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106 results for “data merge”
Merged discrete water-column data from PAL LTER research cruises along the Western Antarctic Peninsula, from 1991 to 2024.
Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. This dataset is the compilation of water samples collected at these stations, merged from other PAL-LTER datasets. Data includes water column Chlorophyll and Phaeopigment concentrations; phytoplankton accessory pigments -including other chlorophyll's (e.g. chlorophyll b), xanthophylls, and carotenes; primary production rates; bacterial production; dissolved organic carbon; particulate organic carbon and nitrogen; dissolved inorganic nutrients; dissolved oxygen, and dissolved inorganic carbon and alkalinity. Measurements of phytoplankton Fv/Fm measured using a FIRe (Fluorescence Induction and Relaxation) fluorometer are also included, though caution is urged as FIRe data has not been corrected nor QCed. Conductivity, temperature, depth, and associated data from instruments on the sampling rosette (e.g. PAR, beam transmission) for each sampling depth are also included. Phytoplankton accessory pigment data is unavailable for the LMG10-01 cruise due to instrumentation problems and for the LMG12-01 cruise due to a freezer failure which resulted in the loss of samples. Dissolved oxygen measurements were discontinues after 2012, and thus no data is available from 2013 onwards. Dissolved Organic Carbon data is unavailable after 2012 due to instrumentation problems. There is no Particulate Organic Carbon data for cruise PD94-01.
Merged discrete water-column data from annual PAL LTER field seasons at Palmer Station, Antarctica, from 1991 to 2025.
Water samples are collected throughout the water column at regular stations within the Palmer LTER region at Palmer Station, on Anvers Island, Antarctica. CTD casts are preformed and water collected using go-flo bottles or a rosette. Near-surface waters are also sampled occassionally at the Palmer Station seawater intake (pumphouse). This dataset is the compilation of water samples collected at these stations, merged from other PAL-LTER datasets. Data includes water column Chlorophyll and Phaeopigment concentrations; phytoplankton accessory pigments -including other chlorophyll's (e.g. chlorophyll b), xanthophylls, and carotenes; primary production rates; bacterial production; dissolved organic carbon; particulate organic carbon and nitrogen; dissolved inorganic nutrients; dissolved oxygen, and dissolved inorganic carbon and alkalinity. Measurements of phytoplankton Fv/Fm measured using a FIRe (Fluorescence Induction and Relaxation) fluorometer are also included, though caution is urged as FIRe data has not been corrected nor QCed. Conductivity, temperature, depth, and associated data from instruments on the CTD/sampling rosette (e.g. PAR, beam transmission) for each sampling depth are also included. Phytoplankton accessory pigment data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, 2016-2017, 2019-2020, 2020-2021, 2023-2024, and 2024-2025 seasons because those samples have not been analyzed yet. Dissolved oxygen measurements and dissolved inorganic carbon/alkalinity measurements are not available for any season. Dissolved Organic Carbon data is unavailable after 2012 due to instrumentation problems. There is no Particulate Organic Carbon after the 2012 season.
The Spitzer Spectroscopic Data Fusion - Merged Spectroscopic Redshift Catalogs in Spitzer Fields
<p>The Spitzer Spectroscopic Data Fusion merges miscellaneous spectroscopic information available within "popular" extragalactic survey fields.</p> <p>Last Updated on 20 March 2025 - <a href="https://zenodo.org/record/6368347">https://zenodo.org/record/6368347</a> - <a href="https://www.mattiavaccari.net/df/specz">https://www.mattiavaccari.net/df/specz</a></p> <p>Based on the Spitzer Data Fusion Project - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a> - <a href="https://mattiavaccari.net/df">https://mattiavaccari.net/df</a></p> <p>Merged Spec-Z ("specz-merged") catalogs merge miscellaneous spec-z information available within a given field. Different spec-z catalogs available within a given field are merged (using a search radius of 1.0 arcsec), and for sources with multiple spec-z measurements the most reliable one is chosen following the (largely arbitrarily) assumed order of decreasing reliability indicated below for each field. If CAT1,...,CATN spec-z catalogs are available in a given field, Z_1 from CAT1 (i.e. NED) is adopted as "best" redshift (i.e. ZBEST), if available, otherwise Z_2 from CAT_2 is adopted if available, and so on up to Z_N and CAT_N. In using ZBEST it's thus important to bear in mind that this is not necessarily actually the "best" redshift for science purposes, and in particular that the choice of NED as CAT1 is often not ideal. However, Z_1,...,Z_N are included to allow users to define the "best" redshift based on their science needs when multiple redshift estimates are available for a given source. ZFLAG specifies which catalog is providing the ZBEST value according to the CATN number below. ZCLASS is meant to provide further info about the class/quality of the spectroscopic redshift measurement, but for the time being is not populated and is simply a copy of ZFLAG. ZWHERE is an additional binary/bit flag indicating in which of the N catalogs each source was given a spec-z estimate in. ZWHERE will e.g. be 2^0=1 if a redshift if available <em>only</em> from CAT_1, whereas it will be 2^0+2^1=3 if a redshift is available *only* from CAT1 and CAT2,<br>so that a source with a redshift available from all catalogues will have ZWHERE=2^0+...+2^n.</p> <p>See AAAREADME.SPECZ-MERGED within the ZIP archive for further information.</p>
