The inD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:

1. 1. That the inD dataset comes „AS IS“, without express or implied warranty. Although every effort has been made to ensure accuracy, we (ika) do not accept any responsibility for errors or omissions.
2. 2. That you include a reference to the inD Dataset in any work that makes use of the inD dataset. For research papers, cite our preferred publication as listed on our website; for other media cite our preferred publication as listed on our website or link to the inD website.
3. 3. That you do not distribute the inD dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of the inD dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the inD dataset or something similar in character.
4. 4. That you may not use the inD dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
5. 5. That all rights not expressly granted to you are reserved by us.

The highD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:

1. 1. That the highD dataset comes „AS IS“, without express or implied warranty. Although every effort has been made to ensure accuracy, we (ika) do not accept any responsibility for errors or omissions.
2. 2. That you include a reference to the highD Dataset in any work that makes use of the highD dataset. For research papers, cite our preferred publication as listed on our website; for other media cite our preferred publication as listed on our website or link to the highD website.
3. 3. That you do not distribute the highD dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of the highD dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the highD dataset or something similar in character.
4. 4. That you may not use the highD dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
5. 5. That all rights not expressly granted to you are reserved by us.

The rounD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:

1. 1. That the rounD dataset comes „AS IS“, without express or implied warranty. Although every effort has been made to ensure accuracy, we (ika) do not accept any responsibility for errors or omissions.
2. 2. That you include a reference to the rounD Dataset in any work that makes use of the rounD dataset. For research papers, cite our preferred publication as listed on our website; for other media cite our preferred publication as listed on our website or link to the rounD website.
3. 3. That you do not distribute the rounD dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of the rounD dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the rounD dataset or something similar in character.
4. 4. That you may not use the rounD dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
5. 5. That all rights not expressly granted to you are reserved by us.

The uniD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:

1. 1. That the uniD dataset comes „AS IS“, without express or implied warranty. Although every effort has been made to ensure accuracy, we (fka) do not accept any responsibility for errors or omissions.
2. 2. That you include a reference to the uniD Dataset in any work that makes use of the uniD dataset. For research papers, cite our preferred publication as listed on our website; for other media cite our preferred publication as listed on our website or link to the uniD website.
3. 3. That you do not distribute the uniD dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of the uniD dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the uniD dataset or something similar in character.
4. 4. That you may not use the uniD dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
5. 5. That all rights not expressly granted to you are reserved by us.

Permitted Non-Commerical Use:

You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the uniD dataset appropriately and includes a citation of the uniD dataset paper.

You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the uniD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the uniD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the uniD dataset and train the published model using the uniD dataset to see if they can confirm the results.

Not Permitted Non-Commerical Use:

You are working at a company. You use the uniD dataset to develop a prototype or proof-of-concept. You use the prototype based on the uniD dataset until you have an internal dataset to replace the prototype based on the uniD dataset.

You train existing neural networks on the uniD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.

Permitted Non-Commerical Use:

You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the rounD dataset appropriately and includes a citation of the rounD dataset paper.

You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the rounD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the rounD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the rounD dataset and train the published model using the rounD dataset to see if they can confirm the results.

Not Permitted Non-Commerical Use:

You are working at a company. You use the rounD dataset to develop a prototype or proof-of-concept. You use the prototype based on the rounD dataset until you have an internal dataset to replace the prototype based on the rounD dataset.

You train existing neural networks on the rounD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.

Permitted Non-Commerical Use:

You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the inD dataset appropriately and includes a citation of the inD dataset paper.

You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the inD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the inD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the inD dataset and train the published model using the inD dataset to see if they can confirm the results.

Not Permitted Non-Commerical Use:

You are working at a company. You use the inD dataset to develop a prototype or proof-of-concept. You use the prototype based on the inD dataset until you have an internal dataset to replace the prototype based on the inD dataset.

You train existing neural networks on the inD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.

Permitted Non-Commerical Use:

You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the highD dataset appropriately and includes a citation of the highD dataset paper.

You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the highD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the highD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the highD dataset and train the published model using the highD dataset to see if they can confirm the results.

Not Permitted Non-Commerical Use:

You are working at a company. You use the highD dataset to develop a prototype or proof-of-concept. You use the prototype based on the highD dataset until you have an internal dataset to replace the prototype based on the highD dataset.

You train existing neural networks on the highD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.

The exiD dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:

1. 1. That the exiD dataset comes „AS IS“, without express or implied warranty. Although every effort has been made to ensure accuracy, we (fka) do not accept any responsibility for errors or omissions.
2. 2. That you include a reference to the exiD Dataset in any work that makes use of the exiD dataset. For research papers, cite our preferred publication as listed on our website; for other media cite our preferred publication as listed on our website or link to the exiD website.
3. 3. That you do not distribute the exiD dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of the exiD dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the exiD dataset or something similar in character.
4. 4. That you may not use the exiD dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
5. 5. That all rights not expressly granted to you are reserved by us.

Permitted Non-Commerical Use:

You are a (PhD) student or researcher at a university or similar institution. You use the dataset to train or parameterize your own algorithms. You don´t share the data within your research group. You publish the results in a publication (e.g. conference paper) or written thesis. Your publication includes only small parts of data taken from the dataset for purposes of illustration. Your publication mentions the exiD dataset appropriately and includes a citation of the exiD dataset paper.

You are a (PhD) researcher at a company. You experiment on the dataset using internal algorithms or models that are not used to provide a product or service to customers. You develop algorithms, model definitions and training code based on experiments on the exiD dataset. You do not share the dataset within your company. You submit a publication about your algorithms, model and/or results to a conference for review. You include a citation of the exiD dataset paper and mention the dataset. Your submission is accepted and you release your model or algorithm code to let others replicate the results. If there are researchers at a university or PhD students at a company, they can download the exiD dataset and train the published model using the exiD dataset to see if they can confirm the results.

Not Permitted Non-Commerical Use:

You are working at a company. You use the exiD dataset to develop a prototype or proof-of-concept. You use the prototype based on the exiD dataset until you have an internal dataset to replace the prototype based on the exiD dataset.

You train existing neural networks on the exiD dataset. You use the model weights and deploy them in a system you intend to use for current/future customers.