The rise in remote work and digital transformation initiatives has accelerated the demand for robust and scalable solutions offered by the database, storage and backup software publishing industry. Cloud adoption has surged, with downstream businesses in finance and healthcare increasingly relying on cloud-based databases and storage systems to ensure accessibility and resilience. To capture demand, publishers have grown revenue through subscription-based offerings, which have expanded the industry's reach and provided recurring revenue over the past five years. Driven by a 47.9% surge in 2021, industry revenue has increased at a CAGR of 10.2% to reach $98.9 billion, including growth of 2.5% in 2025. Advancements in cloud and digital technology have paved the way for new freemium substitutes, reshaping industry competition and introducing operational challenges. As new, cost-effective solutions emerge, traditional publishers have faced the challenge of differentiating their offerings while maintaining profitability. Leading companies such as Microsoft and Oracle have responded with investments in compatibility capabilities and AI features that have been designed to retain users as more options become available. Combined with the emerging threat of cyber attacks, however, these investments have weighed on industry profitability as greater resources are now needed to support different initiatives. With freemium models here to stay, industry revenue growth will decelerate moving forward. Users are expected to demand free tiers among leading publishers, who have already deployed these subscription models at the cost of revenue growth. Despite these trends, however, publishers are expected to benefit from data center expansions and upgrades, which will provide them with the necessary infrastructure to develop next-generation AI and edge computing offerings. With billions of dollars being invested in these areas, industry revenue will be sustained and rise at a CAGR of 2.5% over the next five years to reach $112.0 billion in 2030.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

What is IPGOD?\r

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.\r \r \r

How do I use IPGOD?\r

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.\r \r \r

IP Data Platform\r

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform\r \r

References\r

\r The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.\r \r * Patents\r * Trade Marks\r * Designs\r * Plant Breeder’s Rights\r \r \r

Updates\r

\r

Tables and columns\r

\r Due to the changes in our systems, some tables have been affected.\r \r * We have added IPGOD 225 and IPGOD 325 to the dataset!\r * The IPGOD 206 table is not available this year.\r * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.\r \r

Data quality improvements\r

\r Data quality has been improved across all tables.\r \r * Null values are simply empty rather than '31/12/9999'.\r * All date columns are now in ISO format 'yyyy-mm-dd'.\r * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.\r * All tables are encoded in UTF-8.\r * All tables use the backslash \ as the escape character.\r * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

Winter Steelhead Distribution June 2012 Version This dataset depicts observation-based stream-level geographic distribution of anadromous winter-run steelhead trout, Oncorhynchus mykiss irideus (O. mykiss), in California. It was developed for the express purpose of assisting with steelhead recovery planning efforts. The distributions reported in this dataset were derived from a subset of the data contained in the Aquatic Species Observation Database (ASOD), a Microsoft Access multi-species observation data capture application. ASOD is an ongoing project designed to capture as complete a set of statewide inland aquatic vertebrate species observation information as possible. Please note: A separate distribution is available for summer-run steelhead. Contact information is the same as for the above. ASOD Observation data were used to develop a network of stream segments. These lines are developed by "tracing down" from each observation to the sea using the flow properties of USGS National Hydrography Dataset (NHD) High Resolution hydrography. Lastly these lines, representing stream segments, were assigned a value of either Anad Present (Anadromous present). The end result (i.e., this layer) consists of a set of lines representing the distribution of steelhead based on observations in the Aquatic Species Observation Database. This dataset represents stream reaches that are known or believed to be used by steelhead based on steelhead observations. Thus, it contains only positive steelhead occurrences. The absence of distribution on a stream does not necessarily indicate that steelhead do not utilize that stream. Additionally, steelhead may not be found in all streams or reaches each year. This is due to natural variations in run size, water conditions, and other environmental factors. The information in this data set should be used as an indicator of steelhead presence/suspected presence at the time of the observation as indicated by the 'Late_Yr' (Latest Year) field attribute. The line features in the dataset may not represent the maximum extent of steelhead on a stream; rather it is important to note that this distribution most likely underestimates the actual distribution of steelhead. This distribution is based on observations found in the ASOD database. The individual observations may not have occurred at the upper extent of anadromous occupation. In addition, no attempt was made to capture every observation of O. mykiss and so it should not be assumed that this dataset is complete for each stream. The distribution dataset was built solely from the ASOD observational data. No additional data (habitat mapping, barriers data, gradient modeling, etc.) were utilized to either add to or validate the data. It is very possible that an anadromous observation in this dataset has been recorded above (upstream of) a barrier as identified in the Passage Assessment Database (PAD). In the near future, we hope to perform a comparative analysis between this dataset and the PAD to identify and resolve all such discrepancies. Such an analysis will add rigor to and help validate both datasets. This dataset has recently undergone a review. Data source contributors as well as CDFG fisheries biologists have been provided the opportunity to review and suggest edits or additions during a recent review. Data contributors were notified and invited to review and comment on the handling of the information that they provided. The distribution was then posted to an intranet mapping application and CDFG biologists were provided an opportunity to review and comment on the dataset. During this review, biologists were also encouraged to add new observation data. This resulting final distribution contains their suggestions and additions. Please refer to "Use Constraints" section below.

