Nanopublications LDF server

Matches in Nanopublications for { ?s <http://purl.org/dc/terms/description> ?o ?g. }

• step description "@is_fairstep(label='contrast image by factor') def contrast_image(image, ratio): from PIL import Image, ImageEnhance new_image= image.convert('RGB') new_image = ImageEnhance.Contrast(image) new_image= new_image.enhance(ratio) return new_image " assertion.
• step description "@is_fairstep(label='Add blur to image') def blur(img): from PIL import Image, ImageFilter return img.filter(ImageFilter.BLUR) " assertion.
• step description "@is_fairstep(label='Overlay image with another (partly transparent) one') def overlay(bg_img, fg_img): from PIL import Image img = bg_img.copy() fg_img = fg_img.convert("RGBA") img.paste(fg_img, (0, 0), fg_img) return img " assertion.
• step description "@is_fairstep(label='add awesome filters') # Give it your own name: def add_awesome_filter(image_path): from PIL import Image, ImageFilter with Image.open(image_path) as im: new_image = im.filter(filter=ImageFilter.MinFilter) return new_image " assertion.
• step description "@is_fairstep(label='Convert image to grayscale') def rgb2gray_image(image): from PIL import Image new_image = image.convert('L') return new_image " assertion.
• step description "@is_fairstep(label='Add blur to image') def blur(img): from PIL import Image, ImageFilter return img.filter(ImageFilter.BLUR) " assertion.
• step description "@is_fairstep(label='contrast image by factor') def contrast_image(image, ratio): from PIL import Image, ImageEnhance new_image= image.convert('RGB') new_image = ImageEnhance.Contrast(image) new_image= new_image.enhance(ratio) return new_image " assertion.
• step description "@is_fairstep(label='Adjust the constrast of the final image') def image_constrast(img:float, factor:float) -> float: enhancer = ImageEnhance.Contrast(img) img_constrast = enhancer.enhance(factor) return img_constrast " assertion.
• plan description "@is_fairworkflow(label='A simple image processing workflow, include grayscale, negative, guassian blur, blend, constrast') def image_combined_workflow(img1, img2): img_combined = image_combined(img1, img2) return img_combined " assertion.
• step description "@is_fairstep(label='Put here a name of your awesome step (include the word awesome so others will find it)') # Give it your own name: def xiao_func1(image:float) -> float: from PIL import Image, ImageDraw, ImageFont chars = "wow, what a beautiful image!" width, height = image.size chars_x, chars_y = int(width/8), int(height/3) ttf = ImageFont.load_default() img_draw = ImageDraw.Draw(image) img_draw.text((chars_x, chars_y), chars, font=ttf, fill=(255,0,0)) image.show() return img_draw " assertion.
• step description "@is_fairstep(label='Convert image to grayscale') def rgb2gray_image(image): from PIL import Image new_image = image.convert('L') return new_image " assertion.
• step description "@is_fairstep(label='Add blur to image') def blur(img): from PIL import Image, ImageFilter return img.filter(ImageFilter.BLUR) " assertion.
• step description "@is_fairstep(label='contrast image by factor') def contrast_image(image, ratio): from PIL import Image, ImageEnhance new_image= image.convert('RGB') new_image = ImageEnhance.Contrast(image) new_image= new_image.enhance(ratio) return new_image " assertion.
• step description "@is_fairstep(label='Add blur to image') def blur(img): from PIL import Image, ImageFilter return img.filter(ImageFilter.BLUR) " assertion.
• plan description "@is_fairworkflow(label='Combines two images together by overlapping them') def my_image_overlap_workflow(image1, image2): blurred_img = blur(image1) overlapped_image = blend_image(blurred_img, image2) return overlapped_image " assertion.
• step description "@is_fairstep(label='An awesome function to flip an image from left to right') def my_awesome_flipping(image): from PIL import Image # Flip the image from left to right image_flipped = image.transpose(method=Image.FLIP_LEFT_RIGHT) return image_flipped " assertion.
