Question Paper Solutions of chapter Data format of Big Data Analysis, 8th Semester , Computer Science and Engineering

Version: 6

Date of data collection: May 2025

General description: Publication of datasets according to the FAIR principles could be reached publishing a data paper (and/or a software paper) in data journals as well as in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.

File list:

- data_articles_journal_list_v6.xlsx: full list of 177 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v6.csv: full list of 177 academic journals in which data papers or/and software papers could be published
- readme_v6.txt, with a detailed descritption of the dataset and its variables.

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 6th version
- Information updated: number of journals (17 were added and 4 were deleted), URL, document types associated to a specific journal.
- Information added: diamond journals were identified.

Version: 5

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2023/09/05

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v5.xlsx: full list of 162 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v5.csv: full list of 162 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 5th version
- Information updated: number of journals, URL, document types associated to a specific journal.
163 journals (excel y csv)

Version: 4

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/12/15

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v4.xlsx: full list of 140 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v4.csv: full list of 140 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 4th version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR), Scopus and Web of Science (WOS), Journal Master List.

Version: 3

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/10/28

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v3.xlsx: full list of 124 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_3.csv: full list of 124 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 3rd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR).

Erratum - Data articles in journals Version 3:

Botanical Studies -- ISSN 1999-3110 -- JCR (JIF) Q2
Data -- ISSN 2306-5729 -- JCR (JIF) n/a
Data in Brief -- ISSN 2352-3409 -- JCR (JIF) n/a

Version: 2

Author: Francisco Rubio, Universitat Politècnia de València.

Date of data collection: 2020/06/23

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v2.xlsx: full list of 56 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v2.csv: full list of 56 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 2nd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Scimago Journal and Country Rank (SJR)

Total size: 32 KB

Version 1: Description

This dataset contains a list of journals that publish data articles, code, software articles and database articles.

The search strategy in DOAJ and Ulrichsweb was the search for the word data in the title of the journals.
Acknowledgements:
Xaquín Lores Torres for his invaluable help in preparing this dataset.

This article describes the plans for the BD2K project bioCADDIE (biomedical and healthCAre Data Discovery Index Ecosystem). It includes several contributions from the data science community.

Viscosity data for normal alkanes (methane, ethane, etc.) taken from the literature to accompany the paper "Entropy Scaling of Viscosity -- II: Predictive Scheme for Normal Alkanes". The data were obtained from an internal version of the NIST ThermoDataEngine database version 10.4.2. File contents include Contents MIDAS_alkanes.csv: The data file containing all the data considered in this study MIDAS_alkanes.bib: The bibliography associated with each datasource in BibTeX format allbibs.pdf: A PDF conversion of the bibliography Data File format ## The data are in a comma-separated text format, with the column headings indicating the contents of the column, along with units, where appropriate The phase indicates how the data were measured "L" indicates a liquid phase, "G" a gas phase, "G L" a saturated vapor, and "L G" a saturated liquid For saturated states ("G L" or "L G"), the saturation temperature fully specifies the state For liquid and gaseous states, either the temperature and pressure or temperature and density are provided, and the unused state variable is empty Fluid names match the default names of the compound from NIST REFPROP library The column "TRC_code" indicates the reference code for the data point, and the same references (with spaces and & replaced with hyphens) are used as keys in the BibTeX file

Dataset corresponding to the journal article "Mitigating the effect of errors in source parameters on seismic (waveform) inversion" by Blom, Hardalupas and Rawlinson, accepted for publication in Geophysical Journal International. In this paper, we demonstrate the effect or errors in source parameters on seismic tomography, with a particular focus on (full) waveform tomography. We study effect both on forward modelling (i.e. comparing waveforms and measurements resulting from a perturbed vs. unperturbed source) and on seismic inversion (i.e. using a source which contains an (erroneous) perturbation to invert for Earth structure. These data were obtained using Salvus, a state-of-the-art (though proprietary) 3-D solver that can be used for wave propagation simulations (Afanasiev et al., GJI 2018).