The COKI Open Access Dataset measures open access performance for 142 countries and 5117 institutions and is available in JSON Lines format. The data is visualised at the COKI Open Access Dashboard: https://open.coki.ac/.

The COKI Open Access Dataset is created with the COKI Academic Observatory data collection pipeline, which fetches data about research publications from multiple sources, synthesises the datasets and creates the open access calculations for each country and institution.

Each week a number of specialised research publication datasets are collected. The datasets that are used for the COKI Open Access Dataset release include Crossref Metadata, Microsoft Academic Graph, Unpaywall and the Research Organization Registry.

After fetching the datasets, they are synthesised to produce aggregate time series statistics for each country and institution in the dataset. The aggregate timeseries statistics include publication count, open access status and citation count.

See https://open.coki.ac/data/ for the dataset schema. A new version of the dataset is deposited every week.

Code

• The COKI Academic Observatory data collection pipeline is used to create the dataset.
• The COKI OA Website Github project contains the code for the web app that visualises the dataset at open.coki.ac. It can be found on Zenodo here.

License
COKI Open Access Dataset © 2022 by Curtin University is licenced under CC BY 4.0.

Attributions
This work contains information from:

• Microsoft Academic Graph which is made available under the ODC Attribution Licence.
• Crossref Metadata via the Metadata Plus program. Bibliographic metadata is made available without copyright restriction and Crossref generated data under a CC0 licence. See metadata licence information for more details.
• Unpaywall. The Unpaywall Data Feed is used under license. Data is freely available from Unpaywall via the API, data dumps and as a data feed.
• Research Organization Registry which is made available under a CC0 licence.

This dataset is about books. It has 2 rows and is filtered where the book is Windows of opportunity : how nations make wealth. It features 7 columns including author, publication date, language, and book publisher.

Tech Companies - Merger and Acquisitions Dataset - Software Companies

This dataset contains the list of acquisitions made by the following companies:

Microsoft, Google, IBM, Hp, Apple, Amazon, Facebook, Twitter, eBay, Adobe, Citrix, Redhat, Blackberry, Disney

The attributes include the date, year, month of the acquisition, name of the company acquired, value or the cost of acquisition, business use-case of the acquisition, and the country from which the acquisition was made. The source of the dataset is Wikipedia, TechCrunch, and CrunchBase.

