AndreaBozzo/ceres-open-data-index huggingface dataset:
Localisation des stations du SNO BenthOBS - Réseau d'observation de la macrofaune benthique

[Lien vers la fiche source] BenthOBS a pour objectif de constituer un réseau national d'observation du macrozoobenthos. Dans un contexte de changement global, il est essentiel de disposer de séries temporelles capables de mettre en évidence et de comprendre les changements en cours en terme de diversité spécifique au sein des communautés et leurs conséquences sur le fonctionnement des écosystèmes marins.
BPCO : Hospitalisations (France)

Les séjours hospitaliers pour exacerbation de bronchopneumopathie chronique obstructive (BPCO) sont identifiés à partir des informations du PMSI (programme de médicalisation des systèmes d’information des hôpitaux, auquel Santé publique France accède dans le SNDS). Les taux sont rapportés à la population française, estimée par l’Insee et fournie annuellement en janvier via les estimations localisées de population (ELP). Les taux sont standardisés sur l’âge de la population européenne de 2010 (Revision of the European standard population, Eurostat 2013). Les indicateurs sont calculés annuellement et disponibles pour chaque année de 2000 à 2020. Précisions : L’algorithme de sélection des séjours hospitaliers retient les séjours codés comme suit (CIM10 – classification internationale des maladies, 10e révision) : * J44.0-J44.1 en diagnostic principal * J96.0 en diagnostic principal et J43-J44 en diagnostic associé * J09-J18 J20-J22 en diagnostic principal et J43-J44 en diagnostic associé * J43-J44 en diagnostic principal et J09-J18 J20-J22 J96.0 en diagnostic associé * ET, dans tous les cas, exclusion des séjours de moins de 2 jours dont l'issue n'est pas le décès. Pour aller plus loin : Consulter le dossier BPCO et insuffisance respiratoire chronique du site de Santé publique France : [https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/bpco-et-insuffisance-respiratoire-chronique]
Capacity Management System Data

The "Capacity Management System" data table captures daily aggregated generation losses caused by curtailment under two network management schemes: Active Network Management (ANM) and Load Management Scheme (LMS). These tables have been published to give the users information which will allow them to better understand the curtailment throughout the year in each area. The file that has been published is Capacity Management Systems Data. The file gives the following information: 1. An anonymised unique identifier for generator under a capacity management system 2. Type of capacity management system under operation (ANM or LMS) 3. The month of curtailment 4. The volume of generation curtailed in the month (MWh) For additional information on column definitions, please click on the Dataset schema link below. ### Disclaimer Whilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this). ### Data Triage As part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process. Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click [here] to access the Data Triage documentation for the Capacity Management System dataset. To access our full suite of Data Triage documentation, visit the [SP Energy Networks Data & Information]. [Download dataset metadata (JSON)]
MassiveFold data for target H1220 - T262

[Lien vers la fiche source] MassiveFold data generated for [CASP16], in collaboration with [ CAPRI ]. 8040 predictions were generated for the target = 1005 x 8 sets of parameters. - a description of the setup can be found in `MassiveFold_CASP16_Abstract.pdf` - `README.txt` describes the contents of the main `MassiveFold.tar.gz` file - the main `MassiveFold.tar.gz file contains all the predictions, divided into 8 folders named after the conditions. It contains predictions as well as pickle files, sequence alignments, rankings and plots. The README.txt file describes the contents of this tar.gz. - the `only_pdbs_MassiveFold.tar.gz` is the result of the `gather_runs.py` script, without the pickle files. It contains a list of all the pdb files and ranking files with scores. - `gather_runs.py` allows to gather the runs, to use preferentially to the one included in the main `MassiveFold.tar.gz` at the time of the prediction phase, because it has been updated - `combined_scores.csv` file contains the CASP assessment for the target (from [https://predictioncenter.org/]) - `CAPRI_assessment.csv` file contains the CAPRI assessment for the target (from [https://www.capri-docking.org/assessment/]) - `CAPRI_scoring.pdb.gz` is a multi-pdbs containing all the MassiveFold predictions for the target. It was used for the CAPRI scoring phase.
MassiveFold data for target H1220 - T262

