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RDF content negotiation

For application/rdf+xml unfortunately, we don't have anything, but it is actually possible today to get JSON-LD back in the following manner:

For DOIs, DataCite JSON-LD format:

$ curl -L  -H "Accept: application/ld+json" "https://doi.org/10.5281/zenodo.3922812"
{
  "@context": "http://schema.org",
  "@type": "ScholarlyArticle",
  "@id": "https://doi.org/10.5281/zenodo.3922812",
  "url": "https://zenodo.org/record/3922812",
  "additionalType": "Taxonomic treatment",
  "name": "Cancer cristatus Linnaeus, 1758, spec. nov.",
  "author": {
    "name": "Carolus Linnaeus",
    "givenName": "Carolus",
    "familyName": "Linnaeus"
  },
  "description": "Cancer cristatus [spec. nov.]C. brachyurus, thorace aculeato: rostro bifido cristato, manibus teretibus.<i>Habitat in Oceano</i> Asiatico.",
  "license": [
    "https://creativecommons.org/publicdomain/zero/1.0/legalcode",
    "info:eu-repo/semantics/openAccess"
  ],
  "keywords": "Biodiversity, Taxonomy, Animalia, Arthropoda, Malacostraca, Decapoda, Cancridae, Cancer, Cancer cristatus",
  "datePublished": "1758-12-31",
  "isPartOf": [
    {
      "@id": "https://doi.org/10.5962/bhl.title.542",
      "@type": "ScholarlyArticle"
    },
    {
      "@id": "http://zenodo.org/record/3922206",
      "@type": "ScholarlyArticle"
    },
    {
      "@id": "http://publication.plazi.org/id/3F896AFC21C459639D0D71D9EDB8875C",
      "@type": "ScholarlyArticle"
    },
    {
      "@id": "http://zoobank.org/2C6327E1-5560-4DB4-B9CA-76A0FA03D975",
      "@type": "ScholarlyArticle"
    },
    {
      "@id": "https://zenodo.org/communities/biosyslit",
      "@type": "CreativeWork"
    }
  ],
  "schemaVersion": "http://datacite.org/schema/kernel-4",
  "periodical": {
    "@type": "Series",
    "identifier": "10.5962/bhl.title.542",
    "identifierType": "DOI"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Zenodo"
  },
  "provider": {
    "@type": "Organization",
    "name": "datacite"
  }
}

For Zenodo REST API URLs, our own generated JSON-LD:

$ curl -i -L "https://zenodo.org/api/records/3922812?prettyprint=1" -H "Accept: application/ld+json"
{
  "@context": "https://schema.org/",
  "@id": "https://doi.org/10.5281/zenodo.3922812",
  "@type": "ScholarlyArticle",
  "creator": [
    {
      "@type": "Person",
      "name": "Linnaeus, Carolus"
    }
  ],
  "datePublished": "1758-12-31",
  "description": "<p>Cancer cristatus [spec. nov.]</p><p>C. brachyurus, thorace aculeato: rostro bifido cristato, manibus teretibus.</p><p><i>Habitat in Oceano</i> Asiatico. </p>",
  "headline": "Cancer cristatus Linnaeus, 1758, spec. nov.",
  "identifier": "https://doi.org/10.5281/zenodo.3922812",
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg",
  "isPartOf": [
    {
      "@id": "https://doi.org/10.5962/bhl.title.542",
      "@type": "CreativeWork"
    },
    {
      "@id": "http://zenodo.org/record/3922206",
      "@type": "CreativeWork"
    },
    {
      "@id": "http://publication.plazi.org/id/3F896AFC21C459639D0D71D9EDB8875C",
      "@type": "CreativeWork"
    },
    {
      "@id": "http://zoobank.org/2C6327E1-5560-4DB4-B9CA-76A0FA03D975",
      "@type": "CreativeWork"
    }
  ],
  "keywords": [
    "Biodiversity",
    "Taxonomy",
    "Animalia",
    "Arthropoda",
    "Malacostraca",
    "Decapoda",
    "Cancridae",
    "Cancer",
    "Cancer cristatus"
  ],
  "license": "https://creativecommons.org/publicdomain/zero/1.0/legalcode",
  "name": "Cancer cristatus Linnaeus, 1758, spec. nov.",
  "sameAs": [
    "http://zoobank.org/EBACFF97-D7C1-4BA3-913D-EBF13E018913",
    "http://treatment.plazi.org/id/B9A356279C9C1CF12CEED620D3B80ED9"
  ],
  "url": "https://zenodo.org/record/3922812"
}

You will notice differences in the outputs, and the reason is that the DOI-based request is handled on the DataCite side, which serializes the available DataCite XML we register for each DOI, to whatever JSON-LD representation they have mapped.

