Find research datasets worth reusing

opencc-by-4.0Jan 2020View details →

The Photo Set of the Master Tape of Erkki Kurenniemi's On-Off (1963)

The set includes the photographs of the master tape of Erkki Kurenniemi's first stand alone tape music work On-Off (1963). The tape is located at the University of Helsinki Music Research Laboratory & Electronic Music Studio (UHMRL) tape archive.

opencc-by-4.0Aug 2020View details →

Data cleaning and analysis for the Master's thesis: DIFFERENCES IN CONSUMER PREFERENCES FOR UNWEATHERED AND WEATHERED WOOD

The data and analytical support the Master's thesis submitted by Hana Remesova at the University of Primorska Faculty of Mathematics, Natural Sciences, and Information Technologies. The .csv files are data files, the .Rmd file is an R markdown which can be run. The product of knitting the .Rmd file is the .html.

opencc-by-4.0Jul 2018View details →

Data accompanying the master thesis: A neuronal model for visually evoked startle responses in schooling fish

This dataset contains data that was generated and analyzed for the master thesis "A neuronal model for visually evoked startle responses". All related material, including analysis code, of the master thesis can be found at https://github.com/awakenting/master-thesis.

opencc-by-4.0Apr 2023View details →

Master Coral database used in USVI SCTLD Transmission Experiment Gene Expression Analysis

The Master Coral Database fasta file is comprised of previously published genome-derived predicted gene models and transcriptomes spanning a wide diversity of coral families. Transcriptomes are from Davies et al., 2016 (doi: 10.3389/fmars.2016.00112), Kirk et al., 2018 (DOI: 10.1111/mec.14934); Moya et al., 2012 (doi: 10.1111/j.1365-294X.2012.05554.x); van de Water et al., 2018 (DOI: 10.1111/mec.14489).

opencc-by-4.0Mar 2023View details →

Master and Landsat-8 simultaneous acquisition datacubes for the quantification of directional anisotropy in Thermal Infra-Red domain