The population of merging compact binaries inferred using gravitational waves through GWTC-3 - Data release
<p>Data associated with Figures, Tables, and population parameter samples associated with <br><strong>The population of merging compact binaries inferred using gravitational waves through GWTC-3 , </strong><br><strong><a href="https://dcc.ligo.org/LIGO-P2100239/public">LIGO DCC</a>, <a href="https://arxiv.org/abs/2111.03634">arXiv</a>, <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.13.011048">PRX</a>. </strong><br>This is v3, superseding v2. Please see the README.md for more information.</p>
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data
<p>The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the test compounds to training compounds. We applied these similarity-based merger models using logistic equations to weigh individual features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute, where the required Cell Painting annotations were available. We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.</p>
Data for paper "Efficient Convex Zone Merging in Parametric Timed Automata"
<p>This is the experimental data for paper "Efficient Convex Zone Merging in Parametric Timed Automata"</p> <p>It comes in the form of two archives:</p> <ol> <li><strong>merging-artifact.zip</strong>: the whole set of benchmarks with the scripts to run</li> <li><strong>merging-artifact-FORMATS22-results.zip</strong>: all results executed 5 times</li> </ol>
WageIndicator salary survey face-to-face 2006-2013 merged data
<p>The dataset includes the merged data of face-to-face salary surveys in 28 countries, undertaken between 2006 and 2013. The total number of observations is 59,138 individuals in the labour force. Almost all countries are developing countries in Africa, Asia, and Latin America.</p>
Data of the PhD thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation"
<p>This data set contains raw data and parsed data of all experiments [1] run for the PhD thesis. They were generated using lab (see https://doi.org/10.5281/zenodo.399255).</p> <p>The raw data files (sievers-phd2017-raw-data-part*.tar.gz) contain a subdirectory for each experiment, each containing a subdirectory for each planner run of the experiment, distributed over the directories runs-*. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), and the run script "run" used to start the experiment. The latter cannot be directly used, however, because the directory containing source code and build (compiled object files) have been removed for space reasons. The code is publicly available under https://doi.org/10.5281/zenodo.1163381. The (lab) scripts for parsing run.log are also available in the main directory of each experiment. All other scripts and a corresponding lab version are available on request.</p> <p>For each raw data experiment, the parsed data file (sievers-phd2017-parsed-data.tar.gz) also contains a directory of the same name, with "-eval" appended. It contains a single file called "properties" that combines all of the experiment's parsed data (which can be and was generated from the raw data using lab and the parser scripts). They are in the json format and can be used for easy manipulation of the data. The directories with the prefix "paper-" and "talk-" are combinations of other directories (using the "fetch" mechanism of lab). It is recommended to use these, because due to technical errors, the original eval directories do not contain all runs of all planners (to be more precise: they contain all runs, but a subset of the planner have not been started in these experiments for technical errors and thus considered not solving the task). The missing ones have been run separately, see the directories with "missing-runs" in their name. This is also the reason some of these directories ("paper-", "talk-") contain files named "old-properties" and "fixed-properties" besides the actual "properties". "old-properties" are those with missing/faulty runs, "fixed-properties" are as "old-properties", however with the data of faulty runs removed, and "properties" are as "fixed-properties", however with the addition of the fixed missing runs (in fact, these always contain *all* fixed missing runs of all experiments, for technical reasons).</p> <p>The file sievers-phd2017-parsed-data-all-and-random-merge-strategies.tar.gz contains parsed data of earlier experiments (see [1]), for which no raw data has been archived. The directories contain properties files in the json format.</p> <p>[1] except raw data for the parsed data "sota-symba-spmas-eval" (which in the meantime was added to a separate data set available under https://doi.org/10.5281/zenodo.1189912) and all re-used experiments from the paper "An Analysis of Merge Strategies for Merge-and-Shrink Heuristics" (Silvan Sievers, Martin Wehrle and Malte Helmert, ICAPS 2016), for which the raw data was too large to be archived.</p>
