The Data Science Ontology is a research project of IBM Research AI and Stanford University Statistics. Its long-term objective is to improve the efficiency and transparency of collaborative, data-driven science.

Studyportals dataset provides a comprehensive set of data that can help students find Master's programs in Computer Science and IT worldwide. 🌍

This Dataset features a database of universities and colleges from around the world that offer Master's programs in these fields. Students can search for programs based on various criteria such as location, specialization, duration, and tuition fees.

• Once students find a program of interest, Studyportals provides detailed information about the program, including the curriculum, admission requirements, application deadlines, and tuition fees. Students can also read reviews and ratings from other students who have completed the program, which can help them make informed decisions about their education.

• Studyportals also offers a range of resources to help students prepare for their Master's program in Computer Science and IT. These resources include language courses, standardized test preparation courses, and career advice.

• Overall, Studyportals is a useful resource for students who are interested in pursuing a Master's program in Computer Science and IT, as it provides a comprehensive database of programs and offers a range of resources to help students prepare for their studies.

1. Times Higher Education Ranking (2018): The Times Higher Education (THE) ranking is one of the most prestigious university rankings in the world. It ranks universities based on various factors such as research, teaching quality, knowledge transfer, and international outlook. Some universities that may be included in this ranking are the University of Oxford (UK), the University of Cambridge (UK), and Stanford University (USA).

2. Shanghai Jiao Tong University Ranking (2017): The Shanghai Jiao Tong University (SJTU) ranking is another well-known university ranking system. It focuses mainly on research performance and ranks universities based on factors such as Nobel Prize winners, highly cited researchers, and publications in top journals. Some universities that may be included in this ranking are Harvard University (USA), the University of Tokyo (Japan), and the University of Toronto (Canada).

   1. TopUniversities Ranking (2018): The TopUniversities ranking is based on several factors such as academic reputation, employer reputation, and research impact. It is widely used by students and universities to compare different institutions. Some universities that may be included in this ranking are the Massachusetts Institute of Technology (USA), the University of Cambridge (UK), and the University of Melbourne (Australia).

3. U.S. News & World Report Ranking (2018): The U.S. News & World Report ranking focuses primarily on American universities and colleges, but it also includes some international institutions. It ranks universities based on factors such as research performance, student outcomes, and academic reputation. Some universities that may be included in this ranking are Princeton University (USA), the California Institute of Technology (USA), and the University of Oxford (UK).

"""When using Studyportals, students can search for universities based on their rankings in these systems and also filter by location, program, tuition fees, and other criteria. This can help students find universities that meet their specific needs and preferences."""

All of the datasets and the below description are quoted from Project - Data-driven prediction of battery cycle life before capacity degradation.

Objective

This dataset, used in our publication “Data-driven prediction of battery cycle life before capacity degradation”, consists of 124 commercial lithium-ion batteries cycled to failure under fast-charging conditions. These lithium-ion phosphate (LFP)/graphite cells, manufactured by A123 Systems (APR18650M1A), were cycled in horizontal cylindrical fixtures on a 48-channel Arbin LBT potentiostat in a forced convection temperature chamber set to 30°C. The cells have a nominal capacity of 1.1 Ah and a nominal voltage of 3.3 V.

The objective of this work is to optimize fast charging for lithium-ion batteries. As such, all cells in this dataset are charged with a one-step or two-step fast-charging policy. This policy has the format “C1(Q1)-C2”, in which C1 and C2 are the first and second constant-current steps, respectively, and Q1 is the state-of-charge (SOC, %) at which the currents switch. The second current step ends at 80% SOC, after which the cells charge at 1C CC-CV. The upper and lower cutoff potentials are 3.6 V and 2.0 V, respectively, which are consistent with the manufacturer’s specifications. These cutoff potentials are fixed for all current steps, including fast charging; after some cycling, the cells may hit the upper cutoff potential during fast charging, leading to significant constant-voltage charging. All cells discharge at 4C.

The dataset is divided into three “batches”, representing approximately 48 cells each. Each batch is defined by a “batch date”, or the date the tests were started. Each batch has a few irregularities, as detailed on the page for each batch.

The data is provided in two formats. For each batch, a MATLAB struct is available. The struct provides a convenient data container in which the data for each cycle is easily accessible. This struct can be loaded in either MATLAB or python (via the h5py package). Pandas dataframes can be generated via the provided code. Additionally, the raw data for each cell is available as a CSV file. Note that the CSV files occasionally exhibit errors in both test time and step time in which the test time resets to zero mid-cycle; these errors are corrected for in the structs.