• step description "@is_fairstep(label='An awesome function to flip an image from left to right') def my_awesome_flipping(image): from PIL import Image # Flip the image from left to right image_flipped = image.transpose(method=Image.FLIP_LEFT_RIGHT) return image_flipped " assertion.
• plan description "@is_fairworkflow(label='My awesome workflow: flip left to right image and then write awesome text on it') def my_awesome_workflow(im_in): flipped_image = my_awesome_flipping(im_in) im_out = add_text_into_image(flipped_image) return im_out " assertion.
• step description "@is_fairstep(label='Convert image to grayscale') def rgb2gray_image(image): from PIL import Image new_image = image.convert('L') return new_image " assertion.
• step description "@is_fairstep(label='Blend two images') def blend_image(im1, im2): from PIL import Image im_blended= Image.blend(im1, im2, alpha=0.5) return im_blended " assertion.
• step description "@is_fairstep(label='contrast image by factor') def contrast_image(image, ratio): from PIL import Image, ImageEnhance new_image= image.convert('RGB') new_image = ImageEnhance.Contrast(image) new_image= new_image.enhance(ratio) return new_image " assertion.
• step description "@is_fairstep(label='Convert image to grayscale') def rgb2gray_image(image): from PIL import Image new_image = image.convert('L') return new_image " assertion.
• step description "@is_fairstep(label='Blend two images') def blend_image(im1, im2): from PIL import Image im_blended= Image.blend(im1, im2, alpha=0.5) return im_blended " assertion.
• plan description "@is_fairworkflow(label='An image to sketch conversion workflow') def my_workflow1(img1): img2 = rgb2gray_image(img1) img3 = invert_colors(img2) img4 = blur(img3) img5 = blend_image(img2,img4) img6 = contrast_image(img5,1) return img6 " assertion.
• step description "@is_fairstep(label='Overlay image with another (partly transparent) one') def overlay(bg_img, fg_img): from PIL import Image img = bg_img.copy() fg_img = fg_img.convert("RGBA") img.paste(fg_img, (0, 0), fg_img) return img " assertion.
• step description "@is_fairstep(label='Step to add text (awesome world!) into an image') # Give it your own name: def add_text_into_image(image): from PIL import ImageDraw from PIL import Image ImageDraw.Draw( image).text( (0, image.height/2), # Coordinates 'Awesome world!', # Text (255, 255, 255) # Color ) return image " assertion.
• step description "@is_fairstep(label='Convert image to grayscale') def rgb2gray_image(image): from PIL import Image new_image = image.convert('L') return new_image " assertion.
• step description "@is_fairstep(label='Add blur to image') def blur(img): from PIL import Image, ImageFilter return img.filter(ImageFilter.BLUR) " assertion.
• step description "@is_fairstep(label='contrast image by factor') def contrast_image(image, ratio): from PIL import Image, ImageEnhance new_image= image.convert('RGB') new_image = ImageEnhance.Contrast(image) new_image= new_image.enhance(ratio) return new_image " assertion.
• step description "@is_fairstep(label='Add blur to image') def blur(img): from PIL import Image, ImageFilter return img.filter(ImageFilter.BLUR) " assertion.
• plan description "@is_fairworkflow(label='Blend two images') def wf_t2(img1, img2): blurred_image = blur(img1) transparent_image = white_to_transparency(img2) new_image = overlay(blurred_image, transparent_image) return new_image " assertion.
• step description "@is_fairstep(label='Step to add text (awesome world!) into an image') # Give it your own name: def add_text_into_image(image): from PIL import ImageDraw from PIL import Image ImageDraw.Draw( image).text( (0, image.height/2), # Coordinates 'Awesome world!', # Text (255, 255, 255) # Color ) return image " assertion.
• step description "@is_fairstep(label='Inverting the colors of an image') def invert_colors(img): from PIL import Image, ImageOps return ImageOps.invert(img) " assertion.