Figure 4. After merging, overview is more transparent. Tens of persons were merged together into clusters in order to clarify the visualization. Firms and persons are recognized based on their icons.-Browsing Semantic Data in Slovakia
<p>The usefulness of such visualization has its key points regarding connections. Thanks to SBR browsing module, we were able to get 22 firm records for “Váhostav” query. Between any 2 companies, connections may be (and often are) not bidirectional, so, in order to navigate through connections, we have refined all 22 records. Although, even being filtered, graph is still complex. And it is possible to further navigate and search for outgoing connections, for example firm “MERLIN TRADE, a.s.” on Fig.4 contains item on “Ján Kato”, which is already included in our graph and connected to “VÁHOSTAV&SK&DEVELOPEMENT” on bottom left side and “VÁHOSTAV&SK, a.s.” in the center. Edge coloring and drawing is helpful with overlapped edges. For methods of visualization, including coloring, we refer to studies of H. Omote and K. Sugiyama (2006), and I. Herman, G. Melanon, and M. S. Marshall (2000) or our study on graph clutter filtering and connectivity distance (Mojzis & Laclavik, 2014).</p>
Data supporting: Methodological overview and data-merging approaches in the study of plant-frugivore interactions
<p>Recording species interactions is one of the main challenges in ecological studies. Frugivory has received much attention for decades as a model for mutualisms among free-living species, and a variety of methods have been designed and developed for sampling and monitoring plant–frugivore interactions. The diversity of techniques poses an important challenge when comparing, combining or replicating results from different sources with different methodologies. With the emergence of modern techniques, such as molecular analysis or multimedia remote recorders, issues when combining data from different sources have become especially relevant. We provide an overview of all the techniques used for monitoring endozoochorous primary seed dispersal, focusing on a critical appraisal of the advantages and limitations, as well as the context-dependency nature, of the different methods. We propose five data merging approaches potentially useful to combine frugivory interactions data from different methodologies. Additionally, we provide two case studies where we combine empirical data from plant–animal interactions in Mediterranean shrublands using different methodologies. Data merging resulted in a net increase in the number of distinct pairwise interactions recorded and compensated biases inherent to different methods, resulting in a more robust estimation of network topological descriptors. These case studies clarify the context-dependent character of the merging approaches, highlighting the value of collecting detailed information on the sampling effort in terms of reliable results and reproducibility. Finally, we discuss the trends with different methodological approaches used in the last decades and future perspectives in this field.</p>
Figure 4. Phenetic tree with 25 in A web based tool to merge geometric morphometric data from multiple characters
Figure 4. Phenetic tree with 25 dung beetles based on Procrustes distances, which are computed from the merged geometric morphometric data from characters of the epipharynx, right mandible, pronotum, elytra, hind wing, and metendosternite in lateral and dorsal view (totally 649 landmarks). Abbreviations for the tribe names: AT—Ateuchini, CO—Coprini, DE—Deltochilini, GY— Gymnopleurini, ON—Oniticellini, OP—Onthophagini, OT—Onitini, SC—Scarabaeini, SI—Sisyphini.
Figure 2 in A web based tool to merge geometric morphometric data from multiple characters
Figure 2. Description of the landmarks, curves and merged data. a). Epipharynx (Onthophagus (Palaeonthophagus) gibbulus in dorsal view), 14 landmarks and five curves. b). Mandible (right mandible of Synapsis yunnanus in dorsal view), eight landmarks and four curves. c). Pronotum (Euonthophagus amyntas), two landmarks and one curve. d). Elytra (Euonthophagus amyntas), three landmarks and one curve. e). Hind wing (right hind wing of Copris lunaris), 19 landmarks, terminology following Kukalová- Peck and Lawrence (1993). f). Metendosternite (Digitonphagus gazelle in lateral view), nine landmarks and four curves. g). Metendosternite (Digitonphagus gazelle in dorsal view), 14 landmarks and five curves. h). Merged data of all seven characters. All curves were re-sampled in 30 semi-landmarks, except the curves mentioned in different numbers of semi-landmarks, such as all curves in Figs 1a–b, and curve 1 in Fig. 1f.