This dataset contains:

The entire Salvus project. This project was prepared using Salvus version 0.11.x and 0.12.2 and should be fully compatible with the latter.

A number of Jupyter notebooks used to create all the figures, set up the project and do the data processing.

A number of Python scripts that are used in above notebooks.

two conda environment .yml files: one with the complete environment as used to produce this dataset, and one with the environment as supplied by Mondaic (the Salvus developers), on top of which I installed basemap and cartopy.

An overview of the inversion configurations used for each inversion experiment and the name of hte corresponding figures: inversion_runs_overview.ods / .csv .

Datasets corresponding to the different figures.

One dataset for Figure 1, showing the effect of a source perturbation in a real-world setting, as previously used by Blom et al., Solid Earth 2020

One dataset for Figure 2, showing how different methodologies and assumptions can lead to significantly different source parameters, notably including systematic shifts. This dataset was kindly supplied by Tim Craig (Craig, 2019).

A number of datasets (stored as pickled Pandas dataframes) derived from the Salvus project. We have computed:

travel-time arrival predictions from every source to all stations (df_stations...pkl)

misfits for different metrics for both P-wave centered and S-wave centered windows for all components on all stations, comparing every time waveforms from a reference source against waveforms from a perturbed source (df_misfits_cc.28s.pkl)

addition of synthetic waveforms for different (perturbed) moment tenors. All waveforms are stored in HDF5 (.h5) files of the ASDF (adaptable seismic data format) type

How to use this dataset:

To set up the conda environment:

make sure you have anaconda/miniconda

make sure you have access to Salvus functionality. This is not absolutely necessary, but most of the functionality within this dataset relies on salvus. You can do the analyses and create the figures without, but you'll have to hack around in the scripts to build workarounds.

Set up Salvus / create a conda environment. This is best done following the instructions on the Mondaic website. Check the changelog for breaking changes, in that case download an older salvus version.

Additionally in your conda env, install basemap and cartopy:

conda-env create -n salvus_0_12 -f environment.yml conda install -c conda-forge basemap conda install -c conda-forge cartopy

Install LASIF (https://github.com/dirkphilip/LASIF_2.0) and test. The project uses some lasif functionality.

To recreate the figures: This is extremely straightforward. Every figure has a corresponding Jupyter Notebook. Suffices to run the notebook in its entirety.

Figure 1: separate notebook, Fig1_event_98.py

Figure 2: separate notebook, Fig2_TimCraig_Andes_analysis.py

Figures 3-7: Figures_perturbation_study.py

Figures 8-10: Figures_toy_inversions.py

To recreate the dataframes in DATA: This can be done using the example notebook Create_perturbed_thrust_data_by_MT_addition.py and Misfits_moment_tensor_components.M66_M12.py . The same can easily be extended to the position shift and other perturbations you might want to investigate.

To recreate the complete Salvus project: This can be done using:

the notebook Prepare_project_Phil_28s_absb_M66.py (setting up project and running simulations)

the notebooks Moment_tensor_perturbations.py and Moment_tensor_perturbation_for_NS_thrust.py

For the inversions: using the notebook Inversion_SS_dip.M66.28s.py as an example. See the overview table inversion_runs_overview.ods (or .csv) as to naming conventions.

References:

Michael Afanasiev, Christian Boehm, Martin van Driel, Lion Krischer, Max Rietmann, Dave A May, Matthew G Knepley, Andreas Fichtner, Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophysical Journal International, Volume 216, Issue 3, March 2019, Pages 1675–1692, https://doi.org/10.1093/gji/ggy469

Nienke Blom, Alexey Gokhberg, and Andreas Fichtner, Seismic waveform tomography of the central and eastern Mediterranean upper mantle, Solid Earth, Volume 11, Issue 2, 2020, Pages 669–690, 2020, https://doi.org/10.5194/se-11-669-2020