• step description "@is_fairstep(label='An awesome step for creating composite image by blending images using a transparency mask') def mask_image(image1,image2): from PIL import Image mask = Image.new("L", image1.size, 128) new_image = Image.composite(image1, image2, mask) return new_image " assertion.
• step description "@is_fairstep(label='Add blur to image') def blur(img): from PIL import Image, ImageFilter return img.filter(ImageFilter.BLUR) " assertion.
• plan description "@is_fairworkflow(label='YAIB: Yet Another Image Blender (licence:cc0,author:rsiebes, date:06-05-2021 )') def yaib_workflow(backgroundImg,foregroundImg): blendedImg = mask_image(blur(backgroundImg),foregroundImg) return blendedImg " assertion.
• step description "@is_fairstep(label='Add an awesome Watermark to your image (licence:cc0,author:rsiebes, date:06-05-2021 )') # Give it your own name: def my_watermark_step(image, text): from PIL import ImageFont from PIL import ImageDraw from PIL import Image watermark_image = image.copy() draw = ImageDraw.Draw(watermark_image) # add watermark draw.text((0, 0), text, (0, 0, 0)) # add watermark draw.text((0, 0), text, (255, 255, 255)) # new_image = .... return watermark_image " assertion.
• step description "@is_fairstep(label='Inverting the colors of an image') def invert_colors(img): from PIL import Image, ImageOps return ImageOps.invert(img) " assertion.
• step description "@is_fairstep(label='Blend two images') def blend_image(im1, im2): from PIL import Image im_blended= Image.blend(im1, im2, alpha=0.5) return im_blended " assertion.
• step description "@is_fairstep(label='contrast image by factor') def contrast_image(image, ratio): from PIL import Image, ImageEnhance new_image= image.convert('RGB') new_image = ImageEnhance.Contrast(image) new_image= new_image.enhance(ratio) return new_image " assertion.
• step description "@is_fairstep(label='Make white background of image transparent') def white_to_transparency(img): from PIL import Image img = img.convert("RGBA") data = img.getdata() new_data = [] for item in data: if item[0] == 255 and item[1] == 255 and item[2] == 255: new_data.append((255, 255, 255, 0)) else: new_data.append(item) img.putdata(new_data) return img " assertion.
• step description "@is_fairstep(label='Add blur to image') def blur(img): from PIL import Image, ImageFilter return img.filter(ImageFilter.BLUR) " assertion.
• plan description "@is_fairworkflow(label='puts an image infront of a second image') def overlay_in_front(back_img, front_img): blur_back = blur(back_img) trans_front = white_to_transparency(front_img) final = overlay(blur_back, trans_front) return final " assertion.
• step description "@is_fairstep(label='awesome smoothening of an image') # Give it your own name: def my_awesome_step(image): from PIL import Image # new_image = .... smoothenedImage = image.filter(ImageFilter.SMOOTH) new_image = image.filter(ImageFilter.SMOOTH_MORE) return new_image " assertion.
• plan description "@is_fairworkflow(label='flip and smooth an image') def my_awesome_workflow(im_in): im_half = my_awesome_step(im_in) im_out = my_awesome_flipper(im_half) return im_out " assertion.
• step description "@is_fairstep(label='Convert the RGB color image to grayscale') def image_RGB2Gray(img:float) -> float: image_gray = img.convert('L') return image_gray " assertion.
• step description "@is_fairstep(label='Apply a Gaussian blur to the negative from step 2') def image_Gaussian(img:float) -> float: img_gauss = img.filter(ImageFilter.GaussianBlur(radius = 5)) return img_gauss " assertion.
• step description "@is_fairstep(label='Adjust the constrast of the final image') def image_constrast(img:float, factor:float) -> float: enhancer = ImageEnhance.Contrast(img) img_constrast = enhancer.enhance(factor) return img_constrast " assertion.
• usage-of-linked-data-scopes description "Usage of the Linked Data Scopes ontology in a research project" assertion.