Interesting Tasks and Analysis Ideas

• Which company makes the acquisitions quickly
• What is the trend of business use-cases among the acquired companies throughout the years
• What can be forecasted for upcoming years in terms of acquisitions
• Predict who is likely to make next acquisitions and when

Microsoft Common Objects in Context (COCO) Dataset

The Common Objects in Context (COCO) dataset is a widely recognized collection designed to spur object detection, segmentation, and captioning research. Created by Microsoft, COCO provides annotations, including object categories, keypoints, and more. The model it a valuable asset for machine learning practitioners and researchers. Today, many model architectures are benchmarked against COCO, which has enabled a standard system by which architectures can be compared.

While COCO is often touted to comprise over 300k images, it's pivotal to understand that this number includes diverse formats like keypoints, among others. Specifically, the labeled dataset for object detection stands at 123,272 images.

The full object detection labeled dataset is made available here, ensuring researchers have access to the most comprehensive data for their experiments. With that said, COCO has not released their test set annotations, meaning the test data doesn't come with labels. Thus, this data is not included in the dataset.

The Roboflow team has worked extensively with COCO. Here are a few links that may be helpful as you get started working with this dataset:

• An introduction to the COCO dataset
• Weird images in COCO, and what that tells us about the utility and limits of COCO

Browse Microsoft Corp (MSFT) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

Origin: Options Price Reporting Authority

Supported data encodings: DBN, JSON, CSV Learn more

Supported market data schemas: MBP-1, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, TBBO, Trades, Statistics, Definition Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Context

I've been recently exploring Microsoft Azure and have been playing this game for the past 4 or so years. I am also a software developer by profession. I did a simple pipeline that gets data from the official Clash Royale API using (Python) Jupyter Notebooks and Azure VMs. I tried searching for public Clash Royale datasets, but the ones I saw don't quite have that much data from my perspective, so I decided to create one for the whole community.

I started pulling in the data at the beginning of the month of December until season 18 ended. This covers the season reset last December 07, and the latest balance changes last December 09. This dataset also contains ladder data for the new Legendary card Mother Witch.

The amount of data that I have, with the latest dataset, has ballooned to around 37.9 M distinct/ unique ladder matches that were (pseudo) randomly being pulled from a pool of 300k+ clans. If you think that this is A LOT, this could only be a percent of a percent (even lower) of the real amount of ladder battle data. It still may not reflect the whole population, also, the majority of my data are matches between players of 4000 trophies or more.

I don't see any reason for me not to share this to the public as the data is now considerably large that working on it and producing insights will take more than just a few hours of "hobby" time to do.

Feel free to use it on your own research and analysis, but don't forget to credit me.

Also, please don't monetize this dataset.

Stay safe. Stay healthy.

Happy holidays!

Content

Card Ids Master List is in the discussion, I also created a simple notebook to load the data and made a sample n=20 rows, so you can get an idea on what the fields are.

Inspiration

With this data, the following can possibly be answered 1. Which cards are the strongest? The weakest? 2. Which win-con is the most winning? 3. Which cards are always with a specific win-con? 4. When 2 opposing players are using maxed decks, which win-con is the most winning? 5. Most widely used cards? Win-Cons? 6. What are the different metas in different arenas and trophy ranges? 7. Is ladder matchmaking algorithm rigged? (MOST CONTROVERSIAL)

(and many more)

Implementation

I have 2 VMs running a total of 14 processes, and for each of these processes, I've divided a pool of 300k+ clans into the same number of groups. This went on 24/7, non-stop for the whole season. Each process will then randomize the list of clans it is assigned to and will iterate through each clan, and get that clan's members' ladder data. It is important to note that I also have a pool of 470 hand-picked clans that I always get data from, as these clans were the starting point that eventually enabled me to get the 300k+ clans. There are clans who have minimal ladder data, there are some clans who have A LOT.

To prevent out of memory exceptions, as my VMs are not really that powerful (I'm using Azure free credits), I've put on a time and limit of battles extracted per member.

