data files are empty. The identifiers of workflows forming each subset are stored in the `workload_1.txt`, `workload_2.txt`, and `workload_3.txt` files.</li> </ul>
The state of Diversity and Inclusion in Apache: A Pulse Check (survey)
<p>The state of Diversity and Inclusion in Apache: A Pulse Check (survey)</p>
The state of Diversity and Inclusion in Apache: A Pulse Check
<p>The state of Diversity and Inclusion in Apache: A Pulse Check --- Supplementary materials</p>
Are automated static analysis tools worth it? An investigation into relative warning density and external software quality on the example of Apache open source projects
<p>This is the dataset for the publication "Are automated static analysis tools worth it? An investigation into relative warning density and external software quality on the example of Apache open source projects".</p> <p>It contains just-in-time defect-prediction style data in jit_data2.tar.gz as well as warnings generated by PMD 6.31.0 in warnings_data3.tar.gz.</p> <p>Further information and scripts for generating the plots and tables can be found in the <a href="https://github.com/atrautsch/emse2021a_replication">replication kit</a>.</p>
A Comparative Evaluation of Nottingham Hip Fracture Score (NHFS) and Acute Physiology and Chronic Health Evaluation (APACHE) II Score to Predict 30-day Mortality in Geriatric Patients Above 60 Years U
ClinicalTrials.gov study NCT06598566. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Apache Water Tus High Res
Apache Water Tus at the Hutchings Museum Institute. 172 images, Canon EOS 80D, 35mm, F/16, ISO 100, Agisoft Metashape, Windows 10. Source: Objaverse 1.0 / Sketchfab
The New ICU Prognostic Score APACHE-INf
ClinicalTrials.gov study NCT05955547. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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