The Zenodo URL request on the other hand serves JSON-LD generated on the Zenodo side (i.e. we fully control the output). I believe there are many improvements to be made to the format (e.g. adding all the DarwinCore vocabulary fields we capture).

camwebb

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Pull request review commentinveniosoftware/cookiecutter-invenio-instance

isort: fix compatibility with isort v5

  pipenv check && \ pipenv run pydocstyle {{ cookiecutter.package_name }} tests docs && \-pipenv run isort -rc -c -df && \+pipenv run isort -c --df && \

I'm also generally in favor of using the expanded/full name of options in scripts, since it's a form of documentation compared to the "cryptic" short versions which are nice for being quick when typing on an everyday basis.

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records: improve model + refactor code - switch from MultinomialNB to RandomForestClassifier - rewrite data handling from list to pandas DataFrame - refactor repository between notebooks and python scripts

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[WIP] records: improve model + refactor code
  • switch from MultinomialNB to RandomForestClassifier
  • rewrite data handling from list to pandas DataFrame
  • refactor repository between notebooks and python scripts
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[WIP] records: improve model + refactor code

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Pull request review commentinveniosoftware/invenio-oauthclient

contrib: fix orcid configuration

                               'show_login': 'true'},         base_url='https://pub.orcid.org/v1.2/',         request_token_url=None,-        access_token_url="https://api.orcid.org/oauth/token",+        access_token_url="https://pub.orcid.org/oauth/token",

Tested locally with access_token_url='https://orcid.org/oauth/token' and everything works as expected

jma

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Pull request review commentinveniosoftware/invenio-oauthclient

contrib: fix orcid configuration

                               'show_login': 'true'},         base_url='https://pub.orcid.org/v1.2/',         request_token_url=None,-        access_token_url="https://api.orcid.org/oauth/token",+        access_token_url="https://pub.orcid.org/oauth/token",

And apparently, it seems that we're all wrong :)

Not sure what version we're using but according to the article the {pub,api}.orcid.org endpoints are deprecated.

jma

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Pull request review commentinveniosoftware/invenio-oauthclient

contrib: fix orcid configuration

                               'show_login': 'true'},         base_url='https://pub.orcid.org/v1.2/',         request_token_url=None,-        access_token_url="https://api.orcid.org/oauth/token",+        access_token_url="https://pub.orcid.org/oauth/token",

Indeed, it was changed here. In Zenodo production config we have the old pub.orcid.org. At the ORCID docs though they use the base URL for OAuth requests...

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Bump tensorflow from 1.3.0 to 1.15.2 Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 1.3.0 to 1.15.2. - [Release notes](https://github.com/tensorflow/tensorflow/releases) - [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md) - [Commits](https://github.com/tensorflow/tensorflow/compare/v1.3.0...v1.15.2) Signed-off-by: dependabot[bot] <support@github.com>

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Bump tensorflow from 1.3.0 to 1.15.2 dependencies

Bumps tensorflow from 1.3.0 to 1.15.2. <details> <summary>Release notes</summary>

Sourced from tensorflow's releases.

TensorFlow 1.15.2

Release 1.15.2

Bug Fixes and Other Changes

TensorFlow 1.15.0

Release 1.15.0

This is the last 1.x release for TensorFlow. We do not expect to update the 1.x branch with features, although we will issue patch releases to fix vulnerabilities for at least one year.