Figure 3 in A web based tool to merge geometric morphometric data from multiple characters
Figure 3. Morphological variation of 25 dung beetles species based on merged geometric morphometric data from the epipharynx, right mandible, pronotum, elytra, hind wing, and metendosternite in lateral and dorsal view (totally 649 landmarks). Relative warps computed from the landmark data set merged from seven body character complexes. Each tribe is indicated by a different color. The Minimal Spanning Tree is the shortest possible set of lines connecting all points.
Merging and imputation of flow cytometry data: a critical assessment
<p>This dataset contains the data necessary to reproduce the analyses by Mocking <em>et al</em>.</p> <p>Scripts for reproducing our analyses are available at:</p> <p>https://github.com/AUMC-HEMA/imputation-manuscript</p>
Test data for RAW high dynamic range merging
<p>* the data contains a set of .CR2 raw images taken with a Canon 5D mark II<br> * the exposures where done on a tripod in manual mode, the EXIF information is correct<br> * only the exposure times were varied:</p> <p>IMG_7220.CR2<br> Exposure Time : 1<br> IMG_7221.CR2<br> Exposure Time : 1/5<br> IMG_7222.CR2<br> Exposure Time : 1/20<br> IMG_7223.CR2<br> Exposure Time : 1/80<br> IMG_7224.CR2<br> Exposure Time : 1/320<br> IMG_7225.CR2<br> Exposure Time : 1/1250</p> <p>* Author: Ivo Ihrke, ~2011, Universität des Saarlandes / MPI Informatik</p> <p> </p>
Raw, processed and merged Data for Swiss Cat+ East A1 project related to the automated and high-throughput Bayesian Optimization of CO2 hydrogenation heterogeneous catalysts
<p> All files generated during the fully digitalized automated and high-throughput experimentally-guided Bayesian Optimization project, which led to the synthesis of 144 heterogeneous catalysts with a Chemspeed unit (6 generations of 24) and their testing under CO2 hydrogenation conditions with Avantium fixed bed units. Below are some indication to understand the naming of the files.</p> <ul> <li>A1 stands for the internal project number.</li> <li>G1 to G5 stands for the catalyst generation number and G2NC for the alternative second generation suggested by the Bayesian Optimizer without considering the cost of catalyst as an objective (No_Cost).</li> <li>Three fixed bed units have been used, named XDB4x (a 4 parallel reactors unit), XDC4x (another 4 parallel reactors unit) and XR16x (a 16 parallel reactors unit).</li> <li>Individual fixed bed testing raw files (FB_RawData) generated by each unit are then processed to extract the mean and standard deviation (std) values (e.g conversion, selectivity) and to compute reactions rates.</li> <li>Then the processed files for each individual reactor (XDB, XDC, XR) are combined into one file (All_FBData), and finally aggregated with the synthesis details, viathe catalyst barcodes (AllData_Processed).</li> <li>Finally, the processed file for each generation are merged together (AllGen_Merged) and a condensed file is generated for a given reaction temperature (AllGen_275CDataProcessed_Merged)</li> </ul>
Data supporting: Methodological overview and data-merging approaches in the study of plant-frugivore interactions
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A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of TRMM PR and VIRS
<p>This new merged gridded dataset (M-1B01-2A25-GD) contains precipitation, clouds and atmospheric parameters with 0.25° spatial resolution.</p> <p>It is produced by merging TRMM PR and VIRS measurements with the ERA5 reanalysis dataset at the same spatiotemporal resolution between 40° S and 40° N. The near-surface rain rate, profiles of rain rate and precipitation reflectivity factor, visible and infrared signals and atmospheric parameters (temperature, pressure, geopotential height, specific humidity, divergence, vertical velocity and so on) can be obtained in the dataset. The statistical results indicate that the merging and gridding will not dramatically distort the original data and the new dataset can be used to study the characteristics and distribution of the precipitation and clouds systems.</p>
Merged Hadley-OI sea surface temperature and sea ice concentration data set
<p>The merged Hadley-OI sea surface temperature (SST) and sea ice concentration (SIC) data sets were specifically developed as surface forcing data sets for AMIP style uncoupled simulations of the Community Atmosphere Model (CAM). The Hadley Centre's SST/SIC version 1.1 (HADISST1), which is derived gridded, bias-adjusted in situ observations, were merged with the NOAA-Optimal Interpolation (version 2; OI.v2) analyses. The HADISST1 spanned 1870 onward but the OI.v2, which started in November 1981, better resolved features such as the Gulf Stream and Kuroshio Current which are important components of the climate system. Since the two data sets used different development methods, anomalies from a base period were used to create a more homogeneous record. Also, additional adjustments were made to the SIC data set.</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)
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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.