Tim J. Craig, Accurate depth determination for moderate-magnitude earthquakes using global teleseismic data. Journal of Geophysical Research: Solid Earth, 124, 2019, Pages 1759– 1780. https://doi.org/10.1029/2018JB016902

Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This folder contains data underlying the research paper “Exploring potential contributions of open data intermediaries”. The research is about open data ecosystem and the role of open data intermediaries. The folder consists of 4 items:

1. Tentative interview questions (.pdf and .odt formats)

2. Informed consent form template (for verbal interview & written interview) (.pdf and .odt formats)

3. De-identified interview transcripts (.pdf and .odt formats)

4. Coding results (.pdf and .ods formats)

Note about the tentative interview questions:

The interviews were conducted between May and July 2023 based on the semi-structured approach. We customise the tentative interview questions accordingly for each interview and share them with the interviewees in advance (for the majority, at least three working days in advance). As semi-structured interviews, the ultimate interview questions may differ from the tentative questions based on the information provided by the interviewees and time constraints (refer to item #3).

Note about the informed consent form:

We sent the informed consent form to every interviewee in advance and requested them to return it to us before the interview. The consent form has been reviewed by TU Delft's Human Research Ethics Committee (HREC).

Note about the de-identified interview transcripts (and coding results):

The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of open data intermediaries. We removed personally identifiable information from the transcripts. A few interviewees may risk being identifiable if their organisation is known. Hence, we removed the identification of the organisation and country in all transcripts. Partially disclosing the organisation or country for some transcripts increases the risks of identifying the non-disclosed transcripts. With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcript as close to verbatim as possible. All interviewees whose interview transcripts are recorded in this document give permission for the anonymised transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.

Acknowledgement:

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.

The 2024-25 Budget is officially available at budget.gov.au as the authoritative source of Budget Papers (BPs) and Portfolio Budget Statement (PBS) documents. The 2024-25 Budget was tabled in the Parliament on Tuesday, 14 May 2024 (Budget night).

This dataset is a collection of data sources from the 2024-25 Budget, including:

• PBS Excel spreadsheets (including Table 2.X.1 Budgeted Expenses for Outcome X in machine readable format) – available after PBSs are tabled in the Senate (~8.30pm Budget night); and
• Selected tables from Budget Paper No. 4 (including in machine readable format) will be available (~8.30pm Budget night)

The data has been provided to assist those who wish to analyse and visualise key elements of the 2024-25 Budget. Data users should refer to footnotes and memoranda in the original files as these are not usually captured in machine readable CSVs.

We welcome your feedback and comments below.

This dataset was prepared by the Department of Finance.

Information about the PBS Excel files and CSV: The PBS Excel files published should include the following financial tables with headings and footnotes, which are also are available in CSV. Much of the other data is also available in Budget Papers (No.1) and (No.4) in aggregate form:

• Table 1.1: Entity Resource Statement
• Table 1.2: Entity 2024-25 Budget Measures
• Table 2.X.1: Budgeted Expenses for Outcome X
• Table 2.X.2: Program Component Expenses
• Table 3.1 to 3.6: Departmental Budgeted Financial Statements and
• Tables 3.7 to 3.11: Administered Budgeted Financial Statements.

Please note, total expenses reported in the CSV file ‘2024-25 PBS line items dataset’ were prepared from individual entity program expense tables. Totalling these figures does not produce the total expense figure in ‘Table 1: Estimates of General Government Expenses’ (Statement 6, Budget Paper 1). Differences relate to:

1. Intra entity charging for services which are eliminated for the reporting of general government financial statements
2. Entity expenses that involve revaluation of assets and liabilities are reported as other economic flows in general government financial statements and
3. Additional entities’ expenses are included in general government sector expenses (e.g. Australian Strategic Policy Institute Limited and other entities) noting that only entities that receive funding (either directly or via portfolio department through the annual appropriation acts.

The original PBS Excel files and published documents include sub-totals and totals by entity and appropriation type which are not included in the line item CSV. These can be calculated programmatically. Where modifications are identified they will be updated as required.