&lrm; This dataset contains datacubes of simultaneous Landsat-8 and Master<a href="#fn.1">1</a> data as listed in table <a href="#org4c9ba67">1</a>. Those pairs have been identified by cross-searching Landsat-8 and Master archive for Master flight tracks with a Landsat-8 overpass during the flight. The dataset has been collected and analysed in the following paper: Julien Michel, Olivier Hagolle, Simon J Hook, Jean-Louis Roujean, Philippe Gamet. Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions. 2023. <a href="https://hal.science/hal-04073733">&lang;hal-04073733&rang;</a> <table> <caption>Table 1: List of valid Master and Landsat-8 pairs</caption> <thead> <tr> <th scope="col">Id</th> <th scope="col">Master track id</th> <th scope="col">Landsat L2 product id</th> </tr> </thead> <tbody> <tr> <td>1</td> <td><code>2013-03-29_18:06:53</code></td> <td><code>LC08_L2SP_038037_20130329_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>2</td> <td><code>2013-04-11_18:14:46</code></td> <td><code>LC08_L2SP_041036_20130411_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>3a</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040036_20130522_20200913_02_T1</code></td> </tr> <tr> <td>3b</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040037_20130522_20200913_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>4</td> <td><code>2013-12-05_18:23:35</code></td> <td><code>LC08_L2SP_043035_20131205_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>5a</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039035_20140331_20200911_02_T1</code></td> </tr> <tr> <td>5b</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039036_20140331_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>6a</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041036_20140414_20200911_02_T1</code></td> </tr> <tr> <td>6b</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041037_20140414_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>7</td> <td><code>2014-04-28_18:22:43</code></td> <td><code>LC08_L2SP_043035_20140428_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>8a</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044033_20140606_20200911_02_T1</code></td> </tr> <tr> <td>8b</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044034_20140606_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>9a</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043034_20141021_20200910_02_T1</code></td> </tr> <tr> <td>9b</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043035_20141021_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>10a</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040036_20150528_20200909_02_T1</code></td> </tr> <tr> <td>10b</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040037_20150528_20200909_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>11</td> <td><code>2018-06-19_18:28:30</code></td> <td><code>LC08_L2SP_042034_20180619_20200831_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>12a</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043033_20210330_20210409_02_T1</code></td> </tr> <tr> <td>12b</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043034_20210330_20210409_02_T1</code></td> </tr> </tbody> </table> Variables of interest are resampled on a common UTM grid at 100m. The resulting datacubes are distributed as netCDF files, and contains the variables listed in table <a href="#org09b0cd2">2</a>. Landsat-8 pixels flagged as cloud and missing pixels are set to NaN. <table> <caption>Table 2: Description of variables in netCDF files</caption> <thead> <tr> <th scope="col">Variable Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><code>ls8_lst</code></td> <td>Landsat-8 Land Surface Temperature (K)</td> </tr> <tr> <td><code>ls8_bt</code></td> <td>Landsat-8 Surface Brightness temperature (K)</td> </tr> <tr> <td><code>ls8_b2</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b3</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b4</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b5</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_emis</code></td> <td>Landsat-8 emissivity (unitless)</td> </tr> <tr> <td><code>ls8_water</code></td> <td>Landsat-8 water mask (1 = water, 0 = no water)</td> </tr> <tr> <td><code>ls8_snow</code></td> <td>Landsat-8 snow mask (1 = snow, 0 = no snow)</td> </tr> <tr> <td><code>ls8_view_zenith</code></td> <td>Landsat-8 view zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_view_azimuth</code></td> <td>Landsat-8 view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>ls8_sun_zenith</code></td> <td>Landsat-8 sun zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_sun_azimuth</code></td> <td>Landsat-8 sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> <tbody> <tr> <td><code>master_lst</code></td> <td>Master Land Surface Temperature (K)</td> </tr> <tr> <td><code>master_bt</code></td> <td>Master Surface Brightness Temperature (K)</td> </tr> <tr> <td><code>master_emis3</code></td> <td>Master B47 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis4</code></td> <td>Master B48 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis</code></td> <td>Master interpolated emissivity (unitless)</td> </tr> <tr> <td><code>master_view_zenith</code></td> <td>Master view zenith angle (degrees)</td> </tr> <tr> <td><code>master_view_azimuth</code></td> <td>Master view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>master_sun_zenith</code></td> <td>Master sun zenith angle (degrees)</td> </tr> <tr> <td><code>master_sun_azimuth</code></td> <td>Master sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> </table> Landsat-8 products were downloaded from the collection 2 level 2 archive from the EarthExplorer portal<a href="#fn.2">2</a>. Master L1B products, containing radiances and viewing angles, as well as L2 products, containing LST and geo-location grids, were requested on the Master website<a href="#fn.1">1</a>. Landsat-8 viewing angles have been computed by using a C program publicly available on USGS website<a href="#fn.3">3</a>. Footnotes: <a href="#fnr.1">1</a> <a href="https://masterprojects.jpl.nasa.gov/">https://masterprojects.jpl.nasa.gov/</a>, consulted on 2023.03.01 <a href="#fnr.2">2</a> <a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a>, consulted on 2023.03.01 <a href="#fnr.3">3</a> <a href="https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file">https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file</a>, consulted on 2022.09.12

opencc-by-4.0May 2023View details →

Arthralgia in female Masters weightlifters

OVERVIEW 1. Title of Dataset: Arthralgia in female Master weightlifters 2. Author Information Name: Marianne Huebner Institution: Michigan State University Address: East Lansing, MI 48824 3. Period of data collection: 27 April – 20 May 2022 4. Geographic region of data collection: Online survey in USA with participants from 30 countries in IWF regions Africa, Asia, Europe, Oceania, PanAmerican LIST OF FILES Dataset: wlmeno_oa.csv Data dictionary: wlmeno_oa_meta.xlsx METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: The survey was distributed by the Master Committee of the International Weightlifting Federation (IWF) to the National Master Chairs. They then used email or social media to communicate the study to the women weightlifters. The Survey was available in four languages (English, German, French, Spanish), translated and tested by native speakers. In addition, the survey was advertised in weightlifting interest groups via Facebook and Instagram. The survey was administered online via Qualtrics (Provo, UT, USA). 2. Methods for processing the data: Data were downloaded from Qualtrics (Provo, UT, USA) to Excel and then pre-processed in the statistical software R v. 4.3.0. (<a href="https://www.r-project.org/">https://www.r-project.org</a>)  Variable formats (numeric, character) were checked and transformed, as appropriate. 3. Quality-assurance procedures performed on the data:  Exclusion criteria were younger than 30 years (n=1), currently pregnant (n=3). To account for the possibility of male participants missing responses to age of menstruation or prior pregnancies (n=22), were also excluded. Since the focus was on active weightlifters, missing best snatch or clean and jerk in the last 6 months (n=18) were also exclusion criteria. This resulted in an analysis data set of 868 females. Univariate distributions were evaluated numerically and graphically.  DATA-SPECIFIC INFORMATION 1.Number of variables: 51 2.Number of cases/rows: 868 3.Variable List: wlmeno_oa.xlsx 4.Missing data codes: empty cells

edi44/100

Dataset from University of Idaho 2004, master's thesis [Littoral ecology of epilithic algae in the Rocky Reach Pool, Mid-Columbia River (Washington State) - The effects of reservoir fluctuations.]