[Lien vers la fiche source] MassiveFold data generated for [CASP16], in collaboration with [ CAPRI ]. 8040 predictions were generated for the target = 1005 x 8 sets of parameters. - a description of the setup can be found in `MassiveFold_CASP16_Abstract.pdf` - `README.txt` describes the contents of the main `MassiveFold.tar.gz` file - the main `MassiveFold.tar.gz file contains all the predictions, divided into 8 folders named after the conditions. It contains predictions as well as pickle files, sequence alignments, rankings and plots. The README.txt file describes the contents of this tar.gz. - the `only_pdbs_MassiveFold.tar.gz` is the result of the `gather_runs.py` script, without the pickle files. It contains a list of all the pdb files and ranking files with scores. - `gather_runs.py` allows to gather the runs, to use preferentially to the one included in the main `MassiveFold.tar.gz` at the time of the prediction phase, because it has been updated - `combined_scores.csv` file contains the CASP assessment for the target (from [https://predictioncenter.org/]) - `CAPRI_assessment.csv` file contains the CAPRI assessment of MassiveFold predictions for the target. The `chains` column that gives the chains involved in the interface was filled a posteriori from CAPRI generated files and is provided without any warranty. - `CAPRI_scoring.pdb.gz` is a multi-pdbs containing all the MassiveFold predictions for the target. It was used for the CAPRI scoring phase.
MassiveFold data for target T1270 - T302

[Lien vers la fiche source] MassiveFold data generated for [CASP16], in collaboration with [ CAPRI ]. 8040 predictions were generated for the target = 1005 x 8 sets of parameters. - a description of the setup can be found in `MassiveFold_CASP16_Abstract.pdf` - `README.txt` describes the contents of the main `MassiveFold.tar.gz` file - the main `MassiveFold.tar.gz file contains all the predictions, divided into 8 folders named after the conditions. It contains predictions as well as pickle files, sequence alignments, rankings and plots. The README.txt file describes the contents of this tar.gz. - the `only_pdbs_MassiveFold.tar.gz` is the result of the `gather_runs.py` script, without the pickle files. It contains a list of all the pdb files and ranking files with scores. - `gather_runs.py` allows to gather the runs, to use preferentially to the one included in the main `MassiveFold.tar.gz` at the time of the prediction phase, because it has been updated - `combined_scores.csv` file contains the CASP assessment for the target (from [https://predictioncenter.org/]) - `CAPRI_assessment.csv` file contains the CAPRI assessment for the target (from [https://www.capri-docking.org/assessment/]) - `CAPRI_scoring.pdb.gz` is a multi-pdbs containing all the MassiveFold predictions for the target. It was used for the CAPRI scoring phase.
Localisation des stations du SNO COAST-HF - Système d'observation des océans côtiers - Haute fréquence

[Lien vers la fiche source] COAST-HF est un Service National d'Observation qui vise à fédérer et cordonner à l'échelle du littoral français un ensemble de plateformes fixes instrumentées de mesures in situ hautes fréquences pour des paramètres clés des eaux côtières. Ce réseau s'inscrit dans l'Infrastructure de Recherche ILICO. Il est constitué de 14 bouées ancrées instrumentées et autonomes permettant la mesure en continu de paramètres physiques et bio-géochimiques.
MassiveFold data for target T1270 - T302

[Lien vers la fiche source] MassiveFold data generated for [CASP16], in collaboration with [ CAPRI ]. 8040 predictions were generated for the target = 1005 x 8 sets of parameters. - a description of the setup can be found in `MassiveFold_CASP16_Abstract.pdf` - `README.txt` describes the contents of the main `MassiveFold.tar.gz` file - the main `MassiveFold.tar.gz file contains all the predictions, divided into 8 folders named after the conditions. It contains predictions as well as pickle files, sequence alignments, rankings and plots. The README.txt file describes the contents of this tar.gz. - the `only_pdbs_MassiveFold.tar.gz` is the result of the `gather_runs.py` script, without the pickle files. It contains a list of all the pdb files and ranking files with scores. - `gather_runs.py` allows to gather the runs, to use preferentially to the one included in the main `MassiveFold.tar.gz` at the time of the prediction phase, because it has been updated - `combined_scores.csv` file contains the CASP assessment for the target (from [https://predictioncenter.org/]) - `CAPRI_assessment.csv` file contains the CAPRI assessment of MassiveFold predictions for the target. The `chains` column that gives the chains involved in the interface was filled a posteriori from CAPRI generated files and is provided without any warranty. - `CAPRI_scoring.pdb.gz` is a multi-pdbs containing all the MassiveFold predictions for the target. It was used for the CAPRI scoring phase.
MassiveFold data for targets T1249v1 - T250 and T1249v2 - T252