Major Features and Improvements

  • As announced, tensorflow pip package will by default include GPU support (same as tensorflow-gpu now) for the platforms we currently have GPU support (Linux and Windows). It will work on machines with and without Nvidia GPUs. tensorflow-gpu will still be available, and CPU-only packages can be downloaded at tensorflow-cpu for users who are concerned about package size.
  • TensorFlow 1.15 contains a complete implementation of the 2.0 API in its compat.v2 module. It contains a copy of the 1.15 main module (without contrib) in the compat.v1 module. TensorFlow 1.15 is able to emulate 2.0 behavior using the enable_v2_behavior() function. This enables writing forward compatible code: by explicitly importing either tensorflow.compat.v1 or tensorflow.compat.v2, you can ensure that your code works without modifications against an installation of 1.15 or 2.0.
  • EagerTensor now supports numpy buffer interface for tensors.
  • Add toggles tf.enable_control_flow_v2() and tf.disable_control_flow_v2() for enabling/disabling v2 control flow.
  • Enable v2 control flow as part of tf.enable_v2_behavior() and TF2_BEHAVIOR=1.
  • AutoGraph translates Python control flow into TensorFlow expressions, allowing users to write regular Python inside tf.function-decorated functions. AutoGraph is also applied in functions used with tf.data, tf.distribute and tf.keras APIS.
  • Adds enable_tensor_equality(), which switches the behavior such that:
    • Tensors are no longer hashable.
    • Tensors can be compared with == and !=, yielding a Boolean Tensor with element-wise comparison results. This will be the default behavior in 2.0.
  • Auto Mixed-Precision graph optimizer simplifies converting models to float16 for acceleration on Volta and Turing Tensor Cores. This feature can be enabled by wrapping an optimizer class with tf.train.experimental.enable_mixed_precision_graph_rewrite().
  • Add environment variable TF_CUDNN_DETERMINISTIC. Setting to "true" or "1" forces the selection of deterministic cuDNN convolution and max-pooling algorithms. When this is enabled, the algorithm selection procedure itself is also deterministic.
  • TensorRT
    • Migrate TensorRT conversion sources from contrib to compiler directory in preparation for TF 2.0.
    • Add additional, user friendly TrtGraphConverter API for TensorRT conversion.
    • Expand support for TensorFlow operators in TensorRT conversion (e.g. Gather, Slice, Pack, Unpack, ArgMin, ArgMax,DepthSpaceShuffle).
    • Support TensorFlow operator CombinedNonMaxSuppression in TensorRT conversion which significantly accelerates object detection models.

Breaking Changes

  • Tensorflow code now produces 2 different pip packages: tensorflow_core containing all the code (in the future it will contain only the private implementation) and tensorflow which is a virtual pip package doing forwarding to tensorflow_core (and in the future will contain only the public API of tensorflow). We don't expect this to be breaking, unless you were importing directly from the implementation.
  • TensorFlow 1.15 is built using devtoolset7 (GCC7) on Ubuntu 16. This may lead to ABI incompatibilities with extensions built against earlier versions of TensorFlow.
  • Deprecated the use of constraint= and .constraint with ResourceVariable.
  • tf.keras:
    • OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.
    • tf.keras.model.save_model and model.save now defaults to saving a TensorFlow SavedModel.
    • keras.backend.resize_images (and consequently, keras.layers.Upsampling2D) behavior has changed, a bug in the resizing implementation was fixed.
    • Layers now default to float32, and automatically cast their inputs to the layer's dtype. If you had a model that used float64, it will probably silently use float32 in TensorFlow2, and a warning will be issued that starts with Layer "layer-name" is casting an input tensor from dtype float64 to the layer's dtype of float32. To fix, either set the default dtype to float64 with tf.keras.backend.set_floatx('float64'), or pass dtype='float64' to each of the Layer constructors. See tf.keras.layers.Layer for more information.
    • Some tf.assert_* methods now raise assertions at operation creation time (i.e. when this Python line executes) if the input tensors' values are known at that time, not during the session.run(). When this happens, a noop is returned and the input tensors are marked non-feedable. In other words, if they are used as keys in feed_dict argument to session.run(), an error will be raised. Also, because some assert ops don't make it into the graph, the graph structure changes. A different graph can result in different per-op random seeds when they are not given explicitly (most often).

Bug Fixes and Other Changes

  • tf.estimator:
    • tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with model.load_weights.
    • Fix tests in canned estimators.
    • Expose Head as public API.
    • Fixes critical bugs that help with DenseFeatures usability in TF2 </tr></table> ... (truncated) </details> <details> <summary>Changelog</summary>

Sourced from tensorflow's changelog.