(Abstract from thesis) Epilithic algae, water column physical/chemical properties, and sediments were examined in the impounded Mid-Columbia River including the Rocky Reach Reservoir. Primary objectives included determination of the effects reservoir drawdown has on epilithic algae and potential nutrient enrichment via sediment. Epilithic algae were analyzed by pigment concentration, gravimetrically, and species composition. Reservoir elevation fluctuated at higher rates at tailrace sites (0.41-0.25 m/hr) compared to the forebay site (0.06-0.08 m/hr). Littoral exposure times were also greater at tailrace sites (mean of 8 hrs compared to 0 hr at the forebay site). Mean epilithic algae monochromatic chlorophyll a over all sampling periods at mainstem sites was 76.7 ± 4.8 mg/m2 (95 % C.I.). Epilithic algae monochromatic chlorophyll a in the zone of water fluctuation (0-1 m) was less at Wells tailrace (38.8 mg/m2) compared to Rocky Reach forebay (141.3 mg/m2) during summer, 2000 and 2001. Mean epilithic biofilm ash-free oven-dry weight over all sampling periods at mainstem sites was 25.6 ± 1.5 g/m2 (95 % C.I.). Mean autotrophic index across all mainstem locations was 439 indicating a large heterotrophic component within the epilithic biofilms. Epilithic algae communities were dominated by diatoms (50.2 %) and cyanobacteria (35.9 %), with some green algae (13.8 %). Canonical correlation analysis indicated that temperature, depth, site, and the water elevation change rate were important controllers of epilithic algae chlorophyll pigments. Mean textural characteristics of dredged sediment were 51.1 % sand, 43.2 % silt, and 5.7 % clay. Mean organic matter content in this sediment was 4.1 %. The mean seston sedimentation rate across mainstem locations was 11.3 g m-2 d-1 and organic matter comprised 14.7 % of the material collected from the water column.

openCC0Apr 2022View details →

edi44/100

SBC LTER: Reef: Master species list

These data are the master species list for all the biological surveys conducted in kelp forest communities in the Santa Barbara Channel. In addition to the taxonomic information for the taxa, there are aphiaID (a taxonomic ID from the WORMs database, see methods) and species’ minimum and maximum size ranges.

openCC (other)Feb 2021View details →

opencc-by-4.0May 2020View details →

Geophysical data set for San Ramon Fault master section

This data set is a multivariable analysis carried out in the San Ramon Fault (SRF) along a master section perpendicular to the main fault scarp. These data include: (1) a ~ 1 km long Electrical resistivity tomography (ERT) with a dipolo-dipolo configuration, 48 channels every 20 meters (named as the master section). (2) Differential GPS data with the location of the ERT electrodes and gravity stations. (3) Data of the gravity stations, with a Garmin GPS data. (4) Seismic data of an active experiment of 24 channels every 5 m, with several hammer strikes, which are explained in a text-document inside each seismic file (TRV_seismic_data and SRF_seismic_data). (5) Time-series of the Nakamura stations located along the ERT master profile. (6) The voltage decay curve of a TEM-station in the western edge of the master section, which is ready for modeling.

opencc-by-4.0Nov 2024View details →

Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden"

Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden" (Monitoring open data practices - challenges in finding data publications using the example of publications by researchers at TU Dresden) - Katharina Zinke, Institut für Bibliotheks- und Informationswissenschaften, Humboldt-Universität Berlin, 2023 This ZIP-File contains the data the thesis is based on, interim exports of the results and the R script with all pre-processing, data merging and analyses carried out. The documentation of the additional, explorative analysis is also available. The actual PDFs and text files of the scientific papers used are not included as they are published open access. The folder structure is shown below with the file names and a brief description of the contents of each file. For details concerning the analyses approach, please refer to the master's thesis (publication following soon). ## Data sources  Folder 01_SourceData/ - PLOS-Dataset_v2_Mar23.csv (PLOS-OSI dataset) - ScopusSearch_ExportResults.csv (export of Scopus search results from Scopus) - ScopusSearch_ExportResults.ris (export of Scopus search results from Scopus) - Zotero_Export_ScopusSearch.csv (export of the file names and DOIs of the Scopus search results from Zotero) ## Automatic classification  Folder 02_AutomaticClassification/ - (NOT INCLUDED) PDFs folder (Folder for PDFs of all publications identified by the Scopus search, named AuthorLastName_Year_PublicationTitle_Title)  - (NOT INCLUDED) PDFs_to_text folder (Folder for all texts extracted from the PDFs by ODDPub, named  AuthorLastName_Year_PublicationTitle_Title) - PLOS_ScopusSearch_matched.csv (merge of the Scopus search results with the PLOS_OSI dataset for the files contained in both) - oddpub_results_wDOIs.csv (results file of the ODDPub classification) - PLOS_ODDPub.csv (merge of the results file of the ODDPub classification with the PLOS-OSI dataset for the publications contained in both) ## Manual coding  Folder 03_ManualCheck/ - CodeSheet_ManualCheck.txt (Code sheet with descriptions of the variables for manual coding) - ManualCheck_2023-06-08.csv (Manual coding results file) - PLOS_ODDPub_Manual.csv (Merge of the results file of the ODDPub and PLOS-OSI classification with the results file of the manual coding) ## Explorative analysis for the discoverability of open data   Folder04_FurtherAnalyses  Proof_of_of_Concept_Open_Data_Monitoring.pdf (Description of the explorative analysis of the discoverability of open data publications using the example of a researcher) - in German ## R-Script  Analyses_MA_OpenDataMonitoring.R (R-Script for preparing, merging and analyzing the data and for performing the ODDPub algorithm)

opencc-by-4.0Apr 2024View details →

Master curve BZN_valids_ER_1921-20

<div>Fractesus project. Fracture test mini-CT. Raw data JRQ. BZN.                                                                               </div>

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 9b. Translation of a tab for all languages

After checking a few (or all!) languages for instance and pressing the Ok button , we obtain the windows shown in figure 9a, or respectively 9b for all languages. Here we can add one or more missing translations, or modify one or more of the existing translations accordingly. All the data within the view cluster can be translated into any language supported by the system.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 8. GoTo -> Translation Option

For example, we select in the view cluster the tab called “Forecasting” and then choose Goto -> Translation (figure 8). After selecting Goto -> Translation, we obtain a selecting window for the desired languages where the user can check one or more languages to translate those tabs or areas into.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 9a. Translation of a tab for certain languages

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 7. Dialog structure of the View Cluster

Field dependency has been generated automatically and as a result the following desired structure has been achieved (figure 7). After populating the database tables with data in different languages with the help of the view cluster, the popup has been adapted in order to support the translation of the tabs and areas. Supplementary internal tables have been defined in the function module POPUP_FLEX, in order to copy data from the translation tables. The corresponding SELECT statements have been embedded in TRY-CATCH blocks, in order to prevent short dumps due to faulty selection processes.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 6. View cluster TAFC

For all these tables and views, table maintenance generators have been created and activated, in order to have the possibility to manage individual datasets in every table and view. The corresponding names of those function groups for the table maintenance generators are the same names as those for the views. The purpose of these maintenance views is only to take care of the input data more efficiently. These views will be used later in the view cluster, which ensures a hierarchical order of the data. Therefore, the maintenance views will also include the predecessor, in order to facilitate linking in the field dependency tab of the view cluster (Swapna, 2007). These three maintenance views are embedded in the following view cluster (figure 6)

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 5. Maintenance Views

Beside the tabs and the areas, the database table FLD also contains the fields TABLENAME and FIELD, which suggest the related parameters, whom input may be updated at runtime. Through standard SAP functionality the tables in BASIS, respectively their fields are by default translated in the login language of the user. So in the fields TABLENAME and FIELD of the FLD table, we will obtain, in the user login language, the names of the tables and fields from BASIS via the foreign keys to the table DD03L for TABLENAME and to the table DD02L for FIELD. Beside the database tables, 3 maintenance views have been created, TABV, AREV and FLDV, for the tabs, areas and fields of the popup (figure 5). We have chosen maintenance views instead of database views to be able to use them in the view cluster.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 2. Content of the single database table in the previous implementation

In the previous implementation there has been used a single database table which did not provide a consistent overview of existing tabs, areas and fields, as well as of the languages, in which a specific field of an area or tab was translated. So, it was difficult to maintain this database table by the customizing end-users in different languages, because every update of a tab, area or field required a number of actions in this table which had to be done manually and very carefully, requiring much time and attention. The number of rows of this table was very large and the content looked like the one shown in figure 2.