• plan description "@is_fairworkflow(label='A simple image processing workflow, include grayscale, negative, guassian blur, blend, constrast') def image_combined_workflow(img1, img2): img_combined = image_combined(img1, img2) return img_combined " assertion.
• step description "@is_fairstep(label='An awesome step for creating composite image by blending images using a transparency mask') def mask_image(image1,image2): from PIL import Image mask = Image.new("L", image1.size, 128) new_image = Image.composite(image1, image2, mask) return new_image " assertion.
• RA1FoHM9lwJ1XAV1eB871XcMAKfod73G_i4YtgoLpJVH0 description "All review comments were addressed and responded to and the formalization looks good." assertion.
• RAXkuXJ4IK10Ai9F39_tOFDy6ewi7znau6OQhUEXP4nPc description "The author has addressed the review comments and the formalization looks good." assertion.
• RAokVMmiZSbRh01diNeJLum4p13kUd-NZjGFuVtxVz4Bs description "The review comments were addressed and the updated formalization looks good." assertion.
• RA22JAQihYeiJkNIjvwnxLPmjuG74yPcRXpPyVX8DV6fA description "All review comments were addressed and the formalization looks good." assertion.
• RAyg4UgIVovBGia-hk4qEuRzOq14fcOlYAclC6YGQaVYU description "The review comments were addressed and the updated formalization looks good." assertion.
• RAbWbJCYlLhlYBDn9PVxdJP_WUbbi058aRcK-3sOJsRwY description "All review comments were addressed and the formalization looks good." assertion.
• RAn15vsPJEVdJvjNKtBPo_oadtjeP9oc3Si-69FiJ4poQ description "The review comments were addressed and the formalization looks good." assertion.
• usage-of-linked-data-scopes description "Usage of the Linked Data Scopes ontology in a research project" assertion.
• RAoo8EvTgfkxJw5SgZXbJvRl5nQG7ygeGaHp8Zud1U4Zw description "The review comments were addressed and the formalization looks good." assertion.
• RABzhulhaPhOzo9MxWxl230N72-azdlpMNwu_HtDqsuUc description "The authors have responded to the review comments and the formalization looks good." assertion.
• IADOPT.MacromoleculeConcentration description "The concentration of macromolecules in the immune system in response to bacteria in the gut of a me/cfs patient" assertion.
• IADOPT.AirAldrinConcentration description "Mass concentration of Aldrin in air (passive sampler PUF, ng per sampler)" assertion.
• online-version-of-nanobench description "An online version of Nanobench would allow users to use it via the web without having to install it locally." assertion.
• www.wikidata.org description "Wikidata is a free and open knowledge base that can be read and edited by both humans and machines. Wikidata acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wiktionary, Wikisource, and others." assertion.
• lov.linkeddata.es description "LOV is the main catalog of vocabularies published and used on the Web. LOV provides a choice of several hundreds of such vocabularies, based on quality requirements including URI stability and availability on the Web, use of standard formats and publication best practices, quality metadata and documentation, identifiable and trustable publication body, proper versioning policy." assertion.
• lov.linkeddata.es description "LOV is the main catalog of vocabularies published and used on the Web. LOV provides a choice of several hundreds of such vocabularies, based on quality requirements including URI stability and availability on the Web, use of standard formats and publication best practices, quality metadata and documentation, identifiable and trustable publication body, proper versioning policy." assertion.
• data.ign.fr description "French Mapping open data on administrative units and the register of coordinate systems." assertion.
• leiden-declaration description "This declaration expresses the support of an unprecedented effort to create a new Internet that is based on the FAIR guiding principles, open standards and protocols, and increased reliability and trustworthiness of data. In short, a new Internet that works as a truly meaningful data space." assertion.
• nanopub4arguments description "We could use nanopublications to structure arguments on the web such that we could automatically aggregate them and apply argumentation theories." assertion.