The structure of the line item CSV is:

• Portfolio
• Department/Entity
• Outcome
• Program
• Expense type
• Appropriation type
• Description
• 2023-24
• 2024-25
• 2025-26
• 2026-27
• 2027-28
• Source document
• Source table
• URL

The following Portfolios are included in the line item CSV:

• Agriculture, Fisheries and Forestry
• Attorney-General's
• Climate Change, Energy, the Environment and Water
• Defence
• Education
• Employment and Workplace Relations
• Finance
• Foreign Affairs and Trade
• Health and Aged Care
• Home Affairs
• Industry, Science and Resources
• Infrastructure, Transport, Regional Development, Communications and the Arts
• Prime Minister and Cabinet
• Social Services
• Treasury
• Veterans' Affairs (part of the Defence Portfolio)
• Department of the House of Representatives
• Department of the Senate
• Department of Parliamentary Services
• Parliamentary Budget Office

Tables of interest found in both Budget Paper No.1 Budget Strategy and Outlook and Budget Paper No.4 Agency Resourcing are included for reference as well.

For the dataset for the 2023-24 Portfolio Supplementary Additional Estimates Statements, which were also tabled in the Parliament on Tuesday, 14 May 2024, please see the following page: https://data.gov.au/data/dataset/portfolio-supplementary-additional-estimates-statements-psaes-2024-25-tables-and-data

This short paper provides a snapshot of the current status of data journals within the scholarly publishing landscape. The authors highlight the disparate approaches among data journal publishers as to data management and citation, summarised as findings from a survey of a sample of data journals and provides recommendations for future work/further analysis.

Archaeology is awash with data. Unfortunately, individual archaeologists, institutions responsible for the preservation of this historical and scientific information, and others who would be interested in various aspects of these data often cannot easily find them and lack straightforward ways to make use of the data when they can be located. Archaeological data exist in paper and digital formats. Increasingly, digital records are more common and older paper records are being scanned and transferred to digital formats. The access to and preservation of paper records is an important topic; however, this essay focuses on the curation of digital archaeological data, not paper records.

An RDF knowledge graph that provides comprehensive, current information about almost 400,000 machine learning publications. This includes the tasks addressed, the datasets utilized, the methods implemented, and the evaluations conducted, along with their results. Compared to its non-RDF-based counterpart Papers With Code, LPWC not only translates the latest advancements in machine learning into RDF format, but also enables novel ways for scientific impact quantification and scholarly key content recommendation. LPWC is openly accessible and is licensed under CC-BY-SA 4.0. As a knowledge graph in the Linked Open Data cloud, we offer LPWC in multiple formats, from RDF dump files to a SPARQL endpoint for direct web queries, as well as a data source with resolvable URIs and links to the data sources SemOpenAlex, Wikidata, and DBLP. Additionally, we supply knowledge graph embeddings, enabling LPWC to be readily applied in machine learning applications.

Note: the data is 5.5 months behind PapersWithCode, but hopefully this can be amended soon.

Data in excel format underpinning Plos One research paper about cognitive resilience on the Whitehall II imaging sub-study