My Clan and Handle

My account: https://royaleapi.com/player/89L2CLRP My clan: https://royaleapi.com/clan/J898GQ

Acknowledgements

Thank you to SUPERCELL for creating this FREEMIUM game that has tested countless people's patience, as well as the durability of countless mobile devices after being smashed against a wall, and thrown on the floor.

Thank you to Microsoft for Azure and free monthly credits

Thank you to Python and Jupyter notebooks.

Thank you Kaggle for hosting this dataset.

The dataset used in this study experiment was from the preliminary competition dataset of the 2018 Guangdong Industrial Intelligent Manufacturing Big Data Intelligent Algorithm Competition organized by Tianchi Feiyue Cloud (https://tianchi.aliyun.com/competition/entrance/231682/introduction). We have selected the dataset, removing images that do not meet the requirements of our experiment. All datasets have been classified for training and testing. The image pixels are all 2560×1960. Before training, all defects need to be labeled using labelimg and saved as json files. Then, all json files are converted to txt files. Finally, the organized defect dataset is detected and classified.Description of the data and file structureThis is a project based on the YOLOv8 enhanced algorithm for aluminum defect classification and detection tasks.All code has been tested on Windows computers with Anaconda and CUDA-enabled GPUs. The following instructions allow users to run the code in this repository based on a Windows+CUDA GPU system already in use.Files and variablesFile: defeat_dataset.zipDescription:SetupPlease follow the steps below to set up the project:Download Project RepositoryDownload the project repository defeat_dataset.zip from the following location.Unzip and navigate to the project folder; it should contain a subfolder: quexian_datasetDownload data1.Download data .defeat_dataset.zip2.Unzip the downloaded data and move the 'defeat_dataset' folder into the project's main folder.3. Make sure that your defeat_dataset folder now contains a subfolder: quexian_dataset.4. Within the folder you should find various subfolders such as addquexian-13, quexian_dataset, new_dataset-13, etc.softwareSet up the Python environment1.Download and install the Anaconda.2.Once Anaconda is installed, activate the Anaconda Prompt. For Windows, click Start, search for Anaconda Prompt, and open it.3.Create a new conda environment with Python 3.8. You can name it whatever you like; for example. Enter the following command: conda create -n yolov8 python=3.84.Activate the created environment. If the name is , enter: conda activate yolov8Download and install the Visual Studio Code.Install PyTorch based on your system:For Windows/Linux users with a CUDA GPU: bash conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forgeInstall some necessary libraries:Install scikit-learn with the command: conda install anaconda scikit-learn=0.24.1Install astropy with: conda install astropy=4.2.1Install pandas using: conda install anaconda pandas=1.2.4Install Matplotlib with: conda install conda-forge matplotlib=3.5.3Install scipy by entering: conda install scipy=1.10.1RepeatabilityFor PyTorch, it's a well-known fact:There is no guarantee of fully reproducible results between PyTorch versions, individual commits, or different platforms. In addition, results may not be reproducible between CPU and GPU executions, even if the same seed is used.All results in the Analysis Notebook that involve only model evaluation are fully reproducible. However, when it comes to updating the model on the GPU, the results of model training on different machines vary.Access informationOther publicly accessible locations of the data:https://tianchi.aliyun.com/dataset/public/Data was derived from the following sources:https://tianchi.aliyun.com/dataset/140666Data availability statementThe ten datasets used in this study come from Guangdong Industrial Wisdom Big Data Innovation Competition - Intelligent Algorithm Competition Rematch. and the dataset download link is https://tianchi.aliyun.com/competition/entrance/231682/information?lang=en-us. Officially, there are 4,356 images, including single blemish images, multiple blemish images and no blemish images. The official website provides 4,356 images, including single defect images, multiple defect images and no defect images. We have selected only single defect images and multiple defect images, which are 3,233 images in total. The ten defects are non-conductive, effacement, miss bottom corner, orange, peel, varicolored, jet, lacquer bubble, jump into a pit, divulge the bottom and blotch. Each image contains one or more defects, and the resolution of the defect images are all 2560×1920.By investigating the literature, we found that most of the experiments were done with 10 types of defects, so we chose three more types of defects that are more different from these ten types and more in number, which are suitable for the experiments. The three newly added datasets come from the preliminary dataset of Guangdong Industrial Wisdom Big Data Intelligent Algorithm Competition. The dataset can be downloaded from https://tianchi.aliyun.com/dataset/140666. There are 3,000 images in total, among which 109, 73 and 43 images are for the defects of bruise, camouflage and coating cracking respectively. Finally, the 10 types of defects in the rematch and the 3 types of defects selected in the preliminary round are fused into a new dataset, which is examined in this dataset.In the processing of the dataset, we tried different division ratios, such as 8:2, 7:3, 7:2:1, etc. After testing, we found that the experimental results did not differ much for different division ratios. Therefore, we divide the dataset according to the ratio of 7:2:1, the training set accounts for 70%, the validation set accounts for 20%, and the testing set accounts for 10%. At the same time, the random number seed is set to 0 to ensure that the results obtained are consistent every time the model is trained.Finally, the mean Average Precision (mAP) metric obtained from the experiment was tested on the dataset a total of three times. Each time the results differed very little, but for the accuracy of the experimental results, we took the average value derived from the highest and lowest results. The highest was 71.5% and the lowest was 71.1%, resulting in an average detection accuracy of 71.3% for the final experiment.All data and images utilized in this research are from publicly available sources, and the original creators have given their consent for these materials to be published in open-access formats.The settings for other parameters are as follows. epochs: 200，patience: 50，batch: 16，imgsz: 640，pretrained: true，optimizer: SGD，close_mosaic: 10，iou: 0.7，momentum: 0.937，weight_decay: 0.0005，box: 7.5，cls: 0.5，dfl: 1.5，pose: 12.0，kobj: 1.0，save_dir: runs/trainThe defeat_dataset.(ZIP)is mentioned in the Supporting information section of our manuscript. The underlying data are held at Figshare. DOI: 10.6084/m9.figshare.27922929.The results_images.zipin the system contains the experimental results graphs.The images_1.zipand images_2.zipin the system contain all the images needed to generate the manuscript.tex manuscript.