• prod-dekalog.inria.fr description "This dataset contains the results of the indexing done by IndeGx." assertion.
• www.wikidata.org description "Wikidata is a free and open knowledge base that can be read and edited by both humans and machines. Wikidata acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wiktionary, Wikisource, and others." assertion.
• www.wikidata.org description "Wikidata is a free, collaborative, multilingual, secondary database, collecting structured data to provide support for Wikipedia, Wikimedia Commons, the other wikis of the Wikimedia movement, and to anyone in the world." assertion.
• query.wikidata.org description "Wikidata is a free and open knowledge base that can be read and edited by both humans and machines." assertion.
• cbsmicrodata_metadata description "This knowledge graph was made for metadata of Statistics Netherlands (CBS) microdata (the data from individuals, companies or street level). It was a project for Knowledge Graph Course 2020 at Maastricht University. The goal of this project is to convert the descriptions of all CBS microdata sets into one knowledge graph with high-quality and comprehensive metadata so that the researchers can easily query the metadata, explore the relations among multiple datasets, and find the needed variables." assertion.
• cbsmicrodata_metadata description "This knowledge graph was made for metadata of Statistics Netherlands (CBS) microdata (the data from individuals, companies or street level). It was a project for Knowledge Graph Course 2020 at Maastricht University. The goal of this project is to convert the descriptions of all CBS microdata sets into one knowledge graph with high-quality and comprehensive metadata so that the researchers can easily query the metadata, explore the relations among multiple datasets, and find the needed variables." assertion.
• assertion description "<p>Such nanopublications use the concept of an AIDA sentence to express a (scientific) statement in an informal or semi-formal manner, which can be formally linked.</p> <p>AIDA sentences are:<p> <ul> <li><strong>Atomic:</strong> a sentence describing one thought that cannot be further broken down in a practical way</li> <li><strong>Independent:</strong> a sentence that can stand on its own, without external references like "this effect" or "we"</li> <li><strong>Declarative:</strong> a complete sentence ending with a full stop that could in theory be either true or false</li> <li><strong>Absolute:</strong> a sentence describing the core of a claim ignoring the (un)certainty about its truth and ignoring how it was discovered (no "probably" or "evaluation showed that"); typically in present tense</li> </ul> <p>You can find more information about AIDA sentences <a href="https://github.com/tkuhn/aida" target="_blank">here</a></p>" assertion.
• assertion description "<p>Such nanopublications use the concept of an AIDA sentence to express a (scientific) statement in an informal or semi-formal manner, which can be formally linked.</p> <p>AIDA sentences are:<p> <ul> <li><strong>Atomic:</strong> a sentence describing one thought that cannot be further broken down in a practical way</li> <li><strong>Independent:</strong> a sentence that can stand on its own, without external references like "this effect" or "we"</li> <li><strong>Declarative:</strong> a complete sentence ending with a full stop that could in theory be either true or false</li> <li><strong>Absolute:</strong> a sentence describing the core of a claim ignoring the (un)certainty about its truth and ignoring how it was discovered (no "probably" or "evaluation showed that"); typically in present tense</li> </ul> <p>You can find more information about AIDA sentences <a href="https://github.com/tkuhn/aida" target="_blank">here</a></p>" assertion.
• assertion description "This nanopublication expresses a causal (e.g. "A caused B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between two things." assertion.
• assertion description "This nanopublication expresses a causal (e.g. "A caused B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between two things." assertion.
• assertion description "<p>Such nanopublications expresse a causal (e.g. "A caused B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between two things.</p> <p>Note that the left-hand side element (subject) as well as the right-hand side one (object) need to be <strong>individuals</strong> that exist just once, like <em>the French revolution</em> or <em>the Internet</em>, and <strong>not classes</strong> that stand for an entire collection of things, like <em>city</em> or <em>headache</em>.</p>" assertion.