The data package includes data, model, and code that support the analyses and conclusions in the paper titled “modeling of streamflow in a 30-kilometer-long reach spanning 5 years using OpenFOAM 5.x”. The primary goal of this paper is to demonstrate that key streamflow properties such as water depth, flow velocity, and dynamic pressure in a natural river at 30-kilometer scale over 5 years can be reliably and efficiently modeled using the computational framework presented in this paper. To support the paper, various data types from remote sensing, field observations, and computational models are used. Specific details are described as follows. Firstly, the river bathymetry data was obtained from a Light Detection and Ranging (LiDAR) survey. This data is then converted to a triangulated surface format, STL, for mesh generation in OpenFOAM. The STL data can be found in Model_Setups/BaseCase_2013To2015/constant/triSurface. The OpenFOAM mesh generated using this STL file can be found in constant/polyMesh. Other model setups, boundary and initial conditions can be found in /system and /0.org under folder BaseCase_2013To2015. A similar data structure can also be found in BaseCase_2018To2019 for the simulations during 2018 and 2019. Secondly, the OpenFOAM simulations need the upstream discharge and water depth information at the upstream boundary to drive the model. These data are generated from a one-dimensional hydraulic model and the data can be found under the folder Model_Setups /1D model Mass1 data. The mass1_65.csv and mass1_191.csv files include the results of the 1D model at the model inlet and outlet, respectively. The Matlab source code Mass1ToOFBC20182019.m is used to convert these data into OpenFOAM boundary condition setups. With the above OpenFOAM model, it can generate data for water surface elevation, flow velocity, and dynamic pressure. In this paper, the water surface elevation was measured at 7 locations during different periods between 2011 and 2019. The exact survey locations (see Fig1_SurveyLocations.txt) can be found in folder Fig_1. The variation of water stage over time at the 7 locations can be found in folder /Observation_WSE. The data type include .txt, .csv, .xlsx, and .mat. The .mat data can be loaded by Matlab. We also measured the flow velocities at 12 cross-sections along the river. At each cross-section, we recorded the x, y locations, depth, three velocity components u,v,w. These data are saved to a Matlab format which can be found under folder /Observation_Velocity and /Fig_1. The relative locations of velocity survey locations to the river bathymetry can be found in Figure 1c. The water stage data at the 7 locations from OpenFOAM, 1D, and 2D hydraulic models are also provided to evaluate the long-term performance of 3D models vs 1D/2D models. The water stage data for the 7 locations from OpenFOAM have been saved to .mat format and can be found in /OpenFOAM_WSE. The water stage data from the 1D model are saved in .csv format and can be found in /Mass1_WSE. The water stage from the 2D model is saved as .mat format and can be found in / Mass2_WSE In addition, the OpenFOAM model outputs the information of hydrostatic and hydrodynamic pressure. They are saved as .mat format under folder /Fig_11/2013_1. As the files are too large, we only uploaded the data for January 2013. The area of different ratio of dynamic pressure to static pressure for all simulation range, i.e., 2013-2015, are saved to .mat format. They can be found in /Fig_11/PA. Further, the data of wall clock time versus the solution time of the OpenFOAM modeling are also saved to .mat format under folder /Fig_13/LogsMat. In summary, the data package contains seven data types, including .txt, .csv, .xlsx, .dat, .stl, .m, and .mat. The former 4 types can be directly open using a text editor or Microsoft Office. The .mat format needs to be read by Matlab. The Matlab source code .m files need to be run with Matlab. The OpenFOAM setups can be visualized in ParaView. The .stl file can be opened in ParaView or Blender. The data in subfolders Fig_1 to Fig_10 and Fig_12 are copied from the aforementioned data folders to generate specific figures for the paper. A readME.txt file is included in each subfolder to further describe how the data in each folder are generated and used to support the paper. Please use the data package's DOI to cite the data package. Please contact yunxiang.chen@pnnl.gov if you need more data related to the paper.

all data are in netCDF format for all figures and tables. This dataset is associated with the following publication: Glotfelty, T., K. Alapaty, J. He, P. Hawbecker, X. Song, and G. Zhang. Studying Scale Dependency of Aerosol Cloud Interactions using Scale-Aware Cloud Formulations. Monthly Weather Review. American Meteorological Society, Boston, MA, USA, 1-57, (2020).

PLEASE, READ THE README.TXT FILE

This document describes how to interpret the data and metadata files, and it is licensed under Creative Commons CC BY-NC-AS (https://creativecommons.org/licenses).

