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1,454 results for “large scale”
R code: Large-scale assessment of bird data quality in a citizen science platform
<p>R code archived to Zenodo for publication</p>
Data from: Opportunities for understanding MS mechanisms and progression with MRI using large‐scale data‐sharing and artificial intelligence
<p>This is supplementary material accompanying the paper "Opportunities for understanding MS mechanisms and progression with MRI using large‐scale data‐sharing and artificial intelligence"</p>
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis
<p>Logic synthesis is a challenging and widely-researched combinatorial optimization problem during integrated circuit (IC) design. It transforms a high-level description of hardware in a programming language like Verilog into an optimized digital circuit netlist, a network of interconnected Boolean logic gates, that implements the function. Spurred by the success of ML in solving combinatorial and graph problems in other domains, there is growing interest in the design of ML-guided logic synthesis tools. Yet, there are no standard datasets or prototypical learning tasks defined for this problem domain. Here, we describe OpenABC-D,a large-scale, labeled dataset produced by synthesizing open source designs with a leading open-source logic synthesis tool and illustrate its use in developing, evaluating and benchmarking ML-guided logic synthesis. OpenABC-D has intermediate and final outputs in the form of 870,000 And-Inverter-Graphs (AIGs) produced from 1500 synthesis runs plus labels such as the optimized node counts, and de-lay. We define a generic learning problem on this dataset and benchmark existing solutions for it. The codes related to dataset creation and benchmark models are available athttps://github.com/NYU-MLDA/OpenABC.git.</p>
Household Survey - Impacts of large-scale land acquisitions on smallholder agriculture and livelihoods in Tanzania
<p><strong>** Article & Dataset Currently Under Review **</strong></p> <p><strong>Dataset Overview</strong></p> <p>Our household dataset is associated with a pre-print article "Impacts of large-scale land acquisitions on smallholder agriculture and livelihoods in Tanzania". The household survey is designed for the purposes of policy evaluation with selection of households based on proximity to large-scale land acquisitions (treatment) and a set of households in similar socio-ecological contexts with no association to large-scale land acquisitions (control). Households were selected as a random sample in 35 villages surrounding LSLAs who provided responses to a questionnaire covering household income, assets, farming practices, health, food-security, and energy-use. </p> <p>Two datasets are provided. First, the "hh_dataset_rep.csv" providing household responses for variables used in this study. Second, the "hh_crops_rep.csv" provides detail on crops cultivated by each household, self-reported yields and farm-gate prices. Each variable is described in the "variable_descriptoin.xlsx". In addition to the datasets, we provide replication code for this study "lsla_mechanisms_rep.Rmd" as an R-Markdown file.</p> <p><strong>Article Abstract</strong></p> <p>Improving agricultural productivity is a major sustainability challenge of the 21<sup>st</sup> century. Large-scale land acquisitions (LSLAs) have important effects on both well-being and the environment in the Global South, but their impacts on agricultural productivity and subsequent effects on farm incomes or food-security are under-investigated. Prior studies lack data or methods to investigate the mechanistic nature of household change in agricultural practices that may vary due to LSLA conditions. To overcome this challenge, we use a novel household dataset and a quasi-experimental design to estimate household level changes in agricultural value driven by LSLAs in Tanzania. In addition, we use a causal mediation analysis to assess how contract farming arrangements, land loss, and adoption of new farming technologies around LSLAs influence agricultural productivity. We find that households near LSLAs produced 19.2% (95% CI: 3.5 – 37.2%) higher agricultural value, primarily due to increased crop prices and farmer selection of high-value crops. Importantly, effect sizes are positively and negatively mediated by different mechanisms. The presence of contract farming explains 18.1% (95% CI: 0.56%, 47%) of the effect size in agricultural value, whereas land loss reduces agricultural value by 26.8% (95% CI: -71.3%, -4.0%). We also estimate whether improvements in food-security and household incomes occur in proximity to LSLAs, as anticipated with higher agricultural value. However, we do not find increases in agricultural income and food security, which may be due to higher crop prices in proximity to LSLAs. Our results stand in contrast to assumptions that technological spillovers occur through LSLAs and are principal drivers of agrarian change, holding important implications for agricultural transformations. Instead access to output markets through contract farming enables greater agricultural value whereas land loss negatively affects the agricultural value of households. Governance strategies should focus on limiting negative impacts related to the loss of smallholder land rights enabling greater access to contract farming.</p> <p> </p>
An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China
<p>An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China</p>
A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID
<p>Aviation flight records and maintenance labels. </p>
Realistic Large-Scale Fine-Depth Dehazing Dataset from 3D Videos
<p>Dehaze Dataset LSFD</p>
An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China
<p>An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China.</p>
Data from: Effects of abiotic environmental factors and land use on the diversity of carrion-visiting silphid beetles (Coleoptera: Silphidae): a large scale carrion study
[No abstract entered]
Source and result datasets for ""Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS"
<p>These files are the LRZip [0] compressed datasets used in the research paper 'Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS' [1]. The code can be found on Github [2].</p> <p>ls 2021-12-22-10\:49\:40<br> total 21M<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:38 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 7.7M May 25 12:10 domainfile.log.new.gz<br> -rw-r--r-- 1 sneef sneef 17K May 24 20:53 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 13M May 24 20:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 125 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 27K May 24 20:52 server.log.new.gz</p> <p>ls 2021-12-22-15\:04\:21<br> total 11G<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:43 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 4.2G May 23 12:43 certstream.log.new.gz<br> -rw-r--r-- 1 sneef sneef 3.3M May 23 12:37 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 6.0G May 23 12:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 104 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 6.3M May 23 12:37 server.log.new.gz</p> <p><br> ls results_certstream<br> total 17G<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 19:26 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 22:54 certstream_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 22:39 certstream_analysis_skipped_domains.txt<br> -rw-r--r-- 1 sneef sneef 4.5G May 23 13:21 certstream_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 450 May 23 13:22 certstream_counted_unique_domains_otherlines.csv<br> -rw-r--r-- 1 sneef sneef 602M May 23 13:22 certstream_counted_unique_domains_skipped_domains.csv<br> -rw-r--r-- 1 sneef sneef 3.6G May 23 13:22 certstream_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 6.1K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 39K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt_errors.txt<br> -rw-r--r-- 1 sneef sneef 3.1M Jun 8 19:20 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 94 Jun 8 19:26 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 37K May 23 13:41 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 45M May 23 13:41 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 22M May 23 13:41 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 2.1K Jun 8 23:09 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 23:09 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 1.4K Jun 8 23:09 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 23:08 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 2.6K May 23 13:41 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 76M May 23 13:41 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 206M May 23 13:41 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 3.4G May 23 13:41 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 7.0M May 23 13:41 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 423K May 23 13:41 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 3.9G May 23 13:40 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 6.0K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 259 Jun 8 00:51 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 305K Jun 8 00:24 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 1.4M May 23 22:41 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 63K May 23 22:41 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 74 May 23 22:41 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 9.1K May 23 22:41 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 239 May 23 22:41 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.7M May 23 22:41 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 1.7K May 23 22:41 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 17K May 23 22:41 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 79 May 23 22:41 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 70M May 23 22:41 serverlog_structured_data_structued_data.json</p> <p> </p> <p>ls results_tranco1m<br> total 79M<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 17:03 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 850 Jun 8 22:38 domainfile_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 domainfile_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 19M May 25 12:28 domainfile_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 4.0K May 27 03:03 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 16K Jun 8 16:32 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 83 Jun 8 17:04 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 226 May 25 12:28 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 66K May 25 12:28 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 31K May 25 12:28 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 0 Jun 8 22:38 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 900 Jun 8 22:38 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 915 Jun 8 22:38 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 909 Jun 8 22:38 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 