• assertion description "<p>Such nanopublications define a new named individual. Such a named individual represets a single concrete or abstract thing that is in some way relevant. Examples are the planet Mars, Marie Curie, or the Universal Declaration of Human Rights.</p> <p>Such individuals do <strong>not</strong> represent sets of things, like humans, diseases, or ideas. Such concepts representing sets should be defined with the template for classes.</p>" assertion.
• pluto description "Pluto is a dwarf planet in the Kuiper belt beyond the orbit of Neptune." assertion.
• assertion description "<p>Such nanopublications define a new named individual. Such a named individual represets a single concrete or abstract thing that is in some way relevant. Examples are the planet Mars, Marie Curie, or the Universal Declaration of Human Rights.</p> <p>Such individuals do <strong>not</strong> represent sets of things, like humans, diseases, or ideas. Such concepts representing sets should be defined with the template for classes.</p>" assertion.
• assertion description "<p>Such nanopublications expresse a causal (e.g. "A caused B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between two things.</p> <p>Note that the left-hand side element (subject) as well as the right-hand side one (object) need to be <strong>individuals</strong> that exist just once, like <em>the French revolution</em> or <em>the Internet</em>, and <strong>not classes</strong> that stand for an entire collection of things, like <em>city</em> or <em>headache</em>.</p>" assertion.
• assertion description "<p>Such nanopublications expresse a causal (e.g. "A caused B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between the instances of two classes.</p> <p>The subject and object are classes, and the relations attach to the instances of these classes. As defined in this, template, the chosen relation does not have to apply to all instances of the classes, but they only need to "tend to" have such a relation. This aspect of "tending to" is deliberately left underspecified.</p> <p>For the above reasons, all relations listed here are named according to the pattern "has instances that tend to ... instances of".</p> <p>For more precise statements that formally express the degree of "tending to", use the super-pattern template instead.</p>" assertion.
• assertion description "<p>Such nanopublications expresse a causal (e.g. "A caused B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between the instances of two classes.</p> <p>The subject and object are classes, and the relations attach to the instances of these classes. As defined in this, template, the chosen relation does not have to apply to all instances of the classes, but they only need to "tend to" have such a relation. This aspect of "tending to" is deliberately left underspecified.</p> <p>For the above reasons, all relations listed here are named according to the pattern "has instances that tend to ... instances of".</p> <p>For more precise statements that formally express the degree of "tending to", use the super-pattern template instead.</p>" assertion.
• assertion description "<p>This nanopublication makes a statement that in the context of a thing of a certain type (e.g. a mouse), another thing of a certain type (e.g. a gene) tends to have a relation (such as causing) to another thing of a certain type (e.g. a disease) in the same context.</p> <p>The technical background of the super-pattern can be found in <a href="https://doi.org/10.1145/3460210.3493561">this publication</a>.</p>" assertion.
• assertion description "<p>Such nanopublications expresse a causal (e.g. "A causes B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between the instances of two classes.</p> <p>The subject and object are classes, and the relations attach to the instances of these classes. As defined in this, template, the chosen relation does not have to apply to all instances of the classes, but they are only stated to "tend to" have such a relation. This aspect of "tending to" is deliberately left underspecified.</p> <p>For the above reasons, all relations listed here are named according to the pattern "has instances that tend to ... instances of".</p> <p>For more precise statements that formally express the degree of "tending to", use the super-pattern template instead.</p>" assertion.
• NanoSession1 description "The first Nano Session on 28 February 2023 features short talks by Jorrit Poelen and Tobias Kuhn." assertion.
• assertion description "The creator of such a nanopublication declares to have participated in the specified event." assertion.
• assertion description "<p>This nanopublication makes a statement that in the context of a thing of a certain type (e.g. a mouse), another thing of a certain type (e.g. a gene) tends to have a relation (such as causing) to another thing of a certain type (e.g. a disease) in the same context.</p> <p>Note that the context is <strong>not</strong> a reference to a general topic or field. It denotes the class for the context in which the relations happen. If each relation is happening in a human, for example, such as a certain condition leading to a certain symptom, then the context class should be 'human'.</p> <p>The technical background of the super-pattern can be found in <a href="https://doi.org/10.1145/3460210.3493561">this publication</a>.</p>" assertion.