Release 1.15.2

Bug Fixes and Other Changes

Release 2.1.0

TensorFlow 2.1 will be the last TF release supporting Python 2. Python 2 support officially ends an January 1, 2020. As announced earlier, TensorFlow will also stop supporting Python 2 starting January 1, 2020, and no more releases are expected in 2019.

Major Features and Improvements

  • The tensorflow pip package now includes GPU support by default (same as tensorflow-gpu) for both Linux and Windows. This runs on machines with and without NVIDIA GPUs. tensorflow-gpu is still available, and CPU-only packages can be downloaded at tensorflow-cpu for users who are concerned about package size.
  • Windows users: Officially-released tensorflow Pip packages are now built with Visual Studio 2019 version 16.4 in order to take advantage of the new /d2ReducedOptimizeHugeFunctions compiler flag. To use these new packages, you must install "Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019", available from Microsoft's website here.
    • This does not change the minimum required version for building TensorFlow from source on Windows, but builds enabling EIGEN_STRONG_INLINE can take over 48 hours to compile without this flag. Refer to configure.py for more information about EIGEN_STRONG_INLINE and /d2ReducedOptimizeHugeFunctions.
    • If either of the required DLLs, msvcp140.dll (old) or msvcp140_1.dll (new), are missing on your machine, import tensorflow will print a warning message.
  • The tensorflow pip package is built with CUDA 10.1 and cuDNN 7.6.
  • tf.keras
    • Experimental support for mixed precision is available on GPUs and Cloud TPUs. See usage guide.
    • Introduced the TextVectorization layer, which takes as input raw strings and takes care of text standardization, tokenization, n-gram generation, and vocabulary indexing. See this end-to-end text classification example.
    • Keras .compile .fit .evaluate and .predict are allowed to be outside of the DistributionStrategy scope, as long as the model was constructed inside of a scope.
    • Experimental support for Keras .compile, .fit, .evaluate, and .predict is available for Cloud TPUs, Cloud TPU, for all types of Keras models (sequential, functional and subclassing models).
    • Automatic outside compilation is now enabled for Cloud TPUs. This allows tf.summary to be used more conveniently with Cloud TPUs.
    • Dynamic batch sizes with DistributionStrategy and Keras are supported on Cloud TPUs.
    • Support for .fit, .evaluate, .predict on TPU using numpy data, in addition to tf.data.Dataset.
    • Keras reference implementations for many popular models are available in the TensorFlow Model Garden.
  • tf.data
    • Changes rebatching for tf.data datasets + DistributionStrategy for better performance. Note that the dataset also behaves slightly differently, in that the rebatched dataset cardinality will always be a multiple of the number of replicas.
    • tf.data.Dataset now supports automatic data distribution and sharding in distributed environments, including on TPU pods.
    • Distribution policies for tf.data.Dataset can now be tuned with 1. tf.data.experimental.AutoShardPolicy(OFF, AUTO, FILE, DATA) 2. tf.data.experimental.ExternalStatePolicy(WARN, IGNORE, FAIL)
  • tf.debugging
    • Add tf.debugging.enable_check_numerics() and tf.debugging.disable_check_numerics() to help debugging the root causes of issues involving infinities and NaNs.
  • tf.distribute
    • Custom training loop support on TPUs and TPU pods is avaiable through strategy.experimental_distribute_dataset, strategy.experimental_distribute_datasets_from_function, strategy.experimental_run_v2, strategy.reduce.
    • Support for a global distribution strategy through tf.distribute.experimental_set_strategy(), in addition to strategy.scope().
  • TensorRT
    • TensorRT 6.0 is now supported and enabled by default. This adds support for more TensorFlow ops including Conv3D, Conv3DBackpropInputV2, AvgPool3D, MaxPool3D, ResizeBilinear, and ResizeNearestNeighbor. In addition, the TensorFlow-TensorRT python conversion API is exported as tf.experimental.tensorrt.Converter.
  • Environment variable TF_DETERMINISTIC_OPS has been added. When set to "true" or "1", this environment variable makes tf.nn.bias_add operate deterministically (i.e. reproducibly), but currently only when XLA JIT compilation is not enabled. Setting TF_DETERMINISTIC_OPS to "true" or "1" also makes cuDNN convolution and max-pooling operate deterministically. This makes Keras Conv*D and MaxPool*D layers operate deterministically in both the forward and backward directions when running on a CUDA-enabled GPU.

