Alignment of the abbreviated column names or meta information of the scenario datasets with the OEO terms.

[adelmemariani]

As an example, this scenario dataset has meta information and in the 'resources' -> 'fileds' there are some descriptions about the columns. However, typically, these descriptions are not sufficient for inferring an OEO concept for the column.

As of 'May 2nd, 2022', there are 238 unique column names in total (all uploaded scenario datasets). Some of them can be mapped easily to the OEO concepts:

#	column name	OEO concept	OEO class ID
1	id	unique individual identifier	OEO_00010037
2	scenario	scenario	OEO_00000364
3	constr	constraint	OEO_00000104
4	val
5	region	spatial region	BFO_0000006
6	sector	sector	OEO_00000367
7	type
8	demand	demand	OEO_00140040
9	energy	energy	OEO_00000150
10	reg
11	nutsid
12	geom
13	technology	technology	OEO_00000407
14	co2_var
15	co2_fix
16	eta_elec
17	eta_th
18	eta_el_chp
19	eta_th_chp
20	eta_chp_flex_el
21	sigma_chp
22	beta_chp
23	opex_var
24	opex_fix
25	capex
26	c_rate_in
27	c_rate_out
28	eta_in
29	eta_out
30	cap_loss
31	lifetime
32	wacc
33	ressource
34	transformer	transformer	OEO_00000420
35	power	power	OEO_00000333
36	from_region
37	to_region
38	capacity
39	parameter
40	value
41	unit	unit of measurement	UO_0000000
42	country_code
43	category
44	year	year	UO_0000036
45	gas
46	notation
47	ry
48	base_year
49	default_unit
50	additional_unit
51	source_original
52	comment
53	value_reported
54	value_ry_calibration
55	value_gapfilled
56	submission_year_original
57	subtable
58	data_source_years
59	data_source
60	is_part_of_projections
61	is_ry
62	category_code
63	category_lulucf
64	category_parent
65	level
66	is_user_defined
67	in_cir_2020_1208_annex_xxv_art_38_tab_1a
68	in_cir_749_2014_annex_xii_art_23_tab_1
69	crf_code
70	oeo_id
71	country_name
72	notation_name
73	detail
74	scenario_name
75	energy_carrier	energy carrier	OEO_00020039
76	avg_value
77	hourly_value
78	Szenario	scenario	OEO_00000364
79	Szenariojahr	scenario year	OEO_00020097
80	Region	spatial region	BFO_0000006
81	Kennwert
82	Unterkennwert
83	Sektor	sector	OEO_00000367
84	Energieträger
85	Technologie	technology	OEO_00000407
86	Einheit
87	Wert
88	Kommentar
89	Zeit
90	Bivalente_Luftwärmepumpe
91	Hybride_Luftwärmepumpe
92	Sondenwärmepumpe
93	Ländername
94	Erzeugung_Braunkohle_Kraft_Waerme_Kopplung
95	Erzeugung_Steinkohle_Kraft_Waerme_Kopplung
96	Erzeugung_Erdgas_Kraft_Waerme_Kopplung
97	Erzeugung_Öl_Kraft_Waerme_Kopplung
98	Erzeugung_Braunkohle_Kondensationskraftwerke
99	Erzeugung_Steinkohle_Kondensationskraftwerke
100	Erzeugung_Erdgas_Kondensationskraftwerke
101	Erzeugung_Öl_Kondensationskraftwerke
102	Erzeugung_Uran_Kondensationskraftwerke
103	Erzeugung_Wind_Onshore
104	Erzeugung_Wind_Offshore
105	Erzeugung_Photovoltaik
106	Erzeugung_Laufwasser
107	Erzeugung_Pumpspeicher
108	Erzeugung_Batterien
109	Verbrauch_Herkoemmlich_inkl_Klimatisierung
110	Verbrauch_Pumpspeicher
111	Verbrauch_Batterien
112	Verbrauch_Wärmepumpen
113	Verbrauch_Heizstäbe
114	Verbrauch_Power_to_Gas
115	Verbrauch_Batterie_elektrische_Fahrzeuge
116	Verbrauch_Plug_in_Hybride
117	Verbrauch_Range_Extender
118	Import
119	Export
120	Abregelung_Wind_Onshore
121	Abregelung_Wind_Offshore
122	Abregelung_Photovoltaik
123	variable	variable	OEO_00000435
124	source_category
125	crf
126	application
127	target_fulfilled
128	fuel	fuel	OEO_00000173
129	subsector
130	emission_source
131	greenhouse_gas	greenhouse gas	OEO_00000020
132	technologie
133	jahr	year	UO_0000036
134	at
135	be
136	ch
137	cz
138	dk
139	fr
140	gb
141	lu
142	nl
143	no
144	pl
145	se
146	bundesland
147	szenario	scenario	OEO_00000364
148	braunkohle
149	erdgas
150	kuppelgas
151	oel
152	abfall
153	sonstige_konventionelle
154	kwk_kleiner_10mw
155	pumpspeicher
156	lauf_und_wasserspeicher
157	wind_onshore	onshore wind farm	OEO_00000311
158	wind_offshore	offshore wind farm	OEO_00000308
159	photovoltaik
160	biomasse	biomass	OEO_00010214
161	sonstige_ee
162	band_des_stromverbrauchs_von
163	band_des_stromverbrauchs_bis
164	dsm
165	power_to_heat
166	power_to_gas
167	kategorie
168	energietraeger
169	referenz_2019
170	a_2035
171	b_2035
172	c_2035
173	b_2040
174	stunde
175	mittelwert
176	minimum
177	maximum
178	zeit
179	ungesteuerter_lastgang
180	optimierter_lastgang
181	konventioneller_stromverbrauch
182	elektromoblitaet
183	power_to_heat_haushalte
184	grossverbraucher
185	power_to_heat_industrie
186	sektor	sector	OEO_00000367
187	referenz_2018
188	name
189	carrier
190	tech
191	from_bus
192	to_bus
193	capacity_cost
194	efficiency
195	carrier_cost
196	marginal_cost
197	expandable
198	output_parameters
199	balanced
200	bus
201	amount
202	profile
203	timeindex
204	be_electricity_demand_profile
205	bb_electricity_demand_profile
206	storage_capacity
207	loss_rate
208	storage_capacity_cost
209	input_parameters
210	loss
211	be_solar_pv_profile
212	bb_solar_pv_profile
213	be_wind_onshore_profile
214	bb_wind_onshore_profile
215	dfid
216	nid
217	pathway
218	framework
219	version
220	region_2
221	indicator
222	aggregation
223	tags
224	updated
225	schema
226	field
227	source
228	month	month	UO_0000035
229	year_month
230	rid
231	set
232	internal_id
233	member_state
234	submission_year
235	crf_sector
236	additional_unit_information
237	notation_key
238	is_baseyear

[adelmemariani]

In principle, if the institutions provide enough textual descriptions for the column names of their datasets (and also the distinct values in the categorical columns), then it is possible to automatically recommend to them some candidate OEO concepts: A recommender engine (inside the OEP) for the alignments between the datasets' columns and the OEO terms. I already made a prototype, however, the performance of the final product depends on the quality of the descriptions.