• assertion description "<p>This nanopublication makes a statement that in the context of a thing of a certain type (e.g. a mouse), another thing of a certain type (e.g. a gene) tends to have a relation (such as causing) to another thing of a certain type (e.g. a disease) in the same context.</p> <p>Note that the context is <strong>not</strong> a reference to a general topic or field. It denotes the class for the context in which the relations happen. If each relation is happening in a human, for example, such as a certain condition leading to a certain symptom, then the context class should be 'human'.</p> <p>The technical background of the super-pattern can be found in <a href="https://doi.org/10.1145/3460210.3493561">this publication</a>.</p>" assertion.
• assertion description "Such a nanopublication includes a text that refers to an existing paper or other document, such as stating an agreement or a correction. The reference to the existing paper is based on the <a href="https://sparontologies.github.io/cito/current/cito.html" target="_blank">Citation Typing Ontology (CiTO)</a>." assertion.
• assertion description "<p>Such nanopublications use the concept of an AIDA sentence to express a (scientific) statement in an informal or semi-formal manner, which can be formally linked.</p> <p>AIDA sentences are:<p> <ul> <li><strong>Atomic:</strong> a sentence describing one thought that cannot be further broken down in a practical way</li> <li><strong>Independent:</strong> a sentence that can stand on its own, without external references like "this effect" or "we"</li> <li><strong>Declarative:</strong> a complete sentence ending with a full stop that could in theory be either true or false</li> <li><strong>Absolute:</strong> a sentence describing the core of a claim ignoring the (un)certainty about its truth and ignoring how it was discovered (no "probably" or "evaluation showed that"); typically in present tense</li> </ul> <p>You can find more information about AIDA sentences <a href="https://github.com/tkuhn/aida" target="_blank">here</a>.</p>" assertion.
• assertion description "<p>Such a nanopublication defines a new class. Classes represent sets of concrete or abstract things, and are by convention named with singular nouns (or noun phrases) like 'human', 'cardiovascular disease', or 'approach'.</p> <p>If the term you want to define does not refer to a set of things but a single instnace, such as the planet Mars or Marie Curie, define them with the template for individuals instead.</p>" assertion.
• assertion description "<p>Such nanopublications define a new named individual. Such a named individual represets a single concrete or abstract thing that is in some way relevant. Examples are the planet Mars, Marie Curie, or the Universal Declaration of Human Rights.</p> <p>Such individuals do <strong>not</strong> represent sets of things, like humans, diseases, or ideas. Such concepts representing sets should be defined with the template for classes.</p>" assertion.
• assertion description "<p>Such a nanopublication defines a new class. Classes represent sets of concrete or abstract things, and are by convention named with singular nouns (or noun phrases) like 'human', 'cardiovascular disease', or 'approach'.</p> <p>If the term you want to define does not refer to a set of things but a single instnace, such as the planet Mars or Marie Curie, define them with the template for individuals instead.</p>" assertion.
• assertion description "<p>Such nanopublications express a causal (e.g. "A causes B"), spatio-temporal (e.g. "A is included in B"), or comparative (e.g. "A is larger than B") relation between the instances of two classes.</p> <p>The subject and object are classes, and the relations attach to the instances of these classes. As defined in this, template, the chosen relation does not have to apply to all instances of the classes, but they are only stated to "tend to" have such a relation. This aspect of "tending to" is deliberately left underspecified.</p> <p>For the above reasons, all relations listed here are named according to the pattern "has instances that tend to ... instances of".</p> <p>For more precise statements that formally express the degree of "tending to", use the super-pattern template instead.</p>" assertion.
• assertion description "Such a nanopublication declares that its creator is on the editorial board for a given journal." assertion.

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