The file "primary_studies_final_set-DATA.csv" is a CSV file format and contains the raw data extracted from our systematic literature review primary studies. Such data were extracted based on the research questions defined for our study.
The file "primary_studies_final_set-METADATA.csv" is a CSV file format and contains the following:
- the first row contains two pieces of information: the data type, which might be original or reused;
- the second row contains the reused data URL/DOI, which should inform the URL or DOI from which the data was reused, or n/a if the data is original;
- the third row contains the date of data generation in the format mm/dd/yyyy;
- the fourth row contains 11 elements describing each of the fields of the file "primary_studies_final_set-DATA.csv": the study ID, title, objective, six research questions, and an observation field; and
- the fifth row describes the data type of each field of the file "primary_studies_final_set-DATA.csv".
The .bib files contain the bibtex entry for the final set of studies.
The license.txt file describes the Creative Commons license for this material.

We hope you have an excellent read!!

Cheers!
Thiago, Edson, and Avelino

Aggregated data set in .xlsx format of the qualitative questionnaire resultsThis dataset contains aggregated data accompanying SEFI Paper: Educating Future Robotics Engineers In Multidisciplinary Approaches In Robot Software Design in .xlsx format of the qualitative questionnaire results. The research objective for the study was: To investigate the student experience in the Multi-Disciplinary Project in the MSc Robotics in the academic year 2023-2024 using a questionnaire which was fielded in June and July 2023. The main research question was: What can be learned from student feedback and perceptions regarding the course’s Learning Objectives and the overall running of the course? The data was collected using a Qualtrics online survey and both qualitative and quantitative data such as student satisfaction with course components were collected. The work was reported in a conference paper: Van Der Niet, A., Claij, C., & Saunders-Smits, G. (2023). Educating Future Robotics Engineers In Multidisciplinary Approaches In Robot Software Design. European Society for Engineering Education (SEFI). DOI: 10.21427/W6WB-Z113

Tracking and stimulus data in pandas dataframe format supporting the findings of Shamash et al. Mice learn multi-step routes by memorizing subgoal locations (2021).

This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs).

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to capture expert knowledge from senior researchers in their research domain related to the SDG. The survey was open to the world, but mainly distributed to researchers from the Aurora Universities Network. The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.

17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey. The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs.

The survey contains the following questions (see inside dataset for exact wording):

• Are you familiar with this SDG?
  • Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.
• Suggest research papers that are relevant for this SDG (upload list)
  • This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)
• Select research papers that are relevant for this SDG (radio buttons: accept, reject)
  • A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.
• Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Suggest SDG related glossaries with relevant keywords (text fields: url)
  • Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.
• Suggest improvements for the current queries (text field: suggestions per target)
  • We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.

In the dataset root you'll find the following folders and files:

• /00-survey-input/
  • This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.
• /01-raw-data/
  • This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.
• /02-aggregated-data/
  • This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.
• /03-scripts/
  • This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.
• /04-processed-data/
  • This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.
• /images/
  • images of the results used in this README.md.
• LICENSE.md
  • terms and conditions for reusing this data.
• README.md
  • description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.

In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:

• SDG-survey-questions.pdf
  • This file contains the survey questions

  </li>
  <li><strong>SDG-survey-questions.doc</strong>
  <ul>
    <li>This file contains the survey questions</li>
  </ul>
  </li>
  <li><strong>SDG-survey-respondents-per-sdg.csv</strong>
  <ul>
    <li>Basic information about the survey and responses</li>
  </ul>
  </li>
  <li><strong>SDG-survey-city-heatmap.csv</strong>
  <ul>
    <li>Origin of the respondents per SDG survey</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications.txt</strong>
  <ul>
    <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong>
  <ul>
    <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-accepted.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-rejected.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-keywords.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-keywords.csv</strong>
  <ul>
    <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-keywords.csv</strong>
  <ul>
    <li>List of keywords researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-glossaries.csv</strong>
  <ul>
    <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-journals.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-journals.csv</strong>
  <ul>
    <li>As "selected-journals", this is the list of journals
  

The Global Paper-Based Laminates Market Size Was Worth USD 6.18 Billion in 2023 and Is Expected To Reach USD 9.07 Billion by 2032, CAGR of 4.36%.