Breaking Changes

  • Deletes Operation.traceback_with_start_lines for which we know of no usages.
  • Removed id from tf.Tensor.__repr__() as id is not useful other than internal debugging.
  • Some tf.assert_* methods now raise assertions at operation creation time if the input tensors' values are known at that time, not during the session.run(). This only changes behavior when the graph execution would have resulted in an error. When this happens, a noop is returned and the input tensors are marked non-feedable. In other words, if they are used as keys in feed_dict argument to session.run(), an error will be raised. Also, because some assert ops don't make it into the graph, the graph structure changes. A different graph can result in different per-op random seeds when they are not given explicitly (most often).
  • The following APIs are not longer experimental: tf.config.list_logical_devices, tf.config.list_physical_devices, tf.config.get_visible_devices, tf.config.set_visible_devices, tf.config.get_logical_device_configuration, tf.config.set_logical_device_configuration.
  • tf.config.experimentalVirtualDeviceConfiguration has been renamed to tf.config.LogicalDeviceConfiguration.
  • tf.config.experimental_list_devices has been removed, please use tf.config.list_logical_devices.

Bug Fixes and Other Changes

</tr></table> ... (truncated) </details> <details> <summary>Commits</summary>

  • 5d80e1e Merge pull request #36215 from tensorflow-jenkins/version-numbers-1.15.2-8214
  • 71e9d8f Update version numbers to 1.15.2
  • e50120e Merge pull request #36214 from tensorflow-jenkins/relnotes-1.15.2-2203
  • 1a7e9fb Releasing 1.15.2 instead of 1.15.1
  • 85f7aab Insert release notes place-fill
  • e75a6d6 Merge pull request #36190 from tensorflow/mm-r1.15-fix-v2-build
  • a6d8973 Use config=v1 as this is r1.15 branch.
  • fdb8589 Merge pull request #35912 from tensorflow-jenkins/relnotes-1.15.1-31298
  • a6051e8 Add CVE number for main patch
  • 360b2e3 Merge pull request #34532 from ROCmSoftwarePlatform/r1.15-rccl-upstream-patch
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deposit: fix locations indexer
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issue commentplazi/arcadia-project

extra formats: adding png or jpg files /mulimedia

IIRC, these fragments can be represented as IIIF URLs, if the original image (in actual image format) is stored on Zenodo. This would remove the need of storing the actual image file, and instead just the "coordinates" for defining the slice/crop that is needed.

As an example, from this record on Zenodo, we can crop the label:

Here's the source URL: https://zenodo.org/api/iiif/v2/fe0c6aae-9dcb-449c-a7f2-043725f88ce0:6c728107-34bd-4b0f-b3fa-2e58e31d7c1e:figure.png/20,450,960,100/pct:100/0/default.png (the 20,450,960,100 are the coordinates for the cropped region of the original image).

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views: update OpenAIRE logo

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issue openedzenodo/zenodo

DCAT record export format failing

See the following examples:

  • https://zenodo.org/record/197721/export/dcat
  • https://zenodo.org/record/263067/export/dcat
  • https://zenodo.org/record/273553/export/dcat

The issue seems to be with the XSLT that transforms the DataCite XML. There's a newer version of the XLST though (TODO: find and add it here).

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issue commentglobalbioticinteractions/globalbioticinteractions

Plazi<>GloBI<>Zenodo add two annotated examples: one simple one complex

Basically the value of the custom= querystring field has the format [<verb>]:[<subject>:<object>] (in more simplistic terms that could be interpreted as [<verb>]:[<left>:<right>]).

  • The <subject> and <object> parts accept Elasticsearch's query string syntax, which allows boolean logic queries (e.g. ("Rhinolophus pussilus" OR "Rhinolophus macrotis")), wildcard queries (e.g. Rhinolophus*), fuzzy queries (e.g. SARS~3), etc.
  • Skipping any of the <subject> or <object> parts is equivalent to having a "wildcard" (*).
  • Indeed the format is a bit non-traditional in terms representation/syntax, but for now that's just an implementation detail and we can improve on it in future iterations.
  • In the search examples, note the position of the colon, separating the subject side from the object side.