Summer Steelhead Distribution October 2009 Version This dataset depicts observation-based stream-level geographic distribution of anadromous summer-run steelhead trout, Oncorhynchus mykiss irideus (O. mykiss), in California. It was developed for the express purpose of assisting with steelhead recovery planning efforts. The distributions reported in this dataset were derived from a subset of the data contained in the Aquatic Species Observation Database (ASOD), a Microsoft Access multi-species observation data capture application. ASOD is an ongoing project designed to capture as complete a set of statewide inland aquatic vertebrate species observation information as possible. Please note: A separate distribution is available for winter-run steelhead. Contact information is the same as for the above. ASOD Observation data were used to develop a network of stream segments. These lines are developed by "tracing down" from each observation to the sea using the flow properties of USGS National Hydrography Dataset (NHD) High Resolution hydrography. Lastly these lines, representing stream segments, were assigned a value of either Anad Present (Anadromous present). The end result (i.e., this layer) consists of a set of lines representing the distribution of steelhead based on observations in the Aquatic Species Observation Database. This dataset represents stream reaches that are known or believed to be used by steelhead based on steelhead observations. Thus, it contains only positive steelhead occurrences. The absence of distribution on a stream does not necessarily indicate that steelhead do not utilize that stream. Additionally, steelhead may not be found in all streams or reaches each year. This is due to natural variations in run size, water conditions, and other environmental factors. The information in this data set should be used as an indicator of steelhead presence/suspected presence at the time of the observation as indicated by the 'Late_Yr' (Latest Year) field attribute. The line features in the dataset may not represent the maximum extent of steelhead on a stream; rather it is important to note that this distribution most likely underestimates the actual distribution of steelhead. This distribution is based on observations found in the ASOD database. The individual observations may not have occurred at the upper extent of anadromous occupation. In addition, no attempt was made to capture every observation of O. mykiss and so it should not be assumed that this dataset is complete for each stream. The distribution dataset was built solely from the ASOD observational data. No additional data (habitat mapping, barriers data, gradient modeling, etc.) were utilized to either add to or validate the data. It is very possible that an anadromous observation in this dataset has been recorded above (upstream of) a barrier as identified in the Passage Assessment Database (PAD). In the near future, we hope to perform a comparative analysis between this dataset and the PAD to identify and resolve all such discrepancies. Such an analysis will add rigor to and help validate both datasets. This dataset has recently undergone a review. Data source contributors as well as CDFG fisheries biologists have been provided the opportunity to review and suggest edits or additions during a recent review. Data contributors were notified and invited to review and comment on the handling of the information that they provided. The distribution was then posted to an intranet mapping application and CDFG biologists were provided an opportunity to review and comment on the dataset. During this review, biologists were also encouraged to add new observation data. This resulting final distribution contains their suggestions and additions. Please refer to "Use Constraints" section below.