The temperature measurements are performed by attaching a Type T thermocouple with thermal epoxy (OMEGATHERM 201) and Kapton tape to the exposed cell can after stripping a small section of the plastic insulation. Note that the temperature measurements are not perfectly reliable; the thermal contact between the thermocouple and the cell can may vary substantially, and the thermocouple sometimes loses contact during cycling.

Internal resistance measurements were obtained during charging at 80% SOC by averaging 10 pulses of ±3.6C with a pulse width of 30 ms (2017-05-12 and 2017-06-30) or 33 ms (2018-04-12).

The following repository contains some starter code to load the datasets in either MATLAB or python:

https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation

Low rate data used to generate figure 4:

• 2018-02-20_batchdata_updated_struct_errorcorrect.mat
• 2018-04-03_varcharge_batchdata_updated_struct_errorcorrect.mat

If using this dataset in a publication, please cite: Severson et al. Data-driven prediction of battery cycle life before capacity degradation. Nature Energy volume 4, pages 383–391 (2019).

**Batch - 2017-05-12**
Experimental design
- All cells were cycled with one-step or two-step charging policies. The charging time varies from ~8 to 13.3 minutes (0-80% SOC). There are generally two cells tested per policy, with the exception of 3.6C(80%).
- 1 minute and 1 second rests were placed after reaching 80% SOC during charging and after discharging, respectively.
-We cycle to 80% of nominal capacity (0.88 Ah).
- An initial C/10 cycle was performed in the beginning of each test.
- The cutoff currents for the constant-voltage steps were C/50 for both charge and discharge.
- The pulse width of the IR test is 30 ms.

Experimental notes
- The computer automatically restarted twice. As such, there are some time gaps in the data.
- The temperature control is somewhat inconsistent, leading to variability in the baseline chamber temperature.
- The tests in channels 4 and 8 did not successfully start and thus do not have data.
- The thermocouples for channels 15 and 16 were accidentally switched.

Data notes
- Cycle 1 data is not available in the struct. The sampling rate for this cycle was initially too high, so we excluded it from the data set to create more manageable file sizes.
- The cells in Channels 1, 2, 3, 5, and 6 (3.6C(80%) and 4C(80%) policies) were stopped at the end of this batch and resume...

The booming market for integrated medicine & engineering education is projected to reach $7.68 billion by 2033, growing at an 8% CAGR. This report analyzes market trends, key players (MIT, Stanford, etc.), and regional insights, covering biomedical engineering, health informatics, and more. Explore lucrative investment opportunities in this rapidly expanding sector.

This dataset is the result of a project to support policy making by providing insights on the availability and composition of education offer in four key digital domains: artificial intelligence, high performance computing, cybersecurity, and data science. Following a text mining methodology that captures the inclusion of advanced digital technologies in the programmes’ syllabus, we monitor the availability of masters’ programmes, bachelor’s programmes and short professional courses and study their characteristics. These include the scope or depth with which the digital content is taught (classified into broad or specialised), education fields in which digital technologies are embedded (e.g., Information and communication technologies, Business, administration and law), and the content areas covered by the programmes (e.g. robotics, machine learning). Also, we consider the overlap between the four domains, to identify complementarities and synergies in the academic offer of advanced digital technologies. The dataset covers yearly data, starting from the academic year 2019-2020 and ending in academic year 2023-24 (and will not be further updated). In order to provide comparison with other competing economies, the dataset covers the EU and its Member States plus six additional countries: the United Kingdom, Norway, Switzerland, Canada, the United States, and Australia. Results of the study have been used as reference in the European Artificial Intelligence Strategy, the White Paper on Artificial Intelligence – a European approach to excellence and trust, in the Stanford University’s Artificial Intelligence Index Report 2019 and 2021. These data have substantiated the assessment of the national Recovery and Resilience plans, and are used as input for the Digital Resilience Dashboard, among others.

This dataset was created from tweets taken from Twitter.

Metadata 1 - the polarity of the tweet (0 = negative, 2 = neutral, 4 = positive) 2 - the id of the tweet (2087) 3 - the date of the tweet (Sat May 16 23:58:44 UTC 2009) 4 - the query (lyx). If there is no query, then this value is NO_QUERY. 5 - the user that tweeted (robotickilldozr) 6 - the text of the tweet (Lyx is cool)

Sentiment140 was created by Alec Go, Richa Bhayani, and Lei Huang, who were Computer Science graduate students at Stanford University.

If you use this data, please cite Sentiment140 as your source.

This dataset contains metadata for methane flux sites in Version 1.0 of FLUXNET-CH4. The dataset also has seasonality parameters for select freshwater wetlands, which were extracted from the raw datasets published at https://fluxnet.org/data/fluxnet-ch4-community-product/. These data are used to analyze global methane flux seasonality patterns in the paper "FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands" by Delwiche et al.