In order to ensure the directionality we have to either:

  • Allow only one direction of the relationship to be possible to submit (i.e. obo:RO_0002453 "host of").
  • Allow both encodings to be submitted, but for searching purposes in our backend, index both directions (to allow more flexibility in querying).
    • This introduces some extra implementation effort on our side which we could though schedule for later, and could also be considered as an additional "feature" on top of the base functionality.
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issue commentplazi/arcadia-project

monthly meeting skype

I'm available as well

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events: capture prereleases

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Dotfiles I use...

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semantic-ui: namespace templates
  • Closes #165.
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issue closedinveniosoftware/invenio-app-rdm

semantic-ui: templates should be nested inside "semantic-ui/"

This way, in the generated cookiecutter, one can be explicit and do:

{%- extends 'semantic-ui/invenio_app_rdm/frontpage.html' %}
...

Also without the namespacing, this seems like this module provides by default Bootstrap3 templates (i.e. when no APP_THEME is set at all).

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pull request commentinveniosoftware/invenio-app-rdm

semantic-ui: namespace templates

It might be better to use the config vars, in case the user sets them it will actually make customization useful otherwise they'll need to overwrite the full template.

Agree and applied the suggestions.

There's a bigger topic here to discuss, since now there are three ways to override templates:

  • Use config variables in render_template/extends/include.
    • This requires creating a new template in your instance and then changing also the respective config variable. It has the benefit of being very explicit (i.e. no magic names)
  • Add your instance theme, like APP_THEME = ['zenodo', 'semantic-ui'], and then create the correct template filename
    • This requires a bit of "mental gymnastics" until you understand the templating lookup logic... But it can be very powerful in the sense that you can easily override any template you want with little effort, no matter if there are config variables defined in the original template to assist you.
  • In your instance's templates folder (the one that Flask/Jinja looks inside for the lookup), you can create a template with the same path/filename as the one you want to override
  • This is a bit "magic" overriding, since you rely on the fact that there is a special folder that gets taken into account before everything else
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templates: extend using config variables Co-authored-by: Pablo Panero <ppanero27@gmail.com>

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Custom metadata relationships
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Pull request review commentzenodo/zenodo

Custom metadata relationships

 def custom_metadata_filter(field):     def inner(values):         terms = current_custom_metadata.terms         available_terms = current_custom_metadata.available_vocabulary_set--        must_conditions = []+        conditions = []          for value in values:             # Matches this:-            #   [vocabulary:term]:value-            parsed = re.match(r'\[([-\w]+\:[-\w]+)\]\:(.+)', value)+            #   [vocabulary:term]:[value]+            parsed = re.match(+                r'^\[(?P<key>[-\w]+\:[-\w]+)\]\:\[(?P<val>.+)\]$', value)             if not parsed:                 raise RESTValidationError(                     errors=[FieldError(                         field, 'The parameter should have the format: '-                               'custom=[field_name]:field_value.')])+                               'custom=[term]:[value].')]) -            search_key, search_value = parsed.groups()+            parsed = parsed.groupdict()+            search_key = parsed['key']+            search_value = parsed['val']              if search_key not in available_terms:                 raise RESTValidationError(                     errors=[FieldError(-                        field, 'The "{}" term is not supported.'+                        field, u'The "{}" term is not supported.'                         .format(search_key))]) -            # TODO: check if the search value has the correct type-            # for now we have only 'keyword' and 'text'--            # TODO: move this to a central place-            # get the elasticsearch custom field name             custom_fields_mapping = dict(                 keyword='custom_keywords',-                text='custom_text'+                text='custom_text',+                relationship='custom_relationships',             ) -            custom_type = terms[search_key]['term_type']-            es_field = custom_fields_mapping[custom_type]+            term_type = terms[search_key]['type']+            es_field = custom_fields_mapping[term_type]++            nested_clauses = [+                {'term': {'{}.key'.format(es_field): search_key}},+            ] -            must_conditions.append({+            if term_type in ('text', 'keyword'):+                nested_clauses.append({+                    'query_string': {+                        'fields': ['{}.value'.format(es_field)],+                        'query': search_value,+                    }+                })+            elif term_type == 'relationship':+                if ':' not in search_value:+                    raise RESTValidationError(+                        errors=[+                            FieldError(field, (+                                'Relatinship terms serach values should '+                                'follow the format "<sub>:<obj>".'))+                        ])++                sub, obj = search_value.split(':', 1)

We do above, by checking if ':' in search_value

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