The Radiocarbon dating laboratory of IRPA/KIK was founded in the 1960s. Initially dates were reported at more or less regular intervals in the journal Radiocarbon (Schreurs 1968). Since the advent of radiocarbon dating in the 1950s it had been a common practice amongst radiocarbon laboratories to publish their dates in so-called ‘date-lists’ that were arranged per laboratory. This was first done in the Radiocarbon Supplement of the American Journal of Science and later in the specialised journal Radiocarbon. In the course of time the latter, with the added subtitle An International Journal of Cosmogenic Isotope Research, became a regular scientific journal shifting focus from date-lists to articles. Furthermore the world-wide exponential increase of radiocarbon dates made it almost impossible to publish them all in the same journal, even more so because of the broad range of applications that use radiocarbon analysis, ranging from archaeology and art history to geology and oceanography and recently also biomedical studies.The IRPA/KIK database From 1995 onwards IRPA/KIK’s Radiocarbon laboratory started to publish its dates in small publications, continuing the numbering of the preceding lists in Radiocarbon. The first booklet in this series was “Royal Institute for Cultural Heritage Radiocarbon dates XV” (Van Strydonck et al. 1995), followed by three more volumes (XVI, XVII, XVIII). The next list (XIX, 2005) was no longer printed but instead handed out as a PDF file on CD-rom. The ever increasing number of dates and the difficulties in handling all the data, however, made us look for a more permanent and easier solution. In order to improve data management and consulting, it was thus decided to gather all our dates in a web-based database. List XIX was in fact already a Microsoft Access database that was converted into a reader friendly style and could also be printed as a PDF file. However a Microsoft Access database is not the most practical solution to make information publicly available. Hence the structure of the database was recreated in Mysql and the existing content was transferred into the corresponding fields. To display the records, a web-based front-end was programmed in PHP/Apache. It features a full-text search function that allows for partial word-matching. In addition the records can be consulted in PDF format. Old records from the printed date-lists as well as new records are now added using the same Microsoft Acces back-end, which is now connected directly to the Mysql database. The main problem with introducing the old data was that not all the current criteria were available in the past (e.g. stable isotope measurements). Furthermore since all the sample information is given by the submitter, its quality largely depends on the persons willingness to contribute as well as on the accuracy and correctness of the information he provides. Sometimes problems arrive from the fact that a certain investigation (like an excavation) is carried out over a relatively long period (sometimes even more than ten years) and is directed by different people or even institutions. This can lead to differences in the labeling procedure of the samples, but also in the interpretation of structures and artifacts and in the orthography of the site’s name. Finally the submitter might change address, while the names of institutions or even regions and countries might change as well (e.g.Zaire - Congo)