844 Jun 8 22:38 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 829 Jun 8 22:38 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 27 May 25 12:28 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 589K May 25 12:28 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 269K May 25 12:28 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 18M May 25 12:28 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 58K May 25 12:28 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.8K May 25 12:28 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 4.4K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 241 Jun 8 11:45 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 5.4K Jun 7 23:58 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 2.0K Jun 7 15:57 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 69 Jun 7 15:57 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.5K Jun 7 15:57 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 129K Jun 7 15:57 serverlog_structured_data_structued_data.json<code> </code></p> <p><br> <br> [0] https://github.com/ckolivas/lrzip<br> [1] TBD<br> [2] https://github.com/gehaxelt/sshfp-dns-measurement</p>
"A Lot Takes Place Digitally Now, so It Can Be Good to Train on It'': A Large-Scale Repeated Cross-Sectional Study on Recording Live-Streamed Educational Activities among Health, Social, and Education Students
<p>Research article</p>
Text-fig. 4. SRXTM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Transverse (a) and longitudinal (b) sections of flower showing the ovary wall (ow) and numerous ovules (ov) borne on the mushroom-shaped central globose placenta (pl, blue); placenta shown as a voltex rendering added to orthoslices; note the large openings in the floral tissue (asterisks) interpreted as schizogenous secretory cavities. c, d: Longitudinal (c) and transverse (d) sections of flower (c, volume rendering cut between orthoslice yz0970-1005; d, volume rendering cut between orthoslice xy0780-0820) showing semiinferior ovary (ow, ovary wall) with sepals inserted at the rim of the hypanthium, central column (cc) with mushroom-shaped globose placenta (pl) bearing numerous ovules (ov). Specimens, Mira 100-S153146 (a, b), Mira 100-S170155 (c, d, holotype). Scale bars = 600 µm (a–d). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 4. SRXTM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Transverse (a) and longitudinal (b) sections of flower showing the ovary wall (ow) and numerous ovules (ov) borne on the mushroom-shaped central globose placenta (pl, blue); placenta shown as a voltex rendering added to orthoslices; note the large openings in the floral tissue (asterisks) interpreted as schizogenous secretory cavities. c, d: Longitudinal (c) and transverse (d) sections of flower (c, volume rendering cut between orthoslice yz0970-1005; d, volume rendering cut between orthoslice xy0780-0820) showing semiinferior ovary (ow, ovary wall) with sepals inserted at the rim of the hypanthium, central column (cc) with mushroom-shaped globose placenta (pl) bearing numerous ovules (ov). Specimens, Mira 100-S153146 (a, b), Mira 100-S170155 (c, d, holotype). Scale bars = 600 µm (a–d).
Optimal Experimental Design for Large-Scale Inverse Problems via Multi-PDE-constrained Optimization - supplementary files
<p>The file data.xlsx contains information about four experiments defined in: A. Petrocchi, M.K. Scharrer, F. Pichler, S. Volkwein, Optimal Experimental Design for Large-Scale Inverse Problems via Multi-PDE-constrained Optimization, 2024, Submitted. Preprint available at https://arxiv.org/abs/2404.15797.</p> <p>Some information is included in the file data.pdf.</p>
Uncertainties Inherent from Large-Scale Climate Projections in the Statistical Downscaling Projection of North Atlantic Tropical Cyclone Activity
Open the record for dataset details and reuse information.
Do Developers Use Static Application Security Testing (SAST) Tools Straight Out of the Box? A large-scale Empirical Study
Open the record for dataset details and reuse information.
Dataset belonging to "The Influence of Large-scale Spatial Warming on Jet Stream Extreme Waviness on an Aquaplanet"
Open the record for dataset details and reuse information.
Supplementary material for "A large-scale demonstration and sustainability evaluation of ductile-porous vascular networks for self-healing concrete"
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welborn_24_streaming_large_scale_electron_microscopy_data_si_videos
Open the record for dataset details and reuse information.
Figure 1 in Wooden sticks for plaque streaking and microbiological inoculation might be more cost- effective, but is its large scale use feasible? Quality control methods and proof of concept
Figure 1. (a) wooden sticks in the steam autoclave sterilization packages; (b) performing the sterilization quality control process; (c) result of the quality control of the process of sterilization of the sticks in potato agar, after incubation for 05 days at 30 °C; (d) toothpicks of other sizes (10 and 20 cm) submitted to the same process.
Large-scale purification of Q23 and Q54 HTT-HAP40 from Sf9 expression system 2019/07/29
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Large-scale purification of Q23 and Q54 HTT-HAP40 from Sf9 expression system</p> <p><strong>Date completed:­ </strong>2019/07/29</p> <p><strong>Rationale: </strong>HTT samples are to be purified from different systems for subsequent biophysical and structural studies. </p>
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.