[Lien vers la fiche source] MassiveFold data generated for [CASP16], in collaboration with [ CAPRI ]. 8040 predictions were generated for the target = 1005 x 8 sets of parameters. - a description of the setup can be found in `MassiveFold_CASP16_Abstract.pdf` - `README.txt` describes the contents of the main `MassiveFold.tar.gz` file - the main `MassiveFold.tar.gz file contains all the predictions, divided into 8 folders named after the conditions. It contains predictions as well as pickle files, sequence alignments, rankings and plots. The README.txt file describes the contents of this tar.gz. - the `only_pdbs_MassiveFold.tar.gz` is the result of the `gather_runs.py` script, without the pickle files. It contains a list of all the pdb files and ranking files with scores. - `gather_runs.py` allows to gather the runs, to use preferentially to the one included in the main `MassiveFold.tar.gz` at the time of the prediction phase, because it has been updated - `combined_scores.csv` file contains the CASP assessment for the target (from [https://predictioncenter.org/]) - `CAPRI_assessment.csv` file contains the CAPRI assessment for the target (from [https://www.capri-docking.org/assessment/]) - `CAPRI_scoring.pdb.gz` is a multi-pdbs containing all the MassiveFold predictions for the target. It was used for the CAPRI scoring phase.
Postes de travail salarié selon le lieu de travail de WalStat

L'indicateur donne le nombre de postes de travail salarié assujettis à la sécurité sociale selon le lieu de travail (au lieu de l'unité d'établissement), au 30 juin et au 31 décembre. Ruptures de série : - au 1er janvier 2003 : récolte des données à partir de la déclaration multifonctionnelle (DmfA, relevé électronique du personnel) et non plus à partir d'une annexe statistique dans la déclaration papier du 2è trimestre. - au 1er janvier 2011 : sortie des statistiques de salariés inactifs suite à la modification de la déclaration du personnel du secteur public (réforme Capelo). - au 1er janvier 2012 : sortie des statistiques de salariés inactifs suite à la modification de la déclaration du personnel du secteur public (réforme Capelo) : suite et fin. - au 1er janvier 2014 : ajustement de la répartition par commune de travail suite à l'identification exhaustive des unités d'établissement Voir aussi : - D'autres indicateurs relatifs à l'emploi salarié selon le lieu de travail sont disponibles dans le thème 'Activité économique' (secteurs d'activité, taille des établissements). - Le site de l'ONSS, "[\2](\1)". [http://walstat.iweps.be/fichiers/metadonnees/meta-214500.pdf]
ETP chez des opérateurs du secteur non-marchand reconnus/subventionnés/agréés par la FWB de WalStat

L’indicateur est le nombre d’Equivalents Temps Plein (ETP) théoriques (calculés à partir des régimes de travail des contrats sans tenir compte des jours d'inoccupation/absence) que représentent les travailleurs effectuant des prestations chez des opérateurs du secteur non-marchand reconnus/subventionnés/agréés par la FWB sur base du cadastre de l’emploi non-marchand. Le fichier de données détaille aussi le nombre d’ETP par secteur d’activités (c.-à-d. les compétences de la FWB reconnues chez l’opérateur pour lequel les travailleurs effectuent leurs prestations). Tous les indicateurs sont produits par localité d'exécution des prestations (c.-à-d. la localité INS dans laquelle l’opérateur effectue principalement les activités qui lui sont reconnues/agréées/ subventionnées par la FWB) et par secteurs d’activités. Voir aussi : - le site du "[\2](\1)". [http://walstat.iweps.be/fichiers/metadonnees/meta-821400.pdf]
Produit intérieur brut (PIB) de WalStat

Cet indicateur clé est exprimé en indices permettant de comparer le produit intérieur brut (PIB) par habitant des provinces et des arrondissements wallons à la valeur régionale (l’indice est égal à 100 pour la Wallonie). Une valeur inférieure à 100 indique une situation locale plus défavorable tandis qu’une valeur supérieure à 100 indique une situation locale plus favorable. Le fichier de données reprend également l’indice calculé à partir de la Belgique, la valeur du PIB par habitant à prix courants exprimée en euros, la valeur du PIB à prix courants exprimée en millions d’euros. [http://walstat.iweps.be/fichiers/metadonnees/meta-209300.pdf]
Candidatures à un logement social de WalStat

L’indicateur donne le nombre de candidatures à un logement social ‘actives’ par entité de premier choix. Quelques précisions/précautions doivent être notées : Depuis le 01.01.2013, les candidats sont amenés à sélectionner un maximum de 5 communes classées par ordre de préférence. Les données présentées ici font référence à la seule commune de premier choix. Les informations proviennent de la base de données centralisée des candidatures pour un logement social auprès des SLSP. [http://walstat.iweps.be/fichiers/metadonnees/meta-244202.pdf]
1eb82d09-77ed-4f63-9f03-2c3516a9713d