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

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[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

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License
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The DPJAIT dataset is made available under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/

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Summary
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DPJAIT DATASET – MULTIMODAL DATASET FOR INDOOR 3D DRONE TRACKING
The DPJAIT dataset has been designed for research on vision-based 3D drone tracking. The dataset consists of real measurements registered by a Vicon system containing a synchronized RGB multicamera set and motion capture acquisition, as well as simulated sequences obtained from a similar but virtual camera system created in Unreal Engine and AirSim simulator. The scene for the simulation sequences was prepared using a model of the Human Motion Lab (HML) at the Polish-Japanese Academy of Information Technology (PJAIT) in Bytom, Poland, in which real sequences were registered.

It is obligatory to cite the following paper in every work that uses the dataset:
J. Rosner, T. Krzeszowski, A. Świtoński, H. Josiński, W. Lindenheim-Locher, M. Zielinski, G. Paleta, M. Paszkuta, K. Wojciechowski: Multimodal dataset for indoor 3D drone tracking challenge, Scientific Data 12, 257 (2025). https://doi.org/10.1038/s41597-025-04521-y

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Data description
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The dataset consists of 13 simulated and 18 real sequences, which differ in the number of drones and their pattern of moving on scene.
The sequences were prepared in such a way that they could be used for various types of research. Some sequences contain a larger amount of drones but with limited motion or a smaller amount with a bigger degree of freedom. Additionally, some simulated sequences were generated based on measurements performed in a real laboratory, so they can be used to compare the results obtained for simulation and real sequences.

The simulated sequences were created using an environment based on the Unreal Engine and the AirSim plugin. It is an open-source project created by Microsoft to provide high-fidelity simulation of a variety of autonomous vehicles. Inside the environment, a scene based on the laboratory where real-life recordings took place was created. At the simulation scene, eight different cameras were placed. For some sequences, the stage size was enlarged twice the size of the HML laboratory to accommodate more flying drones without an issue of potential collisions between each of them. This allowed the generation of sequences with a large number of drones (up to 10), which was not possible to achieve in real conditions. Five different drone models were used in the simulations.
Most sequences contain data from eight cameras, except three sequences generated based on real sequences (S11_D4, S12_D3, S13_D3), which contain only data from four cameras. In addition, sequences S01_D2_A, S02_D4_A, and S03_D10_A contain images from the drone camera (First Person View, FPV), and ArUco markers placed on walls.

In real data scenarios, drones are manually controlled by skilled operators and tracked by a multi-modal acquisition system. Videos are registered by a set of four RGB cameras -- cam_1, cam_2, cam_3, and cam_4 -- with 1924x1082 resolution, located in the corners of the lab. Moreover, motion capture measurements are used to provide reference locations and orientations. It is achieved by tracking four markers -- A, B, C, and D -- attached to the top of the drones and forming an asymmetrical cross (see files MarkersCross_1.jpg, MarkersCross_2.jpg, and MarkersCrosses.pdf in "Additional_Files" folder). Details on how to establish the location and orientation in case of the known 3D coordinates of the markers are described by Lindenheim-Locher, W. et al. (Lindenheim-Locher, W. et al. YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System. Sensors 2023, 23, 6396. https://doi.org/10.3390/s23146396).
Moreover, to distinguish different drones visible at the same time instant, various lengths of the cross arms are applied (see MarkersCrosses.pdf in the "Additional_Files" folder). Ground truth data were acquired using a Vicon motion capture system. Synchronization and calibration of the motion capture system and video cameras were carried out using software and hardware provided by Vicon.