[Lien vers la fiche source] This dataset provides a times series of daily Sargassum detection on a 0.0032 x 0.0032 degree grid over the Tropical Atlantic Ocean, the Caribbean Sea and the Gulf of Mexico, produced from acquisitions made by the OLCI sensor on board the Sentinel-3A and 3B satellites. Detection is carried out in several steps, including atmospheric correction, masks computation, Alternative Floating Algae Index (AFAI) calculation and two successive deviations implementation. This process is applied to each Sentinel-3 granules of the area of interest. A composite product is finally created by combining detections of all the granules of a same day. This dataset is generated daily within a 24-hour delay.
Maladies du motoneurone dont sclérose latérale amyotrophique (Région)

Les indicateurs de la surveillance des maladies du motoneurone sont construits à partir des informations de remboursements de soins auxquelles Santé publique France accède dans le SNDS. Les maladies du motoneurones (CIM-10 : G12.2) correspondent dans 90% des situations à des Scléroses Latérales Amyotrophiques (SLA, également appelées Maladie de Charcot). Cependant, à partir des données utilisées par Santé publique France, il n'est pas possible de distinguer la SLA des autres maladies du motoneurone (paralysie bulbaire progressive, amyotrophie spinale progressive, dégénérescence familiale du motoneurone). Précisions : * Les cas incidents de maladies du motoneurone ont été définis comme le nombre de personnes nouvellement pris en charge sur deux années, avec au moins deux contacts de soin spécifique des maladies du motoneurone (remboursement de médicaments riluzole, hospitalisation, affection longue durée, décès) dans les 6 mois. Les données d'incidence ont été cumulées sur deux années pour améliorer la stabilité des résultats. * Les taux ont été standardisés sur l'âge, le sexe et l'année par la méthode indirecte en utilisant les taux d'incidence au niveau national comme référence pour calculer le nombre de cas attendus. Le Ratio standardisé d’incidence sur l’âge, le sexe et l'année est exprimé en écart relatif par rapport aux données nationales avec la formule suivante : ((cas incidents observés / cas incidents attendus) - 1) * 100 * Limites : Les données avant et après 2013 ne peuvent être directement comparées en raison de l'augmentation du périmètre des données à partir de 2013. Pour aller plus loin : Consulter le dossier Maladie du Motoneurone sur le site de Santé publique France : [https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-neurodegeneratives/maladie-du-motoneurone] Consulter l’article présentant plus en détail l’algorithme permettant d’identifier les cas incidents de maladies du motoneurone à partir du Système National des Données de Santé : Kab S, Moisan F, Preux PM, Marin B, Elbaz A. Nationwide incidence of motor neuron disease using the French health insurance information system database. Amyotroph Lateral Scler Frontotemporal Degener. 2017 Aug;18(5-6):426-433. doi: 10.1080/21678421.2017.1306566. Epub 2017 Apr 7. PMID: 28387141.
Curtailment (SPEN_009) Data Quality Checks

This data table provides the detailed data quality assessment scores for the Curtailment dataset. The quality assessment was carried out on the 20 February 2026. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit [Data Quality - SP Energy Networks]. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at [opendata@spenergynetworks.co.uk]. The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available. ### Disclaimer The data quality assessment may not represent the quality of the current dataset that is published on the Open Data Portal. Please check the date of the latest quality assessment and compare to the 'Modified' date of the corresponding dataset. The data quality assessments will be updated on either a quarterly or annual basis, dependent on the update frequency of the dataset. This information can be found in the dataset metadata, within the Information tab. If you require a more up to date quality assessment, please contact the Open Data Team at [opendata@spenergynetworks.co.uk] and a member of the team will be in contact.
Embedded Generation by Type (SPEN_010) Data Quality Checks

This data table provides the detailed data quality assessment scores for the Embedded Generation by Type dataset. The quality assessment was carried out on the 19th February 2026. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit [Data Quality - SP Energy Networks]. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at [opendata@spenergynetworks.co.uk]. The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available. ### Disclaimer The data quality assessment may not represent the quality of the current dataset that is published on the Open Data Portal. Please check the date of the latest quality assessment and compare to the 'Modified' date of the corresponding dataset. The data quality assessments will be updated on either a quarterly or annual basis, dependent on the update frequency of the dataset. This information can be found in the dataset metadata, within the Information tab. If you require a more up to date quality assessment, please contact the Open Data Team at [opendata@spenergynetworks.co.uk] and a member of the team will be in contact.