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Dataset structure
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* Additional_Files - directory with additional files
* lab_hml_map.pdf - scene diagram with camera placement
* MarkersCross_1.jpg - placement of markers on the drone
* MarkersCross_2.jpg - placement of markers on the drone
* MarkersCrosses.pdf - diagrams with dimensions of crosses with markers
* dl_data-ReadMe.txt - description of files with drones detections using the YOLOv5 model
* Real_Data_ArUco - additional files for sequences with ArUco markers
* ArUco-ReadMe.txt - file structure description
* images with the arrangement of markers on the walls
* Real_Data - 18 video sequences recorded in HML at the PJAIT.
* 4 recordings from cameras placed on the scene
* cameras_calibration.csv - cameras calibration data for OpenCV camera model
* .c3d - 3D coordinates of markers on crosses mounted on drones recorded by the Vicon system (see files MarkersCross_1.jpg, MarkersCross_2.jpg, and MarkersCrosses.pdf)
* dl_data - drones detections using the YOLOv5 model
* sequences with ArUco markers (_A in the name) additionally:
* FPV recordings from drones camera
* fpv_camera_data.csv - FPV camera parameters
* ArUco_3D.xlsx - data of ArUco markers placed on the scene
* _REF_ORI.csv - the drone's reference orientation corresponding to the data from the drone's camera
* _REF_POS.csv - the drone's reference position corresponding to the data from the drone's camera
* cameras_specification.csv - parameters of the cameras used
* Simulated_Data - 13 simulation video sequences.
* 4 to 8 recordings from cameras placed on the scene
* cameras_calibrationm.csv - cameras calibration data for OpenCV camera model
* _pos_25.csv - position and orientation of the drone
* _cam_25.csv (only sequences with ArUco markers - _A in the name) - position, orientation, and parameters of the drone's camera
* drone_masks.zip - extracted drone masks
* dl_data - drones detections using the YOLOv5 model
* sequences with ArUco markers (_A in the name) additionally:
* FPV recordings from the drone's camera
* markersAruco.csv - data of ArUco markers placed on the scene
* cameras_specification.csv - parameters of the cameras used

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Project participants
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Jakub Rosner
Tomasz Krzeszowski

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Acknowledgments
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This work has been supported by the National Centre for Research and Development within the research project "Innovative technology for creating multimedia events based on drone combat with synergy between the VR, AR and physical levels" in the years 2020–2023, Project No. POIR.01.02.00-00-0160/20.

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Further information
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For any questions, comments or other issues please contact Tomasz Krzeszowski

The data includs the distance, temperature, and Redshift of 93,060 nearby space objects, including stars, quasars, white dwarfs, and carbon stars. The objects' temperatures are between 671 and 99,575 K, and the distances of the objects are between 413.13 and 0.5 (mas). We have retrieved this information from almost 2,200,000 records. In addition, we have added two new columns for providing equivalent distances in the light year and peak frequency of the black body. We have excluded data from space objects whose temperature doesn’t exist and space objects whose Redshift is less than zero (Blueshift). All data are in a simple table in a Microsoft Access Database. Also, a copy of the data is represented in an excel file. A text file includes the basic script for downloading data.

For ethic add Ethics statements and Acknowledgments

Acknowledgments This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France 2000,A&AS,143,9 , "The SIMBAD astronomical database", Wenger et al.

Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks.

This dataset features intermediate mapping results for estimating material stocks in the CONUS (see related identifiers) on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), Microsoft building footprints, NLCD Impervious data, and crowd-sourced geodata (OSM). These data may also be useful on their own.

Provided layers @10m resolution
- Building height
- Building type
- Building area
- Impervious fraction
- street, and rail area
- Building and street climate zones
- County zones
- State masks
- EQUI7 correction factors

Spatial extent
This dataset covers the whole CONUS.

Temporal extent
The maps are representative for ca. 2018.

Data format
The data are organized in 100km x 100km tiles (EQUI7 grid), and mosaics are provided.

Further information
For further information, please see the main publication.
A web-visualization of the resulting dataset is available here.
Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication
D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. Nature Communications 14, 8014. https://doi.org/10.1038/s41467-023-43755-5

Funding
This research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

Acknowledgments
We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.